An electric vehicle charging station site selection and capacity determination method, device, equipment and medium

Abstract

The application relates to the technical field of charging station site selection, and discloses an electric vehicle charging station site selection and capacity determination method, device, equipment and medium, wherein the method comprises the following steps: generating geographical data maps by first acquiring GPS data of a target area, and performing multiple grid divisions, combining a charging station grid rationality index, and finely dividing actual grid sets suitable for the target area. For each actual grid, the charging demand and service capacity thereof are analyzed, the number range of charging stations is determined, and a candidate station combination set is generated according to the range. Subsequently, a charging station site selection and capacity determination model is used to optimize the charging station position and capacity configuration, ensure that the charging stations provide efficient services at reasonable positions, balance the power grid load and reduce the charging waiting time, and finally improve the utilization efficiency and overall service level of the charging stations. The technical scheme provided by the application can optimize the site selection of the charging stations, reasonably configure the layout, reduce the charging time of users and improve the utilization rate of the charging stations.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of charging station site selection, and particularly relates to an electric vehicle charging station site selection and capacity determination method, device, equipment and medium. BACKGROUND

[0002] With the rapid development of the electric vehicle market, the construction of charging facilities has become a key problem restricting the development of the industry. Although the number of charging equipment increases, the layout, configuration and utilization efficiency of charging stations still face challenges. Traditional researches pay more attention to construction cost, coverage range and vehicle flow, and ignore the details of user experience and charging demand. Factors such as user charging behavior, charging time, distance and charging pile type directly affect the utilization rate and service satisfaction of charging stations.

[0003] Therefore, how to optimize the site selection of charging stations, reasonably configure the layout, reduce the charging time of users and improve the utilization rate of charging stations is a technical problem to be solved at present. SUMMARY

[0004] The application provides an electric vehicle charging station site selection and capacity determination method, device, equipment and medium, which achieves the technical effects of optimizing the site selection of charging stations, reasonably configuring the layout, reducing the charging time of users and improving the utilization rate of charging stations.

[0005] In order to achieve the above purpose, the main technical scheme adopted by the application comprises:

[0006] In a first aspect, the application provides an electric vehicle charging station site selection and capacity determination method, which comprises:

[0007] obtaining GPS data of a target area, and generating a geographic data map of the target area according to the GPS data;

[0008] According to the geographic data map, the charging stations in the target area are divided into multiple grids, for each charging station grid set obtained by each division, the rationality index of the charging station grid set is counted, and according to the counted rationality indexes, the actual grid set suitable for the target area is determined. The rationality index is determined based on the charging compensation relationship between each charging station grid in the charging station grid set;

[0009] Each actual grid in the actual grid set is traversed, and for any actual grid therein, the total charging demand of the actual grid is identified, and according to the maximum service capacity and the minimum service capacity of the charging station in the actual grid and the total charging demand, the number range of the charging station is determined;

[0010] determine a candidate site combination set corresponding to the actual grid based on combinations of different charging station quantities in the charging station quantity range; wherein each candidate site combination represents a quantity of candidate sites;

[0011] For any candidate site combination in the candidate site combination set, determine a target charging station site selection and capacity configuration scheme based on a pre-set charging station site selection and capacity configuration model; wherein the charging station site selection and capacity configuration model is constructed according to the type of charging pile in each charging station, the number of different types of charging piles, and the coverage rate of the charging station.

[0012] The electric vehicle charging station site selection and capacity configuration method provided in this embodiment generates geographic data maps based on GPS data of a target area and performs multiple grid divisions to calculate the rationality index of each grid and evaluate the compensation relationship and load adjustment capability between charging station grids. Then, based on the rationality index, the most suitable grid set is selected to ensure that the charging demand in the region is effectively covered. By analyzing the total charging demand of each grid and the service capability of the charging station, a reasonable charging station quantity range is determined, and a candidate site combination set is generated. Finally, the charging station site selection and capacity configuration model is used to optimize the site selection and capacity configuration of the charging station to ensure that the charging station provides efficient service at the optimal location, reduces resource waste and overload, and improves the overall efficiency of the charging service and user experience.

[0013] In one embodiment, the statistics of the rationality index of the charging station grid set includes:

[0014] Obtain a plurality of preset statistical periods;

[0015] For any statistical period and for any charging station grid in the charging station grid set, determine the first quantity of charging stations with charging compensation relationship in the charging station grid in the statistical period, and generate the charging compensation coefficient of the charging station grid based on the first quantity obtained in each statistical period.

[0016] Obtain the mean value of the charging compensation coefficients of each charging station grid in the charging station grid set, and take the mean value as the rationality index of the charging station grid set.

[0017] The embodiment obtains a plurality of preset statistical time periods, and in each time period, for each charging station grid in a charging station grid set, determines the number of charging stations in the grid that have a charging compensation relationship. According to the number of charging stations counted in each time period, a charging compensation coefficient of the charging station grid is generated. Then, the average of the charging compensation coefficients of all charging station grids is calculated as a rationality index to evaluate the overall layout and operation efficiency of the charging station grid. In this way, the area with smaller load fluctuation and stronger charging compensation ability can be effectively identified, so as to optimize the site selection of the charging station, reasonably layout the charging station, improve the utilization rate of the charging station, reduce the charging waiting time of the user, and improve the overall charging efficiency.

[0018] In one embodiment, the determination of the charging station number range according to the maximum service capacity and the minimum service capacity of the charging station in the actual grid and the total charging demand comprises:

[0019] determining a minimum charging station number according to the maximum service capacity of the charging station in the actual grid and the total charging demand;

[0020] determining a maximum charging station number according to the minimum service capacity of the charging station in the actual grid and the total charging demand;

[0021] determining the range from the minimum charging station number to the maximum charging station number as the charging station number range.

[0022] The embodiment determines the minimum charging station number to ensure that each charging demand point is covered and to avoid resource waste. Then, the maximum charging station number is determined to prevent over-construction and to ensure that the service capacity matches the demand. Then, by selecting different charging station numbers through clustering analysis, the site selection of the charging station can be effectively optimized, the resource utilization rate can be improved, the charging time of the user can be reduced, the actual grid can be avoided from being repeatedly covered and having a blank area, and the use efficiency of the charging station can be improved.

[0023] In one embodiment, the determination of the candidate site combination set corresponding to the actual grid based on the combinations of different charging station numbers in the charging station number range comprises:

[0024] for each charging station number in the charging station number range, determining the distance from each charging demand point in the actual grid to the cluster center, and determining the sum of squared errors according to the distance;

[0025] drawing a relationship diagram of the sum of squared errors corresponding to different charging station numbers, and determining the candidate site combination set from the relationship diagram.

[0026] The embodiment calculates the distance from each charging demand point to the cluster center according to different numbers of charging stations, and evaluates the matching degree of the charging demand point and the charging station through the sum of squared errors. The smaller the sum of squared errors is, the shorter the distance between the charging demand point and the charging station is, and the higher the matching degree is, so that the charging station site selection is optimized, and the coverage and utilization of the charging station are improved. Then, by drawing a relationship diagram of the sum of squared errors and the number of charging stations, a proper number of charging stations is selected to avoid resource waste or insufficient coverage. After reasonable configuration and layout, the distance of users to the charging station is shortened, the charging waiting time is reduced, and the charging efficiency and user experience are improved as a whole. At the same time, regional resources can be scientifically configured to ensure the balanced distribution of charging stations in each region, and efficient use of resources is realized.

[0027] In one embodiment, the candidate station combination set corresponding to the actual grid is determined based on combinations of different numbers of charging stations in the charging station number range, comprising:

[0028] For each number of charging stations in the charging station number range, the first average distance from each charging demand point in the actual grid to the adjacent charging demand point in the same cluster is determined;

[0029] The second average distance from each charging demand point in the actual grid to the charging demand point in the nearest cluster is determined;

[0030] According to the ratio between the first average distance and the second average distance, a single contour coefficient of each charging demand point is determined, and an overall contour coefficient matched with each number of charging stations is determined according to the single contour coefficient;

[0031] The overall contour coefficient is screened to obtain the candidate station combination set.

[0032] The embodiment can evaluate the compactness within the cluster by calculating the first average distance from each charging demand point to the adjacent charging demand point in the same cluster, ensure that the charging station effectively covers the actual grid, and reduce the waiting time of users. Secondly, calculating the second average distance from each charging demand point to the charging demand point in the nearest cluster helps to evaluate the separation between clusters, avoid repeated coverage, and improve the utilization of the station. Further, combined with the calculation of the contour coefficient, the clustering quality can be comprehensively evaluated to ensure the rationality of the number and layout of charging stations, and avoid excessive or insufficient construction. Finally, by screening the overall contour coefficient, the optimal number and layout scheme of charging stations are determined, so that the configuration of charging stations is optimized, the utilization of charging stations is improved, and the charging time of users is reduced.

[0033] In one embodiment, the target function in the charging station site selection and capacity determination model is constructed as follows:

[0034] determining a target construction total cost corresponding to the any candidate station combination;

[0035] determining a coverage range matched with the candidate station position, and determining a target coverage rate based on the coverage range;

[0036] transforming the target coverage rate into a target coverage loss cost by using a preset penalty cost coefficient;

[0037] determining a sum of the target construction total cost and the target coverage loss cost as the target function.

[0038] The embodiment ensures the economic feasibility of the charging station site selection and capacity determination scheme by calculating the target construction total cost corresponding to the candidate station combination, ensures that the charging station can meet the user demand by determining the coverage range and the target coverage rate of the candidate station position, thereby shortening the charging time and waiting time of the user, then quantifies the insufficient coverage into economic loss by using the penalty cost coefficient, promotes the optimization algorithm to improve the coverage rate, finally, constructs the target function by comprehensively considering the target construction total cost and the target coverage loss cost, and finds the best charging station site selection and capacity determination scheme, which meets the user demand and controls the cost.

[0039] In one embodiment, the determining the target construction total cost corresponding to the any candidate station combination comprises:

[0040] determining a construction cost, an equipment cost, an equipment maintenance cost and a power consumption cost matched with each candidate station position for the any candidate station combination and the corresponding candidate station position;

[0041] weighting and summing the construction cost, the equipment cost, the equipment maintenance cost and the power consumption cost according to a decision variable of whether to build the charging station at the candidate station position, to obtain a single construction total cost matched with each candidate station position;

[0042] determining the target construction total cost corresponding to the any candidate station combination according to all the single construction total costs.

[0043] The embodiment determines the construction cost, equipment cost, equipment maintenance cost and power consumption cost of each candidate site location according to the candidate site location. Then, whether to build a charging station at the candidate site location is determined by a decision variable, and a single total construction cost of each candidate site location is obtained by combining the weighted sum of various costs. Finally, the final target total construction cost is determined according to all single total construction costs. The optimization target is not only to select the site with the lowest cost, but also to consider the utilization rate of the site and the user demand, reasonably configure the site layout, and ensure that the charging station meets the user demand while having high utilization rate and low operation cost.

[0044] In one embodiment, the determining of the target coverage rate corresponding to any candidate site combination comprises:

[0045] determining the coverage range matched with the candidate site location, and screening target charging demand points in the coverage range;

[0046] determining the ratio between the target charging demand points and the total charging demand as the target coverage rate corresponding to any candidate site combination.

[0047] The embodiment determines the coverage range of each candidate site location, and screens the target charging demand points in the coverage range. Then, the target coverage rate is obtained by calculating the ratio between the target charging demand points covered by each candidate site location and the total demand points. The target coverage rate reflects the effectiveness of site selection, helping to evaluate whether the site can effectively meet the user demand. A higher target coverage rate indicates that the site selection can better serve the users, thereby improving the utilization rate of the charging station and reducing the charging waiting time of the users.

[0048] In a second aspect, the embodiment of the present application provides a device for site selection and capacity determination of an electric vehicle charging station, which comprises:

[0049] a geographic data acquisition unit configured to acquire GPS data of a target region and generate a geographic data map of the target region according to the GPS data;

[0050] a grid set determination unit configured to perform multiple grid divisions on charging stations in the target region according to the geographic data map, count a rationality index of each charging station grid set obtained by each division, and determine an actual grid set suitable for the target region according to the counted rationality indexes; wherein the rationality index is determined based on charging compensation relationships between charging station grids in the charging station grid set;

[0051] The charging station quantity determination unit is configured to traverse each actual grid in the actual grid set, and for any actual grid, identify total charging demand of the actual grid, and determine a charging station quantity range according to maximum service capability and minimum service capability of a charging station in the actual grid and the total charging demand.

[0052] The candidate site determination unit is configured to determine a candidate site combination set corresponding to the actual grid based on combinations of different charging station quantities in the charging station quantity range, wherein each candidate site combination represents a quantity of candidate sites.

[0053] The site selection and capacity determination scheme determination unit is configured to determine a target charging station site selection and capacity determination scheme for any candidate site combination in the candidate site combination set based on a pre-set charging station site selection and capacity determination model, wherein the charging station site selection and capacity determination model is constructed according to charging pile types in each charging station, quantities of different types of charging piles, and coverage of the charging station.

[0054] In a third aspect, an embodiment of the present application provides a computer device, comprising:

[0055] A memory and a processor, which are communicatively connected with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the electric vehicle charging station site selection and capacity determination method.

[0056] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make a computer perform the electric vehicle charging station site selection and capacity determination method. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0058] Figure 1 A flowchart of an electric vehicle charging station site selection and capacity determination method provided by an embodiment of the present application;

[0059] Figure 2 A flowchart of a reasonable degree index of a charging station grid set provided by an embodiment of the present application;

[0060] Figure 3 A flowchart of a charging station quantity range determination provided by an embodiment of the present application;

[0061] Figure 4 The first flowchart of step S7 provided for the embodiments of the present application;

[0062] Figure 5 The second flowchart of step S7 provided for the embodiments of the present application;

[0063] Figure 6 The flowchart of the construction method of the objective function in the charging station site selection and capacity determination model provided for the embodiments of the present application;

[0064] Figure 7 The flowchart of step S91 provided for the embodiments of the present application;

[0065] Figure 8 The flowchart of step S93 provided for the embodiments of the present application;

[0066] Figure 9 The block diagram of a charging station site selection and capacity determination device provided for the embodiments of the present application;

[0067] Figure 10 The structural schematic diagram of a computer device provided for the embodiments of the present application. DETAILED DESCRIPTION

[0068] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative work fall within the scope of protection of the present application.

[0069] With the rapid rise of the electric vehicle (EV) market, the construction of charging facilities has become a key bottleneck restricting the sustainable and healthy development of the EV industry. With the sharp increase in the number of EVs, the demand for charging equipment has also increased significantly. Although the growth in the number of charging equipment can be considered as a positive development of the industry, the relationship between the number and quality of charging stations is still a major problem that needs to be solved. The layout, configuration and utilization efficiency of charging facilities have a direct impact on the speed of EV popularization and user experience. Whether the distribution of charging stations is reasonable, the equipment configuration is scientific and the utilization rate is up to standard will affect the charging convenience and service satisfaction of users.

[0070] In recent years, there have been a large number of literatures discussing the site selection and capacity determination of electric vehicle charging stations. The site selection and capacity determination of charging stations are subject to various factors, including land cost, construction cost, traffic flow, traffic network, charging demand, etc. These factors provide a complex decision-making background when selecting the site and capacity of charging stations. Traditional researches mainly focus on the construction investment cost of charging stations, and usually select the appropriate location and scale through mathematical optimization models to ensure that the charging station can cover the predetermined area and meet the basic traffic demand.

[0071] However, most of the current researches focus on the macro factors such as the cost of building charging facilities, coverage and traffic flow, ignoring the user experience and demand details of charging stations. In fact, the charging behavior and satisfaction of users on charging services play a crucial role in the actual operation of charging stations. Considering the charging demand of users at different times and different geographical locations can not only effectively improve the charging experience of users, but also optimize the operation efficiency of charging stations.

[0072] The research on charging behavior has gradually attracted attention in recent years, especially in how to incorporate the actual needs and charging habits of users into the site selection and capacity determination model of charging stations. When choosing a charging station, factors such as charging time, distance, charging pile type (such as slow, fast, super-fast charging) and service quality will have an important impact on the utilization rate of the charging station. If the charging station cannot provide enough fast charging facilities, users may give up the site due to long waiting time, resulting in low utilization of charging facilities and potential risk of user loss. In addition, the geographical location of the charging station is also a key factor affecting the charging demand of users. If the site is set in a place far away from the main traffic flow or difficult to access, it may also lead to low user frequency and affect the overall utilization efficiency of the facility.

[0073] Therefore, how to optimize the site selection of charging stations, reasonably configure the layout to reduce the charging time of users and improve the utilization rate of charging stations is a technical problem that needs to be solved at present.

[0074] In order to solve the above technical problems, according to the embodiments of the present application, a method for selecting and determining the capacity of an electric vehicle charging station is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from here.

[0075] In this embodiment, a method for selecting and determining the capacity of an electric vehicle charging station is provided, Figure 1 The flowchart of the method for selecting and determining the capacity of an electric vehicle charging station provided by the embodiments of the present application is shown in Figure 1 As shown in the figure, the flow includes the following steps:

[0076] Step S1, obtain the GPS data of the target area, and generate a geographic data map of the target area according to the GPS data.

[0077] Specifically, the target area can be a city, a city area, or a specific area. In order to solve the site selection and capacity problem, it is necessary to first determine the coverage area of the charging facility and determine the range of the area where GPS data needs to be collected. The data source of GPS data can include GPS trajectory data of vehicles such as taxis, buses, and private cars. A large amount of traffic flow, road condition, and driving behavior data can be obtained through these trajectory data. It should be noted that the data collection process is through user permission to ensure data privacy protection. These data can be uploaded and collected through vehicle-mounted GPS devices and mobile applications (such as taxi or shared travel platforms). The GPS data is used to analyze the traffic flow in the target area, including peak hours, traffic hotspots, and traffic bottlenecks. The charging demand of different areas is analyzed, especially in areas where electric vehicle users are often present, such as business districts, residential areas, and public transportation hubs. Using geographic information system (GIS) tools such as ArcGIS, the GPS data is superimposed with existing street maps, traffic networks, and urban infrastructure data. Through spatial analysis, areas with high traffic flow and high user charging demand are identified. These areas are usually the best site selection areas for electric vehicle charging stations.

[0078] Step S3, according to the geographic data map, the charging stations in the target area are divided into multiple grids, for each charging station grid set obtained by each division, the rationality index of the charging station grid set is counted, and according to the rationality index obtained by counting, the actual grid set suitable for the target area is determined; wherein the rationality index is determined based on the charging compensation relationship between each charging station grid in the charging station grid set.

[0079] Specifically, according to the geographic data map, different grid sizes are selected for multiple divisions using geographic information system (GIS) tools, etc. For example, 1km×1km, 2km×2km, etc. By obtaining a plurality of preset statistical periods, and in each period, for each charging station grid in the charging station grid set, the number of charging stations with charging compensation relationship in the grid is determined. According to the number of charging stations counted in each period, the charging compensation coefficient of the charging station grid is generated. Then, the average value of the charging compensation coefficients of all charging station grids is calculated as the rationality index to evaluate the overall layout and operation efficiency of the charging station grid. The rationality index of each charging station grid set obtained by each grid division is compared. The charging station grid set with the largest rationality index is selected as the actual grid set finally used.

[0080] Step S5, traverse each actual grid in the actual grid set, and for any actual grid therein, identify the total charging demand of the actual grid, and determine the charging station quantity range according to the maximum service capability and the minimum service capability of the charging stations in the actual grid and the total charging demand.

[0081] Specifically, the number of electric vehicles (EVs) is one of the most basic indicators for measuring charging demand. With the popularization of electric vehicles, the charging demand also increases. When performing charging station site selection and capacity determination, each actual grid is analyzed one by one to obtain the total charging demand, i.e., the total charging demand of all vehicles in each actual network. From the geographic data map, according to the historical data of vehicle charging behavior, the charging frequency and charging amount of vehicles in each grid can be counted:

[0082]

[0083] wherein n is the number of vehicles in the actual grid.

[0084] For example, the total charging demand of a certain actual grid in a certain time period is 10,000 vehicles.

[0085] Step S7, based on the combinations of different charging station quantities in the charging station quantity range, determine a candidate site combination set corresponding to the actual grid; wherein each candidate site combination represents the number of candidate sites.

[0086] Specifically, according to the total charging demand, different charging station quantities are selected for clustering analysis within the determined minimum charging station quantity and maximum charging station quantity range. Each combination represents a possible charging station layout scheme, and the number of clusters is the number of candidate sites. By clustering algorithm (such as K-means), the charging demand points are divided to generate multiple candidate site combinations. These combinations not only cover different station quantities, but also reflect the charging station distribution under different layout schemes in the actual grid, providing diversified choices for subsequent optimization analysis.

[0087] Step S9, for any candidate site combination in the candidate site combination set, determine a target charging station site selection and capacity determination scheme based on a pre-set charging station site selection and capacity determination model; wherein the charging station site selection and capacity determination model is constructed according to the charging pile type in each charging station, the number of different types of charging piles, and the coverage rate of the charging station.

[0088] Specifically, the constraint conditions in the charging station site selection and capacity determination model include charging station load, travel time, charging pile type selection, and number of multiple types of charging piles; wherein the charging station load constraint conditions include:

[0089] Basic load constraint:

[0090]

[0091] wherein P i,t is the load of the ith determined construction site at time t; is the maximum load that the ith determined construction site can bear.

[0092] Load balancing constraint:

[0093]

[0094] wherein m is the number of determined construction sites; P g,t is the total power that the power grid can provide at time t, in the load constraint model, in addition to considering the load capacity of a single charging station, the load of each site and the power supply capacity of the external power grid or the load balancing between other sites also need to be considered;

[0095] Travel time constraint condition:

[0096]

[0097]

[0098]

[0099] wherein T w,i (t) is the travel time of the electric vehicle w to the determined construction site i; dis(S w -S i ) is the distance from the current position of the electric vehicle w to the determined construction site i; μ(t) is the congestion coefficient at time t, reflecting the actual traffic situation; T max is the maximum allowable time for the electric vehicle w to go to the determined construction site i; is the average travel speed; is the number of electric vehicles located on the road section L at time t; v w (t) is the real-time travel speed; dis(L w (t)-L w (t-1)) is the geographical distance of the electric vehicle w at time t and time t-1; is the sampling period.

[0100] Charging pile type selection constraint condition:

[0101]

[0102] wherein, is the charging mode of the electric vehicle w at time t; m is the number of determined construction sites.

[0103] The multi-type charging pile quantity constraint condition includes:

[0104] Slow charging pile quantity constraint:

[0105]

[0106] wherein, is the number of slow charging piles required by the electric vehicle at the charging station i at time t; is the number of slow charging piles constructed at the charging station i.

[0107] Fast charging pile quantity constraint:

[0108]

[0109] wherein, is the number of fast charging piles required by the electric vehicle at the charging station i at time t; is the number of fast charging piles constructed at the charging station i.

[0110] Super-fast charging pile quantity constraint:

[0111]

[0112] wherein, is the number of super-fast charging piles required by the electric vehicle at the charging station i at time t; is the number of super-fast charging piles constructed at the charging station i.

[0113] By constructing a charging station site selection and capacity determination model, considering the total construction cost and coverage rate as two objective functions, and introducing charging station load, travel time, charging pile type selection, and multi-type charging pile quantity as constraint conditions, the optimal configuration of charging station layout can be achieved. This model not only ensures the economy of the charging station, but also improves the service coverage and user experience, providing a scientific basis for the site selection and capacity determination of electric vehicle charging stations.

[0114] For any candidate station cluster combination in the candidate station cluster combination set, the charging station site selection and capacity determination model is solved to obtain the target charging station site selection and capacity determination scheme corresponding to the minimum objective function.

[0115] Specifically, the objective function is minimized while satisfying the constraint conditions, and the CPLEX solver in MATLAB is used to solve the charging station site selection and capacity determination model. For example, it can be a comprehensive cost difference analysis under different charging pile configurations, only slow charging piles, only slow and fast charging piles, slow, fast and super-fast charging piles, etc. The target charging station site selection and capacity determination scheme corresponding to the minimum objective function is output, ensuring that the layout of the charging station meets the cost, load and user experience constraints, and achieving the balance between economy and service coverage.

[0116] The embodiment provides a method for site selection and capacity determination of an electric vehicle charging station. A geographic data map is generated based on GPS data of a target area, and multiple grid divisions are performed. A rationality index of each grid is calculated, and compensation relationships and load adjustment capabilities between charging station grids are evaluated. Then, based on the rationality index, the most suitable grid set is selected to ensure that the charging demand in the area is effectively covered. By analyzing the total charging demand of each grid and the service capacity of the charging station, a reasonable charging station quantity range is determined, and a candidate station combination set is generated. Finally, a charging station site selection and capacity determination model is used to optimize the site selection and capacity configuration of the charging station, so as to ensure that the charging station provides efficient service at the optimal position, reduces resource waste and overload, improves the utilization efficiency of the charging station, reduces the charging waiting time of users, balances the power grid load, and thus effectively improves the overall efficiency of the charging service and the user experience.

[0117] Figure 2 The embodiment provided by the present application provides a flowchart for calculating the rationality index of the charging station grid set. The flowchart can include the following steps:

[0118] In step S31, a plurality of preset statistical periods are obtained.

[0119] Specifically, in order to analyze the charging station grid, a statistical period needs to be preset. These periods can be timed, for example, every hour, every two hours, or every half day. The selection of the statistical period depends on the analysis requirement, and a time interval that has a significant impact on the charging demand fluctuation is usually selected. The statistical period is the basis for analyzing the charging station usage, and determines the load condition of the charging station in these time intervals and whether the peak shifting charging can be realized.

[0120] In step S33, for any statistical period and for any charging station grid in the charging station grid set, the first number of charging stations with charging compensation relationship in the charging station grid is determined in the statistical period, and the charging compensation coefficient of the charging station grid is generated based on the first number obtained in each statistical period.

[0121] Specifically, according to each charging station grid in the charging station grid set, the first number of charging stations with charging compensation relationship in the charging station grid is determined in each statistical period. Then, these data are used to calculate the charging compensation coefficient of the charging station grid. The charging compensation relationship refers to that if the power supply loads of two charging stations can be staggered (i.e., one is in the peak and the other is in the valley) in the same statistical period, the two charging stations have the charging compensation relationship. The charging compensation coefficient is calculated as follows:

[0122]

[0123] wherein R i is the charging compensation coefficient of the i-th charging station grid; T is the total number of statistical time periods; N i (t) is the number of charging stations in the i-th charging station grid that have a charging compensation relationship in the statistical time period t, i.e., the first number; Max(N i (t)) is the maximum number of charging stations in the i-th charging station grid that have a charging compensation relationship in all statistical time periods.

[0124] That is, the compensation coefficient of each charging station grid takes into account the ratio of the number of charging stations having a compensation relationship in different statistical time periods to the maximum number. The purpose of this is to compare the load changes of charging station grids at different times. The more volatile the charging station grid, the lower its compensation coefficient, indicating that its rationality is poorer. Conversely, the more stable the charging station grid, the higher its compensation coefficient, indicating that its rationality is better.

[0125] Step S35, the average of the charging compensation coefficients of each charging station grid in the charging station grid set is calculated, and the average is taken as the rationality index of the charging station grid set.

[0126] Specifically, the average of the charging compensation coefficients of all charging station grids is calculated as the rationality index of the entire charging station grid set. The specific calculation method is as follows:

[0127]

[0128] wherein R is the rationality index of the entire charging station grid set; K is the total number of charging station grids included in the charging station grid set.

[0129] By calculating the average of the charging compensation coefficients of each charging station grid, the overall rationality index is obtained. The higher the rationality index, the smaller the load fluctuation of each charging station grid in the entire charging station grid set, indicating better charging compensation capability. Conversely, the lower the rationality index, the greater the load fluctuation of the charging station grids in the charging station grid set, indicating poor charging compensation capability.

[0130] The embodiment obtains a plurality of preset statistical time periods, and in each time period, determines the number of charging stations having a charging compensation relationship in each charging station grid in the charging station grid set. According to the number of charging stations counted in each time period, the charging compensation coefficient of the charging station grid is generated. Then, the average of the charging compensation coefficients of all charging station grids is calculated as the rationality index to evaluate the overall layout and operation efficiency of the charging station grid. In this way, the area with small load fluctuation and strong charging compensation capability can be effectively identified, so as to optimize the site selection of the charging station, reasonably layout the charging station, improve the utilization rate of the charging station, reduce the charging waiting time of the user, and improve the overall charging efficiency.

[0131] Figure 3 The flow chart for determining the range of charging station number provided by the embodiments of the present application can include the following steps:

[0132] Step S51, according to the maximum service capacity of the charging stations in the actual grid and the total charging demand, determine the minimum charging station number.

[0133] Specifically, by calculating the minimum charging station number N min The minimum charging station number required to meet the total charging demand can be determined. It avoids excessive charging stations in areas with low demand, while ensuring that each area has sufficient service capacity to meet user demand. The service capacity of the charging station can be set to 1350, 600 and 300 daily service capacity for large, medium and small charging stations respectively. Therefore, the maximum service capacity of the charging station is 1350, and the minimum service capacity of the charging station is 300. The minimum charging station number N min = 10000 / 300 = 33.

[0134] Step S53, according to the minimum service capacity of the charging stations in the actual grid and the total charging demand, determine the maximum charging station number.

[0135] Specifically, by determining the maximum charging station number N max The maximum charging station number can avoid building too many charging stations in areas with too low demand. The maximum charging station number limits the number of charging stations to avoid duplication of existing charging stations, and ensures that the service area will not be repeated and wasted resources due to too many sites. The maximum charging station number N max = 10000 / 1350 = 7.

[0136] Step S55, determine the range of charging station number from the minimum charging station number to the maximum charging station number as the charging station number range.

[0137] Specifically, by selecting different numbers of charging stations for clustering analysis between the minimum and maximum numbers of charging stations, the number of charging stations can be flexibly adjusted to adapt to different demand changes. For example, in areas with high demand, more charging stations can be selected to improve service capacity, while in areas with low demand, the number of charging stations can be appropriately reduced to avoid over-construction. By using a clustering algorithm (such as K-means), the clustering effect under different K values can be analyzed according to the selected number of charging stations K, the relationship between charging demands can be found, and it can be determined which areas in the actual grid need more charging station construction and which areas can reduce or redistribute resources. By evaluating the quality of clustering (such as silhouette coefficient or elbow rule), the quality of the clustering result can be ensured, thereby providing a more scientific basis for charging station site selection. Finally, the candidate station combination set is obtained according to the clustering result.

[0138] The embodiment determines the minimum number of charging stations to ensure that each charging demand point is covered and to avoid resource waste, and then determines the maximum number of charging stations to prevent over-construction and ensure that the service capacity matches the demand. Then, by selecting different numbers of charging stations through clustering analysis, the charging station site selection can be effectively optimized, the resource utilization rate can be improved, the user's charging time can be reduced, the actual grid can be avoided from being repeatedly covered and having blank areas, and the use efficiency of the charging station can be improved.

[0139] Figure 4 The first flowchart of step S7 provided by the embodiment of the present application can include the following steps:

[0140] Step S711, for each number of charging stations in the range of charging station numbers, determine the distance from each charging demand point in the actual grid to the cluster center, and determine the sum of squared errors according to the distance.

[0141] Specifically, the charging demand points are divided into several clusters, and each cluster corresponds to a charging station. The cluster center here represents the location of a charging station. The purpose of calculating the distance from each charging demand point to the cluster center is to measure the relationship between the charging demand point and its nearest charging station. The center of the cluster is the "average position" of all charging demand points in the cluster, that is, the mean value of the coordinates of all charging demand points. For each number of charging stations (i.e., different clustering numbers K), the distance from each charging demand point in the actual grid to the cluster center is determined, and the sum of squared errors (SSE) is calculated. The purpose of this step is to evaluate the compactness of clustering, that is, the sum of the distances between the sample points in the cluster and the cluster center. The smaller the sum of squared errors (SSE), the better the clustering effect, the closer the points in the cluster to the cluster center, and the more appropriate the site selection of the charging station. The calculation method is as follows:

[0142]

[0143] where K is the number of clusters, i.e., the number of charging stations; C z is the z-th cluster (charging station) set, containing all charging demand points within the cluster; x is a certain charging demand point belonging to cluster C z ; μ z is the cluster center (charging station location) of the z-th cluster, denotes the Euclidean distance from a charging demand point to the cluster center.

[0144] In step S713, the error sum of squares corresponding to different numbers of charging stations is plotted into a relationship graph, and a candidate station combination set is determined from the relationship graph.

[0145] Specifically, for different numbers of charging stations K (for example, K = 7, 8, 9, …, 33), the corresponding error sum of squares (SSE) is calculated. The relationship graph is plotted with the number of charging stations K as the horizontal axis and the error sum of squares (SSE) as the vertical axis. By observing the relationship graph, the point at which SSE significantly decreases, i.e., the elbow point, is found. The elbow point refers to the point at which the SSE decreases significantly slows down. When the value of K is small, increasing the number of charging stations can significantly reduce SSE, because more charging stations can more finely match the demand points. However, as the value of K increases, the speed of SSE decrease slows down, and eventually tends to be stable. The value of K corresponding to the elbow point is usually the optimal number of charging stations, because it reduces SSE while avoiding redundancy caused by too many charging stations. For example, by performing clustering analysis from K = 7 to K = 33, the SSE corresponding to each value of K is calculated. In the relationship graph, the SSE values from K = 7 to K = 33 will usually decrease rapidly, but as the value of K increases, the speed of SSE decrease will gradually slow down. The elbow point is found, such as between K = 18 and K = 23, at which the SSE decreases becomes flat, meaning that the range of K = 18 to K = 23 is a suitable number of charging stations, i.e., the candidate station combination set is K = 18, 19, 20, …, 23.

[0146] The embodiment calculates the distance from each charging demand point to the cluster center according to different numbers of charging stations, and evaluates the matching degree of the charging demand points and the charging stations through the error sum of squares. The smaller the error sum of squares, the shorter the distance between the charging demand points and the charging stations, and the higher the matching degree, thereby optimizing the charging station site selection and improving the coverage and utilization of the charging stations. Then, by plotting the relationship graph of the error sum of squares and the number of charging stations, an appropriate number of charging stations is selected to avoid resource waste or insufficient coverage. After reasonable layout, the distance of users to the charging stations is shortened, the charging waiting time is reduced, and the overall charging efficiency and user experience are improved. At the same time, regional resources can be scientifically configured to ensure the balanced distribution of charging stations in each region, achieving efficient use of resources.

[0147] Figure 5The second flowchart of step S7 provided by the embodiments of the present application can include the following steps:

[0148] Step S731, for each charging station quantity in the charging station quantity range, determine the first average distance from each charging demand point in the actual grid to the adjacent charging demand point in the same cluster.

[0149] Specifically, in order to evaluate the tightness of the cluster in which each charging demand point is located. By calculating the first average distance a(x) from each charging demand point to other adjacent charging demand points in the same cluster, the aggregation of the charging demand points within the cluster can be determined. If the charging demand points in the cluster are very close, it means that the charging demand points in the cluster have strong aggregation and high internal consistency. Higher tightness in the cluster means that the charging stations in the cluster can efficiently serve, reduce the charging waiting time of users, and improve the utilization rate of charging stations.

[0150] Step S733, determine the second average distance from each charging demand point in the actual grid to the charging demand point in the nearest cluster.

[0151] Specifically, in order to evaluate the separation between each charging demand point and other clusters. By calculating the second average distance b(x) from each charging demand point to the charging demand point in the nearest cluster, the distance between clusters can be measured, and the independence of different clusters can be analyzed. If the average distance between clusters is large, it means that the separation between clusters is good, that is, the "discrimination" between charging demand points is high, and the charging stations can avoid covering the demand points of other clusters, reducing resource overlap. Strong separation helps to improve accuracy and efficiency, ensuring that the coverage range of each charging station will not be too overlapped, thereby improving the utilization rate of resources.

[0152] Step S735, according to the ratio between the first average distance and the second average distance, determine the individual contour coefficient of each charging demand point, and according to the individual contour coefficient, determine the overall contour coefficient matched with each charging station quantity.

[0153] Specifically, the contour coefficient considers the tightness within the cluster and the separation between the clusters.

[0154]

[0155] Wherein, S(x) is the individual contour coefficient of the charging demand point x, a(x) is the first average distance from the charging demand point x to the adjacent charging demand point in the same cluster, and b(x) is the second average distance from the charging demand point x to the charging demand point in the nearest cluster.

[0156] When a(x) is small and b(x) is large, S(x) is large, which means that the charging demand point x is close to the cluster it belongs to and has good separation from other clusters, and the clustering quality is high.

[0157] Step S737, screening the overall contour coefficient to obtain a candidate site combination set.

[0158] Specifically, after calculating the individual contour coefficient of each charging demand point, the next step is to calculate the average value of the contour coefficients of all charging demand points to obtain the overall contour coefficient. This overall contour coefficient reflects the quality of the entire cluster, helping to judge the clustering effect under the current number of charging stations (K). When different numbers of charging stations K are selected, the overall contour coefficient will change. By screening the K value with a higher overall contour coefficient, the optimal number of charging stations and layout scheme are selected to ensure that the site selection of charging stations is reasonable and efficient. For example, from K = 7 to K = 33, the overall contour coefficient corresponding to each K value is calculated. After screening, it is determined that K = 18 to K = 23 is the appropriate range of the number of charging stations, i.e., the candidate site combination set is K = 18, 19, 20, …, 23.

[0159] This embodiment can evaluate the tightness within the cluster by calculating the first average distance from each charging demand point to the adjacent charging demand point in the same cluster, ensuring that the charging stations effectively cover the actual grid and reducing user waiting time. Secondly, calculating the second average distance from each charging demand point to the nearest charging demand point in the cluster helps to evaluate the separation between clusters, avoid repeated coverage, and improve the utilization of the site. Further, in combination with the calculation of the contour coefficient, the clustering quality can be comprehensively evaluated to ensure the rationality of the number and layout of charging stations, and to avoid excessive or insufficient construction. Finally, by screening the overall contour coefficient, the optimal number of charging stations and layout scheme are determined to optimize the configuration of charging stations, improve the utilization of charging stations, and reduce user charging time.

[0160] Figure 6 The flowchart of the construction method of the objective function in the charging station site selection and capacity determination model provided by the embodiments of the present application can include the following steps:

[0161] Step S91, for any candidate site combination and corresponding candidate site location, determine the target construction total cost corresponding to any candidate site combination.

[0162] Specifically, the construction cost, equipment cost, equipment maintenance fee, and power consumption cost of each candidate site location are comprehensively evaluated. Specifically, the construction cost (including land development and infrastructure construction cost), equipment purchase and installation cost, equipment maintenance fee, and power consumption cost during operation of each candidate site location are collected. By introducing weight coefficients (such as γ, δ, ε) to adjust the relative importance of different cost items, the actual demand can be more flexibly reflected. Finally, combined with the decision variable x j(whether to build a charging station at candidate site location j), the target construction total cost of any candidate site combination is obtained by weighted sum of the costs of all candidate site locations. The calculation of this cost not only provides basic data for subsequent optimization analysis, but also helps decision-makers evaluate the economic efficiency among multiple candidate schemes, so as to select the charging station layout scheme with the highest cost-effectiveness while meeting the charging demand.

[0163] Step S93, determine the coverage range matched with the candidate site location, and determine the target coverage rate corresponding to any candidate site combination based on the coverage range.

[0164] Specifically, the coverage range of each candidate site location is determined according to the preset service radius (such as 2.5 kilometers). This can usually be achieved through buffer analysis in geographic information system (GIS) tools, generating the coverage range of each candidate site location. Then, the target charging demand points located within these coverage ranges are screened out. Based on these data, the target coverage rate is calculated, which is the ratio of the target charging demand points covered by all candidate site locations to the total charging demand in the target area. This ratio intuitively reflects the degree of satisfaction of the current candidate site combination to the charging demand of the entire region, and is an important indicator for measuring the pros and cons of the charging station layout scheme. By maximizing the target coverage rate, it can be ensured that the layout of the charging station can efficiently serve as many electric vehicle users as possible, thereby optimizing the site selection and resource allocation of the charging station.

[0165] Step S95, convert the target coverage rate into target coverage loss cost using a preset penalty cost coefficient.

[0166] Specifically, the target coverage rate represents the proportion of the target charging demand points covered by each candidate site combination to the total charging demand. If the target coverage rate is lower than the expected value, it means that the coverage effect of this candidate site combination is not ideal, and the charging demand of the users cannot be fully met. In order to quantify this coverage deficiency into a calculable cost, a penalty cost coefficient p is introduced, and the target coverage loss cost is converted by the following formula: target coverage loss cost = p x (1-T n ). The target coverage loss cost reflects the economic loss caused by the failure to effectively meet the charging demand. This loss not only directly affects the utilization rate of the charging station, but also may lead to the decline of user experience, thereby bringing more operating pressure.

[0167] Step S97, determine the target function as the total of the target construction total cost and the target coverage loss cost.

[0168] Specifically, the target function is the core of site selection optimization, which integrates the total construction cost and the target coverage loss cost. min F = CO + p x (1-T n). Wherein, CO is the target total construction cost, usually including site construction, equipment procurement, installation and commissioning, etc. p x (1-T n ) is the target coverage loss cost. In the optimization process, the purpose of the objective function is to find the best charging station site selection and capacity planning scheme by minimizing the total cost. This scheme should ensure that the construction cost of the charging station and the penalty cost due to insufficient coverage reach a balance point. In other words, the optimization process aims to select a charging station layout that can effectively meet user demand and reasonably control cost.

[0169] The embodiment ensures the economic feasibility of the charging station site selection and capacity planning scheme by calculating the target total construction cost corresponding to the candidate site combination; ensures that the charging station can meet user demand, thereby shortening the user's charging time and waiting time, by determining the coverage range and target coverage rate of the candidate site location; then uses the penalty cost coefficient to quantify the insufficient coverage as economic loss, prompting the optimization algorithm to improve the coverage rate; finally, by combining the target total construction cost and the target coverage loss cost, the objective function is constructed to find the best charging station site selection and capacity planning scheme, which meets user demand and controls cost.

[0170] Figure 7 The flowchart of step S91 provided by the embodiment of the present application can include the following steps:

[0171] Step S911, for any candidate site combination and corresponding candidate site location, determine the construction cost, equipment cost, equipment maintenance cost and power consumption cost matched with each candidate site location.

[0172] Specifically, determine the multiple cost factors of each candidate site location in any candidate site combination. Construction cost : including land acquisition cost, infrastructure construction cost, etc. The level of construction cost is usually determined by the geographical location of the land, the price of the land and the standard of infrastructure construction (such as power grid access, road construction, etc.). Different candidate sites may differ in these factors, thereby affecting the construction cost. Equipment cost : refers to the procurement and installation cost of the charging equipment itself, including charging piles, power distribution equipment, etc. The market price and installation cost of the equipment may vary due to factors such as brand, technology, quantity, installation environment, etc. Equipment maintenance cost : including equipment maintenance, repair, personnel management cost, etc., which is closely related to the workload and maintenance plan of the equipment. Some equipment may require more maintenance and inspection, resulting in higher maintenance cost. Power consumption cost E j : refers to the cost related to power consumption during the operation of the charging station. The power consumption cost depends on the power demand of the site, the local electricity price and the usage frequency of the charging station.

[0173] Step S913, according to whether the decision variable of building a charging station at the candidate site position, the weighted sum of the construction cost, equipment cost, equipment maintenance cost and power consumption cost is obtained. The single construction total cost of each candidate site position is matched.

[0174] Specifically, according to the decision variable x j , whether to build a charging station at each candidate site position, the weighted sum of the various costs is calculated to calculate the single construction total cost CO of each candidate site position:

[0175]

[0176] Where x j is the decision variable, indicating whether to build a charging station at the candidate site position (1 if it means to build, if it is 0, it means not to build). γ, δ, ε are weight coefficients, used to adjust the influence degree of each cost factor on the total cost. Different weight coefficients reflect the importance of different cost factors. For example, some projects may pay more attention to equipment cost than construction cost, or maintenance cost may be particularly important in some areas.

[0177] Step S915, according to all single construction total costs, determine the target construction total cost corresponding to any candidate site combination.

[0178] Specifically, by weighted sum of the single construction total cost of all candidate sites, the target construction total cost of the whole candidate site combination is obtained:

[0179]

[0180] Where n is the number of any candidate site combination, i.e. the number of candidate sites, and CO represents the total cost of all candidate sites. Through this objective function, the whole optimization problem becomes a minimization problem, the goal is to select the optimal site construction combination to minimize the total cost.

[0181] The embodiment determines the construction cost, equipment cost, equipment maintenance cost and power consumption cost of each candidate site position according to the candidate site position. Then, through the decision variable to judge whether to build a charging station at the candidate site position, combined with the weighted sum of the various costs, the single construction total cost of each candidate site position is obtained. Finally, according to all single construction total costs, the final target construction total cost is determined. The goal of optimization is not only to select the lowest cost site, but also to consider the site utilization and user demand, to reasonably configure the site layout, to ensure that the charging station meets the user demand while having high utilization rate and low operating cost.

[0182] Figure 8The flowchart of step S93 provided by the embodiments of the present application can include the following steps:

[0183] Step S931, determine the coverage range matching the candidate site location, and screen the target charging demand points in the coverage range.

[0184] Specifically, according to the actual demand or design standard, the coverage range R can be set as a fixed value, for example, 2.5 kilometers. This value represents the maximum radius range that can be covered from the candidate site location. The specific coverage range selection depends on different factors, such as: the driving range of electric vehicles is limited, so the coverage radius of the site needs to consider the regular endurance of electric vehicles. In densely populated areas, the coverage range may need to be smaller in order to more effectively distribute charging stations; while in sparsely populated areas, the coverage range may be larger to ensure enough charging sites.

[0185] Next, by setting the coverage range R, the target charging demand points in the coverage range are screened. Specifically, given any candidate site location , the charging demand points in the coverage range R of the candidate site location will be considered as "target charging demand points , The set D contains all charging demand points that meet the conditions.

[0186]

[0187] Where, x i is the coordinates of the charging demand point; is the jth candidate site location.

[0188] Step S933, determine the ratio of the target charging demand points to the total charging demand as the target coverage rate corresponding to any candidate site combination.

[0189] Specifically, by screening the target charging demand points , the target coverage rate T n corresponding to any candidate site combination can be calculated. The target coverage rate represents the proportion of the target charging demand points covered by any candidate site combination to the total charging demand points. The calculation formula is as follows:

[0190]

[0191] Where, n is the number of any candidate site combination, i.e., the number of candidate sites; C is the total charging demand.

[0192] The embodiment determines the coverage range of each candidate site location, and screens out target charging demand points in the coverage range. Then, by calculating the ratio between the target charging demand points covered by each candidate site location and the total demand points, a target coverage rate is obtained. The target coverage rate reflects the effectiveness of site selection, helping to evaluate whether the site can effectively meet user demand. A higher target coverage rate indicates that the site selection can better serve users, thereby improving the utilization rate of charging stations and reducing user charging waiting time.

[0193] The specific implementation of the present application is described below in combination with a specific application scenario. The present embodiment takes real geographic information of a certain region as the background, and collects actual travel trajectory data of taxis in a certain city. The charging station site selection analysis is performed for nearly 10,000 electric vehicle (EV) charging demand points in a certain region. Through analysis based on SSE value and elbow method, it is found that the contour coefficient is concentrated between 0.54 and 0.565, and the difference is small, making it difficult to determine the best K value. As the K value increases, when the K value exceeds 17, the descending rate of SSE tends to be flat. Therefore, combined with the service capacity of the charging station, the daily service capacity of large, medium and small charging stations is 1350, 600 and 300 respectively. Through analysis, it is considered that the more reasonable candidate site combination set K value should be set between 18 and 23.

[0194] In ArcGIS 10.8, based on the coverage range R of 2.5 km, the total construction cost and coverage rate when K value is from 18 to 23 are calculated in turn. Then, the taxi data, geographic information data and candidate charging station data are imported into the ArcGIS platform for spatial analysis and visualization processing, providing basic data support for subsequent charging station site selection and layout.

[0195] Then, combined with the user demand points of each candidate site combination, the Monte Carlo simulation method is used to predict the charging station user load demand at different times. In order to comprehensively consider multiple constraints such as charging station construction cost, coverage range, load demand and user charging experience, the present embodiment constructs a mixed integer linear programming model, and introduces multiple types of charging equipment (such as slow charging, fast charging, super fast charging), i.e. a charging station site selection and sizing model with charging station construction total cost and charging station coverage rate as objective functions, and charging station load, travel time, charging pile type selection, multiple type charging pile quantity as constraint conditions. Finally, the CPLEX solver in the MATLAB platform is used to optimize and solve the charging station site selection and sizing model, determine the optimal capacity configuration scheme of each charging station, and obtain the construction of 21 charging stations, the total construction cost of 1209.7 (K$) yuan, and the coverage rate of 93.28%.

[0196] Correspondingly, please refer to Figure 9A block diagram of an electric vehicle charging station site selection and capacity determination device provided by an embodiment of the present application, the device comprising:

[0197] The geographic data acquisition unit 101 is configured to acquire GPS data of the target region and generate a geographic data map of the target region according to the GPS data.

[0198] The grid set determination unit 103 is configured to perform multiple grid divisions on the charging stations in the target region according to the geographic data map, count a rationality index of each charging station grid set obtained by each division, and determine an actual grid set suitable for the target region according to the counted rationality indexes. The rationality index is determined based on the charging compensation relationship between the charging stations in the charging station grid set.

[0199] The charging station number determination unit 105 is configured to traverse each actual grid in the actual grid set, identify the total charging demand of any actual grid, and determine a charging station number range according to the maximum service capacity and the minimum service capacity of the charging stations in the actual grid and the total charging demand.

[0200] The candidate site determination unit 107 is configured to determine a candidate site combination set corresponding to the actual grid based on combinations of different charging station numbers in the charging station number range. Each candidate site combination represents the number of candidate sites.

[0201] The site selection and capacity determination scheme determination unit 109 is configured to determine a target charging station site selection and capacity determination scheme for any candidate site combination in the candidate site combination set based on a pre-set charging station site selection and capacity determination model. The charging station site selection and capacity determination model is constructed according to the charging pile type in each charging station, the number of different types of charging piles, and the coverage rate of the charging station.

[0202] In some optional embodiments, the grid set determination unit 103 comprises:

[0203] A plurality of preset statistical time periods are acquired.

[0204] For any statistical time period and for any charging station grid in the charging station grid set, the first number of charging stations having a charging compensation relationship in the charging station grid is determined in the statistical time period, and a charging compensation coefficient of the charging station grid is generated based on the first numbers obtained in each statistical time period.

[0205] The mean value of the charging compensation coefficients of each charging station grid in the charging station grid set is calculated, and the mean value is taken as the rationality index of the charging station grid set.

[0206] In some optional embodiments, the charging station number determination unit 105 comprises:

[0207] determine the minimum number of charging stations according to the maximum service capacity of the charging stations in the actual grid and the total charging demand;

[0208] determine the maximum number of charging stations according to the minimum service capacity of the charging stations in the actual grid and the total charging demand;

[0209] determine the range of the number of charging stations as the number of charging station range from the minimum number of charging stations to the maximum number of charging stations.

[0210] In some optional embodiments, the candidate site determining unit 107 comprises:

[0211] determine the distance from each charging demand point in the actual grid to the cluster center for each number of charging stations in the number of charging station range, and determine the sum of squared errors according to the distance;

[0212] draw a relationship diagram of the sum of squared errors corresponding to different numbers of charging stations, and determine the candidate site combination set from the relationship diagram.

[0213] In some optional embodiments, the candidate site determining unit 107 comprises:

[0214] determine the first average distance from each charging demand point in the actual grid to the adjacent charging demand point in the same cluster for each number of charging stations in the number of charging station range;

[0215] determine the second average distance from each charging demand point in the actual grid to the charging demand point in the nearest cluster;

[0216] determine the individual contour coefficient of each charging demand point according to the ratio between the first average distance and the second average distance, and determine the overall contour coefficient matched with each number of charging stations according to the individual contour coefficient;

[0217] screen the overall contour coefficient to obtain the candidate site combination set.

[0218] In some optional embodiments, the construction method of the objective function in the charging station site selection and capacity determination model is as follows:

[0219] determine the target construction total cost corresponding to any candidate site combination for any candidate site combination and the corresponding candidate site position;

[0220] determine the coverage range matched with the candidate site position, and determine the target coverage rate corresponding to any candidate site combination based on the coverage range;

[0221] convert the target coverage rate into the target coverage loss cost by using a preset penalty cost coefficient;

[0222] The sum of the target total construction cost and the target coverage loss cost is determined as the target function.

[0223] In some optional embodiments, determining the target total construction cost corresponding to any candidate site combination comprises:

[0224] For any candidate site combination and the corresponding candidate site location, the construction cost, equipment cost, equipment maintenance cost and power consumption cost matched by each candidate site location are determined;

[0225] According to the decision variable of whether to construct a charging station at the candidate site location, the construction cost, equipment cost, equipment maintenance cost and power consumption cost are weighted and summed to obtain the single total construction cost matched by each candidate site location;

[0226] According to all single total construction costs, the target total construction cost corresponding to any candidate site combination is determined.

[0227] In some optional embodiments, determining the target coverage rate corresponding to any candidate site combination comprises:

[0228] The coverage range matched by the candidate site location is determined, and the target charging demand points in the coverage range are screened;

[0229] The ratio of the target charging demand points to the total charging demand is determined as the target coverage rate corresponding to any candidate site combination.

[0230] The further function description of each module and unit is the same as the above-mentioned corresponding embodiments, and will not be repeated here.

[0231] The electric vehicle charging station site selection and capacity determination device in the embodiment is presented in the form of a functional unit. The unit here refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory executing one or more software or fixed programs, and / or other devices that can provide the above functions.

[0232] Please refer to Figure 10 , Figure 10 A structural schematic diagram of a computer device provided in the embodiment of the present application is shown in FIG. 1. Figure 10As shown, the computer device includes one or more processors 10, memory 20, and interfaces 30 for external devices such as a keyboard and a mouse and peripheral devices such as disk devices or other storage devices. One or more busses 10 can be used to implement the interface between the various internal and external components and can be implemented using any one or more of a variety of bus technologies including a System bus, PCI, SCSI, AGP, Super- I / O bus, etc. Furthermore, various buses can be used in front side buses, back side buses, and other bus configurations based on any bus or messaging technology known to those skilled in the art. Figure 10 The processor 10 is used in the embodiments described herein as an example.

[0233] The processor 10 can be a central processing unit, a network processor, or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic device, a general array logic, or any combination thereof.

[0234] The memory 20 stores instructions that can be executed by the at least one processor 10 to cause the at least one processor 10 to perform the methods described above.

[0235] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system, application programs, and the like for controlling the computer device. The data storage area can store application data and the like that can be used by the computer device. The memory 20 can also include a high speed memory, such as RAM, and can also include a nonvolatile memory, such as flash memory, a hard disk drive, or a solid state drive. In some embodiments, the memory 20 can include memory that is remote from the processor 10, such as a network storage device that is connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and a combination thereof.

[0236] The memory 20 can include a volatile memory, such as a random access memory, and can also include a nonvolatile memory, such as a flash memory, a hard disk drive, or a solid state drive. The memory 20 can also include a combination of the above-mentioned types of memory.

[0237] The computer device also includes a communication interface 30 that can be used to communicate with other devices or computer devices or networks, including the Internet.

[0238] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, processor or hardware, implements the method shown in the above embodiments.

[0239] It can be understood that, before using the technical solutions disclosed in the embodiments of the present disclosure, the type, use range, use scenario, etc. of the personal information involved in the present disclosure should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0240] For example, in response to receiving the active request of the user, prompt information is sent to the user to explicitly prompt the user that the operation requested to be performed will require obtaining and using the personal information of the user. Thus, the user can voluntarily choose whether to provide the personal information to the software or hardware such as electronic device, application program, server or storage medium that performs the operation of the technical solutions of the present disclosure according to the prompt information.

[0241] As an optional but non-limiting implementation manner, in response to receiving the active request of the user, the manner of sending prompt information to the user may, for example, be a pop-up window manner, and the prompt information can be presented in the form of text in the pop-up window. In addition, the pop-up window can also carry selection controls for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0242] It can be understood that the above notification and user authorization process is only illustrative and does not limit the implementation of the present disclosure, and other manners meeting the relevant laws and regulations can also be applied to the implementation of the present disclosure.

[0243] It can be understood that the data involved in the present technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the relevant laws and regulations and the relevant provisions.

[0244] It can be understood that in the specific embodiments of the present application, related data such as user information, location information, and navigation data are involved, and when the above embodiments of the present application are applied to specific products or technologies, user permission or consent needs to be obtained, and the collection, use, and processing of related data need to comply with relevant laws, regulations, and standards of countries and regions.

[0245] The apparatuses and units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an electronic mail device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0246] For the convenience of description, the above apparatuses are described in functions and are described respectively as various units. Of course, the functions of the units can be implemented in the same or multiple software and / or hardware in the implementation of the present application.

[0247] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, apparatuses, or units. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0248] The present application is described with reference to flowcharts and / or block diagrams of methods, devices, and apparatuses according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The functions specified in one flow or multiple flows and / or blocks

[0249] These computer program instructions can also be stored in a computer readable storage medium capable of guiding a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocksFigure 1 the function specified in the one or more blocks.

[0250] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable devices to generate a computer-implemented process, thus the instructions executed on the computer or other programmable devices provide a process for implementing the functions described in flowcharts Figure 1 the functions specified in the one or more blocks. Figure 1 the function specified in the one or more blocks.

[0251] It should also be noted that the terms "comprising", "comprises", "including", "includes" or any other variations thereof are intended to cover a non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise a list of elements do not include only those elements but can also include other elements not expressly listed or inherent to such processes, methods, articles, or apparatuses. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0252] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0253] The above only describes the embodiments of the present application and does not limit the present application. Those skilled in the art can make various modifications and changes to the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the scope of claims of the present application.

[0254] Although the embodiments of the present application are described in conjunction with the drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes shall fall within the scope defined by the appended claims.

Claims

1. A method for site selection and capacity determination of an electric vehicle charging station, characterized in that, The method comprises: obtaining GPS data of a target area, and generating a geographic data map of the target area according to the GPS data; According to the geographic data map, the charging stations in the target area are divided into multiple grids, for each charging station grid set obtained by each division, the rationality index of the charging station grid set is counted, and according to the rationality index obtained by counting, the actual grid set suitable for the target area is determined; wherein the rationality index is determined based on the charging compensation relationship between each charging station grid in the charging station grid set; the counting of the rationality index of the charging station grid set comprises: obtaining a plurality of preset statistical periods; for any statistical period, and for any charging station grid in the charging station grid set, the first quantity of charging stations having charging compensation relationship in the charging station grid is determined within the statistical period, and the charging compensation coefficient of the charging station grid is generated based on the first quantity obtained in each statistical period; the mean value of the charging compensation coefficients of each charging station grid in the charging station grid set is calculated, and the mean value is taken as the rationality index of the charging station grid set; Traverse each actual grid in the actual grid set, and for any actual grid, identify the total charging demand of the actual grid, determine the charging station quantity range according to the maximum service capacity and the minimum service capacity of the charging station in the actual grid and the total charging demand; Based on the combination of different charging station quantities in the charging station quantity range, a candidate site combination set corresponding to the actual grid is determined; wherein each candidate site combination represents the number of candidate sites. For any candidate site combination in the candidate site combination set, a target charging station site selection and capacity determination scheme is determined based on a pre-set charging station site selection and capacity determination model; wherein the charging station site selection and capacity determination model is constructed according to the charging pile type in each charging station, the number of different types of charging piles and the coverage rate of the charging station.

2. The method of claim 1, wherein, According to the maximum service capacity of the charging station in the actual grid and the total charging demand, the minimum charging station quantity is determined; According to the minimum service capacity of the charging station in the actual grid and the total charging demand, the maximum charging station quantity is determined; The range from the minimum charging station quantity to the maximum charging station quantity is determined as the charging station quantity range. For each charging station quantity in the charging station quantity range, the distance from each charging demand point to the cluster center in the actual grid is determined, and the error sum of squares is determined according to the distance; 3. The method of claim 1, wherein, The error sum of squares corresponding to different charging station quantities is drawn into a relationship diagram, and the candidate site combination set is determined from the relationship diagram. ​ ​ 4. The method of claim 1, wherein, The determining of the candidate station combination set corresponding to the actual grid based on the combinations of different charging station quantities in the charging station quantity range comprises: For each charging station quantity in the charging station quantity range, a first average distance from each charging demand point in the actual grid to a neighboring charging demand point in the same cluster is determined. A second average distance from each charging demand point in the actual grid to a charging demand point in the nearest cluster is determined. A single profile coefficient of each charging demand point is determined according to a ratio between the first average distance and the second average distance, and an overall profile coefficient matched with each charging station quantity is determined according to the single profile coefficient. The overall profile coefficient is screened to obtain the candidate station combination set.

5. The method of claim 1, wherein, The construction manner of the objective function in the charging station site selection and capacity determination model is as follows: For any candidate station combination and a corresponding candidate station position, a target construction total cost corresponding to the any candidate station combination is determined. A coverage range matched with the candidate station position is determined, and a target coverage rate corresponding to the any candidate station combination is determined based on the coverage range. The target coverage rate is converted into a target coverage loss cost by using a preset penalty cost coefficient. An addition of the target construction total cost and the target coverage loss cost is determined as the objective function.

6. The method of claim 5, wherein, The determination of the target construction total cost corresponding to the any candidate station combination comprises: For any candidate station combination and a corresponding candidate station position, a construction cost, a device cost, a device maintenance fee, and a power consumption cost matched with each candidate station position are determined. The construction cost, the device cost, the device maintenance fee, and the power consumption cost are weighted and summed according to a decision variable of whether to construct a charging station at the candidate station position to obtain a single construction total cost matched with each candidate station position. The target construction total cost corresponding to the any candidate station combination is determined according to all the single construction total costs.

7. The method of claim 5, wherein, The determination of the target coverage rate corresponding to the any candidate station combination comprises: A coverage range matched with the candidate station position is determined, and a target charging demand point in the coverage range is screened. A ratio between the target charging demand point and the total charging demand is determined as the target coverage rate corresponding to the any candidate station combination.

8. An electric vehicle charging station siting and sizing device, characterized by, The apparatus comprises: a geographic data acquisition unit configured to acquire GPS data of a target region and generate a geographic data map of the target region according to the GPS data; The grid set determination unit is configured to perform multiple grid divisions on the charging stations in the target region according to the geographic data map, to obtain a charging station grid set for each division, to count a rationality index of the charging station grid set, and to determine an actual grid set suitable for the target region according to the rationality indexes obtained by counting. The rationality index is determined based on the charging compensation relationship between the charging station grids in the charging station grid set. The counting of the rationality index of the charging station grid set includes: obtaining a plurality of preset statistical periods; for any statistical period and for any charging station grid in the charging station grid set, determining a first number of charging stations having a charging compensation relationship in the charging station grid in the statistical period, and generating a charging compensation coefficient of the charging station grid based on the first numbers obtained in each statistical period; calculating the mean value of the charging compensation coefficients of the charging station grids in the charging station grid set, and taking the mean value as the rationality index of the charging station grid set; The charging station number determination unit is configured to traverse each actual grid in the actual grid set, and to identify the total charging demand of any actual grid, to determine a charging station number range according to the maximum service capability and the minimum service capability of the charging stations in the actual grid and the total charging demand; The candidate site determination unit is configured to determine a candidate site combination set corresponding to the actual grid based on combinations of different charging station numbers in the charging station number range. Each candidate site combination represents the number of candidate sites. The site selection and capacity determination unit is configured to determine a target charging station site selection and capacity determination scheme for any candidate site combination in the candidate site combination set based on a pre-set charging station site selection and capacity model. The charging station site selection and capacity model is constructed according to the charging pile type in each charging station, the number of different types of charging piles, and the coverage rate of the charging station.

9. A computer device, comprising: The memory and the processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the method of any one of claims 1-7. The computer readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, ​

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