A site selection planning method and system for urban charging stations

Abstract

The present disclosure belongs to the technical field of charging station planning, and specifically relates to a site selection planning method and system for urban charging stations, comprising: constructing a city network model based on the structure of the urban transportation network; evaluating the importance of charging demand points based on the constructed network model and urban charging station address evaluation indicators; performing a preliminary selection of charging station addresses based on the evaluated importance of the charging demand points; and planning the preliminarily selected charging station addresses using an improved grey wolf algorithm with the goal of minimizing total investment, thereby obtaining a site selection planning scheme for urban charging stations.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of charging station planning, and in particular relates to a site selection and planning method and system for urban charging stations. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the rapid development of the economy, dependence on fossil fuels has become increasingly serious. In order to alleviate the contradiction between the scarcity of oil resources and demand, the automobile industry should transform and develop in a more environmentally friendly direction. The new energy vehicle industry has developed rapidly in recent years, and its share in the market has been rising steadily. The development of electric vehicles can alleviate the energy crisis.

[0004] In the development of the electric vehicle industry, the focus has always been on the development of electric vehicles, while the site selection of charging infrastructure has received relatively little attention, which is a major constraint on the development of electric vehicles. However, the availability of public charging facilities plays a crucial role in users' decision to purchase electric vehicles. Scientific site planning can not only motivate users to purchase electric vehicles, but also increase the enthusiasm of existing users to use charging stations and improve the utilization rate of charging stations after installation. Despite the maturity of electric vehicle technology, its development prospects are not optimistic. The main reason is not the high manufacturing cost, but the charging problems in many cities. In most places, the lack of well-planned charging infrastructure has hindered the popularization of electric vehicles. Therefore, it is necessary to plan the site selection of electric vehicles. In the construction of charging stations, experience is often based on subjective experience and lacks rigorous scientific principles.

[0005] The inventors have discovered that the site selection and planning of urban charging stations is a complex issue, constrained by many factors. Research on it must not only consider the investment cost of the charging station, but also factors such as the charging demand and traffic volume within the service range of the charging station. At present, the research on the site selection problem of urban electric vehicle charging stations is still in its initial stage, and there is no complete systematic theory. Most of the research on the site selection of urban charging stations is based on the selection of optimization objectives and the final site selection plan is obtained based on the heuristic algorithm; however, this site selection method requires the optimization algorithm to be calculated for each candidate address. The calculation process is time-consuming and it is very easy to fall into local optimality. Even if some site selection plans first pre-select candidate addresses, they are only selected based on experience and have no theoretical basis. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a site selection planning method and system for urban charging stations. Starting from the actual situation, according to the structure of the urban transportation network and the traffic flow information of each intersection, a site selection planning scheme for urban charging stations based on preliminary selection and then decision-making is designed to realize the site selection planning of urban charging stations.

[0007] According to some embodiments, a first solution of the present disclosure provides a method for site selection and planning of urban charging stations, which adopts the following technical solutions:

[0008] A method for site selection and planning of urban charging stations, comprising:

[0009] Based on the structure of the urban transportation network, build a city network model;

[0010] Based on the constructed network model and the evaluation indicators of urban charging station addresses, the importance of charging demand points is evaluated;

[0011] Conduct preliminary selection of charging station locations based on the assessed importance of charging demand points;

[0012] With the goal of minimizing the total investment, the improved grey wolf algorithm is used to plan the preliminary selection of charging station addresses, and the site selection planning scheme for urban charging stations is obtained.

[0013] As a further technical limitation, the network model of the city constructed is the complex network graph G of the city, that is, G = (N, E, W); where N represents the set of nodes in the complex network graph, N = {n i}, n i represents node i; E represents the set of edges in the complex network graph, E={e ij}, e ij represents the edge between node i and node j, i.e., the traffic road; W represents the set of edge weights in the complex network graph, W = {w ij},w ij Indicates e ij The weight of y ij Indicates e ij Traffic flow data of traffic roads, z ij Indicates e ij The length of the traffic road.

[0014] As a further technical limitation, the city charging station address evaluation index z i for Wherein, w1 and w2 are weight coefficients, and w1+w2=1; represents the normalized index value of node betweenness of node i, a i represents the node betweenness of node i, a min and amax Respectively represent the minimum and maximum values ​​of the node betweenness of node i; represents the normalized index value of the proximity centrality of node i, b i represents the proximity centrality of node i, b min and b max They represent the minimum and maximum values ​​of the proximity centrality of node i respectively; the importance of the charging demand point is evaluated by the size of the evaluation index of the urban charging station address obtained.

[0015] As a further technical limitation, in the process of preliminary selection of charging station addresses, the Percentile preliminary selection quantity P in statistics is used. x ,Right now Among them, L is the lower limit of the group segment where the desired percentile is located, i is the group interval of the group segment, and f x is the frequency within the group segment, n is the total frequency, F L is the cumulative frequency of the group segment where L is less than.

[0016] As a further technical limitation, the objective function constructed with the minimum total investment as the goal is minF = f inv +f om +f c , where F represents the annual cost of the charging station during the entire operating cycle; f inv represents the construction and operation cost of the charging station; f om represents the energy consumption cost of users driving electric vehicles back and forth between charging stations; f c Indicates the loss costs incurred within the charging station.

[0017] Furthermore, the constraints of the objective function include constraints for planning electric vehicle fast charging stations and constraints for power supply of special nodes in the network and power supply capacity of the power grid; the constraints for planning electric vehicle fast charging stations include the number of charging stations and the distance between charging stations, and the constraints for power supply of special nodes in the network and power supply capacity of the power grid include node voltage constraints and power balance constraints.

[0018] As a further technical limitation, the mathematical model of the improved grey wolf algorithm is

[0019]

[0020]

[0021] in, is the distance between the prey and the gray wolf; t is the number of iterations; and is the gray wolf position vector and the prey position; and is the coefficient vector, and is a random number in the range [0, 1], represents the control parameter, and Indicates that the gray wolf conducts a global search, Indicates that the gray wolf conducts a local search.

[0022] According to some embodiments, a second solution of the present disclosure provides a system for site selection and planning of urban charging stations, which adopts the following technical solutions:

[0023] A site selection and planning system for urban charging stations, comprising:

[0024] A construction module configured to construct a network model of the city based on the structure of the city's transportation network;

[0025] an evaluation module configured to evaluate the importance of charging demand points based on the constructed network model and the city charging station address evaluation index;

[0026] a preliminary selection module configured to perform preliminary selection of charging station addresses based on the assessed importance of the charging demand points;

[0027] The planning module is configured to use the improved grey wolf algorithm to plan the initial selection of charging station addresses with the goal of minimizing the total investment, and obtain a site selection planning scheme for urban charging stations.

[0028] According to some embodiments, a third solution of the present disclosure provides a computer-readable storage medium, which adopts the following technical solution:

[0029] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps in the method for site selection and planning of urban charging stations as described in the first aspect of the present disclosure.

[0030] According to some embodiments, a fourth solution of the present disclosure provides an electronic device, which adopts the following technical solution:

[0031] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for site selection and planning of urban charging stations as described in the first aspect of the present disclosure are implemented.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] In the preliminary selection layer, the present disclosure calculates the traffic flow of each node to obtain an evaluation index for evaluating the importance of the node, and selects the charging demand points that have a greater impact on the site selection, thereby reducing the amount of calculation in the decision-making layer, avoiding high-dimensional calculations in the subsequent intelligent algorithm calculation process, and improving the calculation efficiency;

[0034] At the decision-making level, an objective function with the goal of minimizing economic costs is given. By replacing the original linearly changing convergence factor in the algorithm with an exponentially changing factor, the Grey Wolf Algorithm is improved, which to a certain extent improves the global search capability of the algorithm and calculates the optimal solution, that is, the installation location of the optimal charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0036] Figure 1 This is a flow chart of the site selection and planning method for urban charging stations in the first embodiment of the present disclosure;

[0037] Figure 2 This is a block diagram of the overall solution of the site selection and planning method for urban charging stations in the first embodiment of the present disclosure;

[0038] Figure 3 It is a map of a certain area in the first embodiment of the present disclosure;

[0039] Figure 4 is a weighted complex network graph of a certain region in the first embodiment of the present disclosure;

[0040] Figure 5 is a schematic diagram of the social hierarchy of the gray wolf population in the first embodiment of the present disclosure;

[0041] Figure 6 This is a schematic diagram of the principle of searching for prey in the first embodiment of the present disclosure;

[0042] Figure 7 This is the traffic distribution map of the analysis area in the example of the first embodiment of the present disclosure;

[0043] Figure 8 is a schematic diagram of the traffic network model in the first embodiment of the present disclosure;

[0044] Figure 9 It is the most preferred site map of the traffic network model in the first embodiment of the present disclosure;

[0045] Figure 10 This is an actual rendering of the charging pile in the first embodiment of the present disclosure;

[0046] Figure 11This is a structural block diagram of the site selection and planning system for urban charging stations in the second embodiment of the present disclosure. DETAILED DESCRIPTION

[0047] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0048] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0050] In the absence of conflict, the embodiments of the present disclosure and the features thereof may be combined with each other.

[0051] Example 1

[0052] The first embodiment of the present disclosure introduces a method for site selection and planning of urban charging stations.

[0053] like Figure 1 A site selection and planning method for urban charging stations is shown, comprising:

[0054] Based on the structure of the urban transportation network, build a city network model;

[0055] Based on the constructed network model and the evaluation indicators of urban charging station addresses, the importance of charging demand points is evaluated;

[0056] Conduct preliminary selection of charging station locations based on the assessed importance of charging demand points;

[0057] With the goal of minimizing the total investment, the improved grey wolf algorithm is used to plan the preliminary selection of charging station addresses, and the site selection planning scheme for urban charging stations is obtained.

[0058] like Figure 2 As shown, this embodiment first constructs a complex network model of the city based on the complex network theory and the structure of the urban transportation network, and evaluates the importance of charging demand points based on node betweenness, proximity centrality and comprehensive indicators. Based on Percentile, the nodes with greater impact on site selection among the charging demand points are screened out and used as the preliminary nodes for charging station site selection.

[0059] Secondly, after the initial selection, the standard Grey Wolf Algorithm's tendency to fall into local optima was addressed by improving its convergence factor, thereby enhancing its global search capabilities. Furthermore, the improved Grey Wolf Algorithm was used to determine the optimal charging station site selection plan, taking charging station infrastructure investment, annual user loss costs, and annual economic loss costs within charging stations as targets, and the number of charging stations and inter-station distances as constraints.

[0060] Finally, based on the charging station site selection scheme given in this embodiment, a city was used as an example to verify the feasibility and correctness of the charging station site selection scheme given in this embodiment through simulation.

[0061] Preliminary site selection for charging stations in the urban transportation network

[0062] Simplifying the urban traffic network into a weighted complex network model, the city map is as follows Figure 3 , a weighted complex network graph such as Figure 4 shown.

[0063] The network model of the city is the complex network graph G of the city, that is, G = (N, E, W); where N represents the set of nodes in the complex network graph, N = {n i}, n i represents node i; E represents the set of edges in the complex network graph, E={e ij}, e ij represents the edge between node i and node j, i.e., the traffic road; W represents the set of edge weights in the complex network graph, W = {w ij},w ij Indicates e ij The weight of y ij Indicates e ij Traffic flow data of traffic roads, z ij Indicates e ij The length of the traffic road.

[0064] The evaluation index of the city charging station address z i for Wherein, w1 and w2 are weight coefficients, and w1+w2=1; represents the normalized index value of node betweenness of node i, a i represents the node betweenness of node i, a min and a max Respectively represent the minimum and maximum values ​​of the node betweenness of node i; represents the normalized index value of the proximity centrality of node i, b i represents the proximity centrality of node i, bmin and b max They represent the minimum and maximum values ​​of the proximity centrality of node i respectively; the importance of the charging demand point is evaluated by the size of the evaluation index of the urban charging station address obtained.

[0065] In the process of preliminary selection of charging station addresses, the Percentile preliminary selection quantity P in statistics is used. x ,Right now Among them, L is the lower limit of the group segment where the desired percentile is located, i is the group interval of the group segment, and f x is the frequency within the group segment, n is the total frequency, F L is the cumulative frequency of the group segment where L is less than.

[0066] Charging station site selection decision

[0067] After obtaining the charging demand points in the transportation network that have a greater impact on site selection in the preliminary selection layer, the decision-making layer is entered. The objective function is established with the minimum economic cost as the goal, and the optimal geographical location for the charging station is calculated through the improved grey wolf algorithm.

[0068] In the planning and construction of charging stations, not only factors such as construction cost and distribution cost should be considered, but also the user's charging cost, so as to obtain the objective function of charging station optimization planning. The improved gray wolf algorithm is used to calculate the objective function and obtain the optimal solution of the objective function. The objective function is:

[0069] minF=f inv +f om +f c

[0070] Where F represents the annual cost of the charging station during the entire operation cycle, f inv represents the construction and operation cost of the charging station; f om represents the energy consumption cost of users driving electric vehicles back and forth between charging stations; f c Indicates the loss costs incurred within the charging station.

[0071] The annual construction and operation costs of the charging station can be calculated using the following formula:

[0072]

[0073] Where, e i and a represent the number and unit price of transformers required for the construction of charging station i, respectively; c i represents the infrastructure cost of the charging station; m i and b represent the number of motors required by the charging station and the average price of motors respectively; r0 represents the discount rate of the charging station.

[0074] The user's annual loss cost is:

[0075] Where p represents the charging price of electric vehicles; ∑L icar and ∑L ibus They represent the comprehensive distance traveled by electric vehicles from users to charging stations within the service area of ​​the charging station; g car and g bus They represent the mileage that electric vehicles can travel per unit of electricity, among which,

[0076]

[0077]

[0078] Where N um Indicates the number of charging demand points in the charging station area; d ij represents the distance from the jth charging demand point to the charging station i; q jbus and q jbus They represent the number of electric vehicles and electric buses that go from the jth charging demand point to the charging station i every day.

[0079] The loss cost in the charging station is:

[0080] f c =e i (C Fe +C Ca )×T v ×365×p0×n+m i (C L +C D )×T v ×k i ×p0×n

[0081] Where C Fe and C Ca They represent the iron loss and copper loss of the transformer respectively; T v represents the charging time of a charging station per day; p0 represents the unit price of electricity purchased by the charging station; C L Indicates the line loss in the charging station; C D Represents the loss generated by the charging station itself; k i Indicates the simultaneous rate of the charger.

[0082] The constraints of electric vehicle fast charging station planning are considered, mainly including the number of charging stations and the distance between charging stations.

[0083] Constraint on the number of charging stations: N min ≤N≤N max Where, N min and N maxThey represent the minimum and maximum number of charging stations to be built in the region respectively.

[0084] Distance constraint between charging stations: D min ≤D ij ≤D max ,ij=1,2,…,N,i≠j; where D ij Indicates the distance between two charging stations; D min and represents D max Respectively represent the upper and lower limits of the distance allowed between two charging stations.

[0085] When planning the site for charging stations, it is necessary to consider whether the theoretically selected power grid can meet power demand, and the impact of installing charging stations on the power grid should not be too significant. This phenomenon is very common in actual transportation networks. It is necessary to consider the power supply of specific nodes in the network and the power supply capacity of the power grid, and incorporate constraints into the calculation of the plan. The constraints are as follows:

[0086] Node voltage constraint: U i,min ≤U i (t)≤U i,max ,

[0087] Where U i,min and U i,max are the upper and lower voltage limits of the node respectively.

[0088] Power balance constraints:

[0089] P i (t)≥P ch,i (t)+P L,i (t)

[0090] Q i (t)≥Q ch,i (t)+Q L,i (t)

[0091] Where, P i (t) represents the active power generated by the power plant; Q i (t) represents the reactive power generated by the power plant; P ch,i (t) is the original active power of the power plant; P L,i (t) is the active power of the node; Q ch,i (t) is the reactive power of the power plant's original electricity consumption; Q L,i (t) is the reactive power of the node.

[0092] The inspiration for the Gray Wolf Algorithm comes from the social hierarchy and hunting mechanism of the gray wolf population. Gray wolves are a group of carnivores at the top of the food chain and usually live in groups. The gray wolf population has a strict social class structure and can be divided into four social classes, such as Figure 5 As shown in the figure, during the calculation process, the individual gray wolf searches for prey by circling, approaching and tracking. The principle is as follows: Figure 6 shown.

[0093] The mathematical model of the improved grey wolf algorithm is expressed as:

[0094]

[0095]

[0096] Where, is the distance between the prey and the gray wolf; t is the number of iterations; and is the coefficient vector; and is the gray wolf position vector and the prey position. and The calculation formula is as follows:

[0097]

[0098]

[0099] Where, and Is a random number in the range [0, 1]. Generally, The control parameter takes values ​​in the range of [0, 2] and decreases linearly with the increase of the number of algorithm iterations. This means that the gray wolf conducts a global search. Indicates that the gray wolf conducts a local search.

[0100] By calculating the objective function value of the gray wolf optimization algorithm, the optimal solution, good solution, and suboptimal solution are set as α wolf, β wolf, and δ wolf. The positions of other gray wolves are jointly determined by the positions of α wolf, β wolf, and δ wolf. Specifically:

[0101]

[0102]

[0103]

[0104] After generating a new population, the elements in the population are controlled by boundary control to complete an iteration. The above process is repeated until the algorithm termination condition is met and the optimal solution is finally output.

[0105] The standard gray wolf algorithm has a small amount of computation and is easy to operate, but its disadvantages are: the initial search speed is too fast, which easily leads to local optimization. In the later stages, the convergence speed is slow and the search efficiency decreases. The standard gray wolf algorithm uses a linearly decreasing convergence factor to judge the progress of the algorithm during the calculation process. The search speed in the early and late stages of the calculation process is the same. Therefore, in order to alleviate the impact of this shortcoming on the gray wolf algorithm, the following improvement direction is proposed: replacing the convergence factor in the algorithm with a factor that changes exponentially can effectively reduce the search speed in the early stages and avoid local optimization, that is,

[0106] Solution Verification

[0107] The central area of ​​a county town was selected as the research object, and the site selection scheme given in this embodiment was used to simulate and verify the location of charging stations in this area. The area contains 35 intersections connected by 48 roads. Figure 7 The traffic flow data of the node is shown in Table 1.

[0108] Table 1 Traffic flow data of each demand point

[0109]

[0110] Based on complex network theory, each intersection on the road is regarded as a charging demand point, and the urban road is regarded as the line connecting the charging demand points. The traffic network model of the area is simplified as follows: Figure 8 As shown in Table 2, the node betweenness and proximity centrality of the charging demand points in the region are calculated.

[0111] Table 2 Betweenness and proximity centrality of each point

[0112]

[0113]

[0114] As can be seen from Table 2, when nodes 2, 11, 18, and 27 are evaluated using betweenness as the importance metric, their importance rankings differ significantly from those when using proximity centrality as the evaluation metric. Therefore, using a single metric as the standard for evaluating node importance is one-sided. To overcome the limitations of a single evaluation metric, a composite metric is obtained by normalizing and weighting both metrics to better assess node importance.

[0115] The comprehensive index is obtained from the node betweenness and proximity centrality, as shown in Table 3.

[0116] Table 3 Node comprehensive indicators

[0117]

[0118]

[0119] After obtaining comprehensive indicators and using the initial Percentile metric, when the threshold reaches 0.636, nodes 4, 17, 5, 20, 7, 18, 9, and 13 are considered key nodes. When the threshold reaches 0.435, nodes 22, 31, 23, 8, 14, 3, 32, 2, 11, and 21 are considered secondary key nodes. The total traffic volume of these 18 nodes accounts for 92% of the total traffic volume, achieving near-complete coverage.

[0120] After screening 18 charging demand points that have a greater impact on the selection, the total investment cost and constraints required to build a charging station were considered from multiple aspects, and an objective function was established with the minimum economic cost as the goal. The objective function was calculated using the improved grey wolf algorithm to obtain the optimal site selection location.

[0121] The project plans to build 10 to 18 charging stations, with an initial wolf pack of 100 wolves and a maximum number of iterations of 300. Software simulations are performed, with the locations of charging points represented by coordinates and road distances calculated at a 1:10,000 ratio based on actual traffic network distances. To compare the algorithms before and after optimization and evaluate their performance, the iteration graphs before and after optimization are first compared.

[0122] The objective function is used for optimization, where the values ​​of various indicator parameters in the function are shown in Table 4. The results obtained by selecting the number of charging stations are shown in Table 5.

[0123] Table 4 Parameter values ​​in the objective function

[0124]

[0125] Table 5 Results of different numbers of charging stations

[0126]

[0127] The number of installed charging stations is set to 13 to 20, and the calculation results are obtained after 30 independent calculations, as shown in Table 6.

[0128] Table 6 Results of different numbers of charging stations

[0129]

[0130] From Table 6, we can see that when the number of installed charging stations is 15, the difference between the average value and the optimal value is the smallest, which is the optimal number of locations. The installation coordinates of the 15 charging stations are shown in Table 7; the installation locations are shown in Figure 9 and Figure 10 shown.

[0131] Table 7 Optimal charging station coordinates

[0132]

[0133]

[0134] from Figure 9 and Figure 10 As can be seen from the figure, the location distribution of urban electric vehicle charging stations follows a certain pattern, with them primarily concentrated in industrial parks, prosperous commercial areas, schools, and residential areas. This is primarily because these areas have high population concentrations and heavy traffic, leading to higher user charging demand. This result demonstrates that the urban charging station site selection scheme proposed in this example is practical and feasible.

[0135] Based on complex network theory, this implementation simplifies the urban transportation network into a network model based on its topological structure, population density, and unique locations. The model then calculates the node betweenness and proximity centrality of each charging demand point to obtain a comprehensive index. Using the Pareto principle, the model then identifies preliminary nodes for charging stations within the transportation network, narrowing the scope and reducing the computational effort required for subsequent charging station site selection. The final site selection is then optimized using an improved Grey Wolf Algorithm, aiming to minimize total investment.

[0136] Example 2

[0137] A second embodiment of the present disclosure introduces a site selection and planning system for urban charging stations.

[0138] like Figure 11 The site selection and planning system for urban charging stations includes:

[0139] A construction module configured to construct a network model of the city based on the structure of the city's transportation network;

[0140] an evaluation module configured to evaluate the importance of charging demand points based on the constructed network model and the city charging station address evaluation index;

[0141] a preliminary selection module configured to perform preliminary selection of charging station addresses based on the assessed importance of the charging demand points;

[0142] The planning module is configured to use the improved grey wolf algorithm to plan the initial selection of charging station addresses with the goal of minimizing the total investment, and obtain a site selection planning scheme for urban charging stations.

[0143] The detailed steps are the same as those of the site selection and planning method for urban charging stations provided in Example 1 and will not be repeated here.

[0144] Example 3

[0145] A third embodiment of the present disclosure provides a computer-readable storage medium.

[0146] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the method for site selection and planning of urban charging stations as described in the first embodiment of the present disclosure.

[0147] The detailed steps are the same as those of the site selection and planning method for urban charging stations provided in Example 1 and will not be repeated here.

[0148] Example 4

[0149] A fourth embodiment of the present disclosure provides an electronic device.

[0150] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for site selection and planning of urban charging stations as described in the first embodiment of the present disclosure are implemented.

[0151] The detailed steps are the same as those of the site selection and planning method for urban charging stations provided in Example 1 and will not be repeated here.

[0152] The foregoing description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present disclosure shall be included within the scope of protection of the present disclosure.

[0153] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for site selection and planning of urban charging stations, characterized in that: include: Based on the structure of the urban transportation network, build a city network model; The network model of the city constructed is a complex network graph G of the city, that is, G = (N, E, W); wherein N represents the set of nodes in the complex network graph, N = {n i }, n i represents node i; E represents the set of edges in the complex network graph, E={e ij }, e ij represents the edge between node i and node j, i.e., the traffic road; W represents the set of edge weights in the complex network graph, W = {w ij },w ij Indicates e ij The weight of y ij Indicates e ij Traffic flow data of traffic roads, z ij Indicates e ij the length of the traffic roads; Based on the constructed network model and the evaluation indicators of urban charging station addresses, the importance of charging demand points is evaluated; The evaluation index of the city charging station address z i for Wherein, w1 and w2 are weight coefficients, and w1+w2=1; represents the normalized index value of node betweenness of node i, a i represents the node betweenness of node i, a min and a max Respectively represent the minimum and maximum values ​​of the node betweenness of node i; represents the normalized index value of the proximity centrality of node i, b i represents the proximity centrality of node i, b min and b max They represent the minimum and maximum values ​​of the proximity centrality of node i respectively; the importance of the charging demand point is evaluated by the size of the evaluation index of the city charging station address obtained; Conduct preliminary selection of charging station locations based on the assessed importance of charging demand points; With the goal of minimizing the total investment, the improved grey wolf algorithm is used to plan the preliminary selection of charging station addresses, and the site selection planning scheme for urban charging stations is obtained.

2. A method for site selection and planning of urban charging stations as claimed in claim 1, characterized in that: With the minimum total investment as the goal, the constructed objective function is minF=f inv +f om +f c , where F represents the annual cost of the charging station during the entire operating cycle; f inv represents the construction and operation cost of the charging station; f om represents the energy consumption cost of users driving electric vehicles back and forth between charging stations; f c Indicates the loss costs incurred within the charging station.

3. A method for site selection and planning of urban charging stations as claimed in claim 2, characterized in that: The constraints of the objective function include constraints for planning electric vehicle fast charging stations and constraints for considering the power supply of special nodes in the network and the power supply capacity of the power grid; the constraints for planning electric vehicle fast charging stations include the number of charging stations and the distance between charging stations, and the constraints for considering the power supply of special nodes in the network and the power supply capacity of the power grid include node voltage constraints and power balance constraints.

4. A method for site selection and planning of urban charging stations as claimed in claim 1, characterized in that: The mathematical model of the improved grey wolf algorithm is: in, is the distance between the prey and the gray wolf; t is the number of iterations; and is the gray wolf position vector and the prey position; and is the coefficient vector, and is a random number in the range [0, 1], represents the control parameter, and Indicates that the gray wolf conducts a global search, Indicates that the gray wolf conducts a local search.

5. A site selection and planning system for urban charging stations, characterized in that: include: The construction module is configured to construct a network model of the city based on the structure of the urban transportation network; the constructed network model of the city is a complex network graph G of the city, that is, G = (N, E, W); wherein N represents the set of nodes in the complex network graph, N = {n i }, n i represents node i; E represents the set of edges in the complex network graph, E={e ij }, e ij represents the edge between node i and node j, i.e., the traffic road; W represents the set of edge weights in the complex network graph, W = {w ij },w ij Indicates e ij The weight of y ij Indicates e ij Traffic flow data of traffic roads, z ij Indicates e ij the length of the traffic roads; The evaluation module is configured to evaluate the importance of charging demand points based on the constructed network model and the city charging station address evaluation index; the city charging station address evaluation index z i for Wherein, w1 and w2 are weight coefficients, and w1+w2=1; represents the normalized index value of node betweenness of node i, a i represents the node betweenness of node i, a min and a max Respectively represent the minimum and maximum values ​​of the node betweenness of node i; represents the normalized index value of the proximity centrality of node i, b i represents the proximity centrality of node i, b min and b max They represent the minimum and maximum values ​​of the proximity centrality of node i respectively; the importance of the charging demand point is evaluated by the size of the evaluation index of the city charging station address obtained; a preliminary selection module configured to perform preliminary selection of charging station addresses based on the assessed importance of the charging demand points; The planning module is configured to use the improved grey wolf algorithm to plan the initial selection of charging station addresses with the goal of minimizing the total investment, and obtain a site selection planning scheme for urban charging stations.

6. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for site selection and planning of urban charging stations as described in any one of claims 1 to 4 are implemented.

7. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for site selection and planning of urban charging stations according to any one of claims 1 to 4 are implemented.

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