Locating and sizing method and system for new energy bicycle charging station

Abstract

The invention discloses a locating and sizing method and system for a new energy bicycle charging station, and belongs to the technical field of power grid planning, and the method comprises the steps: carrying out the statistics of charging station data and new energy bicycle data of a village-in-city area, and carrying out the quantification of the fly line charging behavior of residents in the village-in-city area and the holding condition of new energy bicycles, constructing a plurality of charging behavior indexes; a traffic topological graph is obtained by constructing the road adjacency relation between the path nodes and the residential buildings and the charging stations; and mapping the charging behavior indexes and the charging station data into the traffic topological graph, determining a service coverage range of the charging station, and further determining a charging station addressing and sizing scheme of the village in the city. Therefore, by implementing the application, the problem that a charging station locating and sizing scheme capable of improving the residential electricity utilization safety and the electric energy quality cannot be obtained by effectively analyzing the charging behavior of the new energy bicycle in the village in the city and the use condition of the charging station in the prior art can be solved.

Description

Technical Field

[0001] The present application belongs to the technical field of power grid planning, and specifically relates to a site selection and capacity determination method and system for new energy bicycle charging stations. Background Art

[0002] With the rapid adoption of new energy bicycles in recent years, the planning and construction of charging infrastructure has become a key issue in urban development and power grid management. In densely populated areas such as urban villages, the number of new energy bicycles has skyrocketed, significantly increasing the demand for daily centralized charging. However, the number and distribution of existing community or street-level charging facilities (such as public charging piles and centralized charging stations) are insufficient to meet the high-frequency, decentralized charging needs of users, forcing users to resort to non-standard charging practices such as "flying wire charging" (connecting the charging cable directly to the indoor power source).

[0003] While "flying wire charging" can solve local power supply issues in the short term, it presents multiple safety and operational risks. For example, charging cables are susceptible to being stepped on or mechanically damaged. When the insulation of wires under high load ages, shorts, or poor contact occur, it can easily cause local overheating, even directly leading to fire accidents and posing fire risks. However, existing public literature and industry practices mostly focus on the macro-layout of electric vehicle charging networks, lacking in-depth research on the micro-behavior patterns of "small-power, large-scale" new energy vehicles. In particular, given the unique environment and electricity usage characteristics of urban village substations, there is no systematic and detailed analysis method. Therefore, how to integrate and analyze the geographic and behavioral data of new energy vehicles and their user groups in urban village substations to achieve efficient planning of charging facility layout and time-sharing charging strategies, and improve the overall electricity safety and power quality of urban village residents, is an urgent challenge. Summary of the Invention

[0004] This application proposes a site selection and sizing method and system for new energy bicycle charging stations, which can solve the problem in the existing technology that it is impossible to effectively analyze the charging behavior of new energy bicycles and the usage of charging stations in urban villages, and obtain a charging station site selection and sizing plan that can improve residents' electricity safety and power quality.

[0005] A first aspect of the present application provides a method for site selection and capacity determination for a new energy bicycle charging station, the method comprising:

[0006] Collect data on charging stations in urban villages and new energy bicycles in each residential building;

[0007] Based on the new energy bicycle data, the flying charging behavior and new energy bicycle ownership of residents in the urban village area are quantified, and several charging behavior indicators are constructed;

[0008] By constructing the road adjacency relationship between the preset path nodes and residential buildings and charging stations, the traffic topology map of the urban village area is obtained;

[0009] Mapping the charging behavior indicator and the charging station data to the traffic topology map, and determining the service coverage of the charging station by using a preset acceptable distance for charging service;

[0010] Determine the location and capacity plan of the charging station in the urban village area based on the service coverage and the charging behavior indicators.

[0011] This approach observes the charging behavior of new energy bicycles by urban village residents over a period of time and quantifies the observed data to derive charging behavior indicators related to fly-line charging and new energy bicycle ownership, providing data support for subsequent modeling. Then, through coordinate mapping and establishing geographic proximity, an accurate traffic topology map is constructed between residential buildings, roads, and charging stations. The shortest path approach accurately calculates the existing charging service distance and range, and collaboratively optimizes service accessibility and network connectivity. This leads to a charging station layout plan that can provide sufficient power for the current urban village scenario. This plan also provides reasonable capacity allocation for charging stations, mitigating the risk of overloading the area due to concentrated charging during localized periods.

[0012] In a possible implementation method of the first aspect, based on the new energy bicycle data, the flying wire charging behavior and new energy bicycle ownership of residents in the urban village area are quantified to construct several charging behavior indicators, specifically:

[0013] Extracting behavioral characteristic parameters related to residents' charging behavior from the new energy bicycle data; wherein the behavioral characteristic parameters include the number of new energy bicycles owned, the number of daily fly-line charging times, and the number of households;

[0014] Obtaining a new energy bicycle ownership rate based on the number of new energy bicycles owned and the number of households;

[0015] Obtaining a flying wire charging rate according to the daily flying wire charging times and the number of new energy bicycles owned;

[0016] By detecting the maximum value of the daily flying wire charging times within a data sampling period, a flying wire maximum load impact coefficient is obtained;

[0017] Combined with the maximum load impact coefficient of the flying wires of all residential buildings in the urban village substation area, the total flying wire impact coefficient of the substation area is obtained;

[0018] The charging behavior index is obtained based on the new energy bicycle ownership rate, the flying line charging rate, the flying line maximum load impact coefficient and the total flying line impact coefficient of the station area.

[0019] This approach dynamically extracts features from new energy bike ownership and over-the-air charging behavior. It incorporates charging behavior metrics such as new energy bike ownership rate, over-the-air charging rate, and over-the-air maximum load impact coefficient. This allows for quantitative expression and horizontal comparison of user charging behavior, providing a stable data foundation and logical support for subsequent charging behavior modeling and spatial analysis. Furthermore, the over-the-air maximum load impact coefficient can be used to indicate whether over-the-air charging behavior is concentrated or sudden. This serves as a crucial indicator for determining whether the short-term impact of charging behavior on electricity load is excessive, and is therefore crucial for subsequent planning of charging station capacity.

[0020] In a possible implementation method of the first aspect, the maximum load impact coefficient of the flying line and the total flying line impact coefficient of the station area are specifically:

[0021] The expression of the maximum load impact coefficient of the flying line is:

[0022]

[0023] Where, α i is the total flying line impact coefficient of the station area of ​​residential building i, T is the data sampling period, F i,t is the number of flying wire charges of residential building i on day t, and ε is the adjustment coefficient;

[0024] The expression of the total flying line impact coefficient of the station area is:

[0025]

[0026] Where, α zone is the total flying line impact coefficient of the substation area representing the urban village area, n is the total number of residential buildings, α i is the total flying line impact coefficient of the substation area of ​​residential building i.

[0027] In a possible implementation method of the first aspect, a traffic topology map of the urban village area is obtained by constructing a road adjacency relationship between preset path nodes and residential buildings and charging stations, specifically:

[0028] Select the two buildings with the farthest diagonal distance in the urban village area as the vertices of the map, project the vertices into the coordinate system, and construct the initial map;

[0029] Marking preset path nodes, residential building nodes, and charging station nodes in the urban village map to obtain the urban village map;

[0030] Traversing the path nodes in sequence in the urban village map, and building the road adjacency relationship by calculating the minimum distance between the path nodes and the residential building nodes and the charging station nodes;

[0031] The traffic topology map is constructed according to the road adjacency relationship.

[0032] The above solution marks the locations of path nodes, residential building nodes, and charging station nodes on the map, and more intuitively calculates the distance between residential buildings and charging stations based on the map, breaking through the limitations of traditional static distance estimation and intuitively demonstrating service accessibility.

[0033] In a possible implementation method of the first aspect, preset path nodes, residential building nodes, and charging station nodes are marked in an urban village map to obtain the urban village map, specifically:

[0034] Based on the relative position between the path node and the vertex, marking the path node in the urban village map;

[0035] Determine the coordinates of the residential building and the charging station within the urban village map based on the acquired latitude and longitude coordinates of the residential building and the relative positions of the latitude and longitude coordinates of the charging station and the vertex;

[0036] The coordinates are projected onto the urban village map to complete the marking of residential building nodes and charging station nodes.

[0037] In a possible implementation method of the first aspect, the charging behavior indicator and the charging station data are mapped to the traffic topology map, and the service coverage of the charging station is determined by a preset acceptable charging service distance, specifically:

[0038] Based on the actual needs of new energy bicycle users in urban villages, the acceptable distance of the charging service is set;

[0039] Based on the charging station data, the utilization rate of each charging station is marked on the traffic topology map, and based on the charging behavior indicator, the charging behavior of each residential building is marked on the traffic topology map;

[0040] Obtaining the road distance between the charging station and the residential building according to the marked traffic topology map;

[0041] The acceptable distance for charging service is compared with the road distance to determine the service coverage of each charging station in the urban village area.

[0042] This solution maps charging behavior metrics onto a traffic topology, providing a more intuitive picture of the rate of missed connections and the density of new energy bikes. Mapping charging station data onto the traffic topology demonstrates whether current charging stations are adequately meeting the needs of urban village residents. By comparing road distances with acceptable charging service distances, the map identifies charging station blind spots, providing data support for subsequent charging station site selection.

[0043] In a possible implementation method of the first aspect, the acceptable charging service distance is compared with the road distance to determine the service coverage range of each charging station in the urban village area, specifically:

[0044] If the road distance is less than or equal to the acceptable distance for charging service, the charging station is deemed to be able to provide services to the residential building, and the residential building is included in the serviceable user set of the charging station;

[0045] After comparing all the road distances, determining the number of serviceable users and the number of serviceable new energy bicycles for each charging station based on the set of serviceable users;

[0046] According to the number of serviceable users and the number of serviceable new energy bicycles, the residential buildings that can be served and those that cannot be served by the charging station are marked in the traffic topology map to obtain the service coverage.

[0047] In a possible implementation method of the first aspect, data on charging stations in urban villages and data on new energy bicycles in each residential building are collected, specifically as follows:

[0048] During a preset time period, the number of new energy bicycles owned, the number of daily fly-line charging times, the number of households, and the longitude and latitude coordinates of each residential building in the urban village area are collected;

[0049] During the preset time period, the number of charging piles, daily usage times and longitude and latitude coordinates of each charging station in the urban village area are collected.

[0050] The data obtained by the above scheme can be used to characterize the usage intensity and supply-demand matching degree of a charging station, and the obtained latitude and longitude coordinates provide the basis for subsequent spatial positioning.

[0051] In a second aspect, the present application provides a site selection and capacity determination system for new energy bicycle charging stations, the system comprising: a data acquisition module, a charging behavior indicator construction module, a map construction module, a coverage range determination module, and a site selection and capacity determination plan generation module;

[0052] The data collection module is used to collect data on charging stations in urban villages and new energy bicycle data for each residential building.

[0053] The charging behavior index construction module is used to quantify the flying wire charging behavior and new energy bicycle ownership of residents in the urban village area based on the new energy bicycle data, and construct a number of charging behavior indicators;

[0054] The map construction module is used to obtain the traffic topology map of the urban village area by constructing the road adjacency relationship between preset path nodes and residential buildings and charging stations;

[0055] The coverage range determination module is used to map the charging behavior indicator and the charging station data to the traffic topology map, and determine the service coverage range of the charging station according to the preset acceptable distance of the charging service;

[0056] The site selection and sizing plan generation module is used to determine the site selection and sizing plan for the charging station in the urban village area based on the service coverage and the charging behavior indicators.

[0057] The third aspect of the present application provides a terminal device, which includes: a terminal device including a processor and a memory, the memory storing a computer program, and the processor implementing the steps of a site selection and capacity determination method for a new energy bicycle charging station as described in any one of the embodiments of the present application when executing the computer program. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0059] Figure 1 This is a schematic diagram of a specific process of a method for site selection and capacity determination for a new energy bicycle charging station provided in one embodiment of the present application;

[0060] Figure 2 A new energy bicycle data diagram for a method for site selection and capacity determination of new energy bicycle charging stations provided in one embodiment of the present application;

[0061] Figure 3 A new energy bicycle distribution map of a residential building for a method for selecting and sizing a new energy bicycle charging station provided in one embodiment of the present application;

[0062] Figure 4 This is a flying wire charging distribution diagram of a residential building for a method of site selection and capacity determination for a new energy bicycle charging station provided in a certain embodiment of the present application;

[0063] Figure 5 This is a charging station usage change diagram for a method for site selection and capacity determination of new energy bicycle charging stations provided in one embodiment of the present application;

[0064] Figure 6 This is a charging station utilization efficiency diagram for a method for site selection and capacity determination of new energy bicycle charging stations provided in one embodiment of the present application;

[0065] Figure 7This is a diagram showing the distribution of the flying line charging load impact coefficient for a method for site selection and capacity determination of a new energy bicycle charging station provided in one embodiment of the present application;

[0066] Figure 8 This is a spatial distribution diagram of the flying line charging rate for a method for site selection and capacity determination of new energy bicycle charging stations provided in a certain embodiment of the present application;

[0067] Figure 9 This is a traffic topology diagram for a method of site selection and capacity determination for new energy bicycle charging stations provided in one embodiment of the present application;

[0068] Figure 10 This is a structural diagram of a site selection and capacity determination system for new energy bicycle charging stations provided in one embodiment of the present application;

[0069] Figure 11 A structural diagram of a terminal device is provided for a certain embodiment of the present application. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0071] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0072] First embodiment

[0073] Since the distribution of charging stations that provide electricity for new energy bicycles in urban villages is unreasonable and does not take into account the distribution of new energy bicycles, large-scale single-phase flying wire charging will cause obvious three-phase load asymmetry in the urban village power grid, which will in turn bring about a series of power grid quality problems such as system voltage fluctuations, harmonic pollution, and transformer overheating, affecting the overall power safety and power quality of residential areas. Therefore, based on the above problems, the embodiments of this application aim to accurately depict the spatial distribution of new energy bicycles in urban villages and the charging behavior of residents. By appropriately discretizing the geographic information of urban villages and surrounding areas, it is possible to intuitively represent the distribution of charging demand and build a reasonable charging network.

[0074] like Figure 1As shown, in order to solve the problem in the prior art that it is impossible to effectively analyze the charging behavior of new energy bicycles and the usage of charging stations in urban villages, and obtain a charging station site selection and sizing solution that can improve residents' electricity safety and power quality, the first embodiment of the present application provides a specific flow chart of a site selection and sizing method for new energy bicycle charging stations. The site selection and sizing method for new energy bicycle charging stations in this embodiment includes steps S1 to S5, which are detailed as follows:

[0075] Step S1, statistics the charging station data in the urban village area and the new energy bicycle data of each residential building.

[0076] The charging power of a new energy bicycle is generally between 100W and 500W, which is lower than that of electric vehicles. However, due to their large number, when they are charged simultaneously during local periods, it will still have an obvious "peak" effect on the load curve of the substation, posing a major challenge to the scheduling of the distribution network.

[0077] Therefore, in order to characterize the spatial distribution of new energy bicycles and the charging behavior of residents in an urban village, the embodiment of the present application first collects the charging status of new energy bicycles and the status of residents holding new energy bicycles within a period, and obtains the charging station data and new energy bicycle data within this period.

[0078] This embodiment of the application uses a 7-day cycle to collect data on new energy bicycles for each residential building and charging station data for each charging station. The new energy data includes the number of new energy bicycles in each residential building, the number of daily fly-line charging times, the number of residents, and the longitude and latitude coordinates; the charging station data includes the number of charging piles at each charging station, the number of daily usage times, and the longitude and latitude coordinates.

[0079] This new energy data allows for building-by-building analysis of new energy bike ownership and unlicensed charging behavior. Each building typically houses multiple households, and whether or not a household owns a new energy bike is probabilistically distributed. If charging stations in urban villages are located far away or have limited capacity, they cannot effectively meet users' daily charging needs. This leads users to prefer privately charging their bikes with "unlicensed" cables. Therefore, the unlicensed charging rate of new energy bikes can be used as an indicator of the unlicensed charging tendency of users within a residential building.

[0080] As a formal charging facility, the service capacity of a new energy bicycle charging station is determined by the number of charging piles configured. By analyzing the number of charging piles within a cycle, the number of daily uses and the number of new energy bicycles using the charging station, the usage intensity of the charging station and the degree of supply and demand matching can be characterized.

[0081] In addition, a theodolite is used to actually measure the latitude and longitude coordinates of each residential building and charging station, and the map coordinate information is combined for position matching to obtain complete spatial positioning data.

[0082] Step S2: Based on the new energy bicycle data, the flying wire charging behavior and new energy bicycle ownership of residents in the urban village area are quantified to construct several charging behavior indicators.

[0083] After obtaining the charging station data and the new energy bicycle data, the data was first cleaned and standardized, including filling in missing values, processing outliers, removing duplicates, unifying the data format, and converting data types. The data of different dimensions was then scaled to a uniform range and normalized to improve data quality.

[0084] After completing data preprocessing, behavioral characteristic parameters related to residents' charging behavior are extracted from the new energy bicycle data, including the number of new energy bicycles owned, the number of daily fly-line charging times, and the number of households (these data are all based on each residential building).

[0085] According to the number of new energy bicycles and the number of households, the new energy bicycle ownership rate is obtained, and the specific expression is:

[0086]

[0087] Where R v,i is the ownership rate of new energy bicycles in residential building i, N h,i is the number of households in residential building i, N v,i is the number of new energy bicycles owned by residential building i.

[0088] The new energy bicycle ownership rate is used to measure the popularity of new energy bicycles in a region and can be used as an important indicator to judge the regional new energy transportation penetration rate and charging demand base.

[0089] The flying wire charging rate is obtained according to the number of daily flying wire charging times and the number of new energy bicycles owned. The specific expression is:

[0090]

[0091] Where R f,i is the flying wire charging rate of residential building i, F i,t The number of daily flying wire charging times for residential building i, N v,i is the number of new energy bicycles in residential building i, T is the data collection period, and NAN represents the no-flying-wire charging rate when the number of new energy bicycles is 0.

[0092] The flying wire charging rate represents the proportion of new energy bicycle users who adopt flying wire charging due to limited infrastructure conditions, and reflects the flying wire risk level at the building level.

[0093] The number of new energy bicycles charging over the air per day in buildings with over-the-air charging is counted. The maximum value during the data collection period is divided by the average value to obtain the maximum load impact coefficient of over-the-air charging for the building. The specific expression is:

[0094]

[0095] Where, α i is the total flying line impact coefficient of the station area of ​​residential building i, T is the data collection period, F i,t is the number of flying wire charges for residential building i on day t, ε is the adjustment coefficient, and its value is very small to avoid the denominator being 0.

[0096] The maximum load impact coefficient of flying line charging is used to simulate the load level of the urban village power grid and the location and capacity determination of charging stations.

[0097] Then, the maximum load impact coefficient of the flying wires of all residential buildings in the urban village area is combined to obtain the total flying wire impact coefficient of the area. The specific expression is:

[0098]

[0099] Where, α zone is the total flying line impact coefficient of the substation area representing the urban village area, n is the total number of residential buildings, α i is the total flying line impact coefficient of the substation area of ​​residential building i.

[0100] The total flying line impact coefficient of the substation area represents the impact of the overall centralized flying line charging in the urban village substation area on the substation area power grid and can be used for load calculation.

[0101] In addition, the utilization rate of each charging station is calculated based on the charging station data. The specific expression is:

[0102]

[0103] Where U j is the utilization rate of charging station j, T is the data collection period, C j is the number of charging piles at charging station j, U j,t is the number of times charging station j is used per day.

[0104] The utilization rate of a charging station can indicate the balance between supply and demand for charging station facilities. A high utilization rate indicates high pressure on charging supply, while a low utilization rate may indicate problems such as wasted charging resources or misplaced charging station locations.

[0105] The charging behavior index is constructed based on the new energy bicycle ownership rate, flying line charging rate, flying line maximum load impact coefficient, total flying line impact coefficient of the substation and charging station utilization rate.

[0106] In step S3, a traffic topology map of the urban village area is obtained by constructing a road adjacency relationship between preset path nodes and residential buildings and charging stations.

[0107] Step S3 of the embodiment of the present application mainly generates a traffic topology map that represents the locations of residential buildings, charging stations and the distribution of new energy bicycles in the urban village, and intuitively displays the charging behavior of new energy bicycles and the usage of charging stations.

[0108] Consider the urban village area as a rectangular area, select the two buildings with the farthest diagonal distance in the urban village as the lower left corner and upper right corner of the map, and then project these two vertices into the plane rectangular coordinate system (0,0) ~ (L x ,L y ), L x is the horizontal length of the map, L y The vertical length of the map.

[0109] In the embodiment of the present application, L is set x =10000,L y =13000.

[0110] The coordinates of the path nodes on the map are then determined based on the relative positions between the preset path nodes and the lower left corner and upper right corner points, and the path nodes are marked on the map. The measured longitude and latitude coordinates of the residential buildings and charging stations are then compared and projected with the map vertices to determine the coordinates of the residential buildings and charging stations on the urban village map and mark them.

[0111] In the annotated urban village map, traverse each path node along the map road path. During the traversal process, calculate the minimum distance between the path node and the residential building node or charging station node to establish the corresponding road adjacency relationship. The spacing between each path node in the urban village map should not exceed 10m.

[0112] Specifically, the adjacency between path nodes is determined by the direction of the map road. Therefore, each path node is traversed sequentially along the direction of travel. For each path node, the distance between it and the surrounding charging station nodes or residential building nodes is calculated, and the path node with the smallest distance is selected to establish an adjacency relationship. Then, for all nodes with a road adjacency relationship, the Euclidean distance between the two points is calculated based on their coordinates.

[0113] For example, let the node set be V={BD i ,CS j ,RNk}, BD i For residential building nodes, CS j For charging station nodes, RN k is a path node. The edge set E contains all node pairs with road adjacency, and the weights of these node pairs are the Euclidean distances between the nodes.

[0114] The specific formula of the Euclidean distance is:

[0115]

[0116] Where, d uv is the Euclidean distance between node pairs, x u is the horizontal coordinate of node u, x v is the horizontal coordinate of node v, y u is the vertical coordinate of node u, y v is the vertical coordinate of node v.

[0117] Optionally, in other embodiments, the Floyd algorithm is also used to calculate the shortest path distance between two nodes that have a road adjacency relationship. If the two nodes do not have any road adjacency relationship, the distance between the two points is infinite.

[0118] Finally, based on the road adjacency relationship, a traffic topology map of the urban village area is constructed.

[0119] Step S4: Mapping the charging behavior indicator and the charging station data to the traffic topology map, and determining the service coverage of the charging station by using a preset acceptable distance for charging service.

[0120] Based on the actual needs of new energy bicycle users in the urban village area, the acceptable distance for charging service is set. The acceptable distance for charging service is the longest distance to the charging station that the residents of the urban village can actually accept. In this embodiment of the application, it is set to 3000 (in map units).

[0121] Based on the charging station data obtained above, the utilization rate of each charging station is marked on the traffic topology map, and based on the charging behavior indicators, the charging behavior of each residential building is marked on the traffic topology map, such as the new energy bicycle ownership rate, the flying line charging rate, and the flying line maximum load impact coefficient.

[0122] According to the marked traffic topology map, the road distance between the charging station and the residential building is obtained, and then the set acceptable distance for charging service is compared with the road distance.

[0123] If there is D BDi,CSj ≤d a , then CS j ∈CSwi , that is, charging station node CS j BD for residential building node i Serviceable sites, residential building BD i Will be counted into the charging station CS j The serviceable user set BDw j Among them, D BDi,CSj For charging station CS j BD for residential buildings i The distance between a Acceptable distance for charging service, CSw i BD for residential building node i Available charging station nodes.

[0124] This set of serviceable users provides a quantitative basis for the site selection and capacity configuration of charging stations in urban villages. If a charging station node does not currently have a serviceable site for a residential building node, the residential building node is considered a service blind spot, and a new charging station needs to be set up near the residential building node to provide charging services to residents.

[0125] Furthermore, after comparing all road distances, the number of serviceable users is defined as:

[0126]

[0127] Where, is the number of serviceable users of charging station j, BDw j is the serviceable user set of charging station j, BD i is the residential building node, N h,i is the number of households in residential building i.

[0128] Define the number of new energy bicycles that can be serviced:

[0129]

[0130] Where, is the number of new energy bicycles that can be served by charging station j, BDw j is the serviceable user set of charging station j, BD i is the residential building node, N v,i is the number of new energy bicycles owned by residential building i.

[0131] Then, based on the number of serviceable users and the number of serviceable new energy bicycles, the serviceable and unserviceable residential buildings of the charging station are marked in the traffic topology map to obtain the service coverage of each charging station.

[0132] Therefore, an embodiment of the present application provides a table representing the service availability of each charging station, as shown in Table 1 below.

[0133] Table 1 Statistics on the number of users that can be served by each charging station

[0134]

[0135] As can be seen from the table above, among the eight charging stations in the urban village area, charging stations numbered 1 to 5 can serve a larger number of users and have a wider service coverage area. Moreover, the larger the service coverage area, the more new energy bicycle owners the charging station needs to serve. If it cannot provide sufficient electricity to users, the risk of flying wire charging will be higher.

[0136] Step S5: Determine the location and capacity plan of the charging station in the urban village area based on the service coverage.

[0137] Therefore, based on the determined service coverage of each charging station, we can know whether the power capacity of the charging station can meet the needs of nearby users, whether there is a service blind spot in the urban village substation, where is the best location to set up the charging station in the service blind spot, and what capacity should be set to provide sufficient power for users, so as to obtain the charging station site selection and capacity determination plan for the urban village substation.

[0138] The implementation of the embodiments of the present application has the following beneficial effects:

[0139] The embodiment of the present application observes the charging behavior of new energy bicycles of urban village residents over a period of time and quantifies the observed data to obtain charging behavior indicators related to flying wire charging behavior and new energy bicycle ownership, providing data support for subsequent modeling. Then, through coordinate mapping and building geographic proximity relationships, an accurate traffic topology map between residential buildings, roads and charging stations is constructed, and the existing charging service distance and service range are accurately calculated in combination with the shortest path. The service accessibility and network connectivity are collaboratively optimized to obtain a charging station planning and layout plan that can provide sufficient electricity for the current urban village scenario, and provide a reasonable capacity configuration for the charging station, reducing the risk of excessive load in the substation area due to concentrated charging during local periods.

[0140] Second embodiment

[0141] Furthermore, in order to execute the site selection and sizing method for new energy bicycle charging stations corresponding to the above-mentioned method embodiment to achieve corresponding functions and technical effects, the present invention provides an embodiment of a site selection and sizing method for new energy bicycle charging stations in a typical urban village area in South China. For ease of explanation, only the parts related to this embodiment are shown.

[0142] In this example, the urban village area has 102 buildings and 1,812 households, with a total of 558 new energy bicycles, representing an overall new energy bicycle ownership rate of 30.79%. On average, 121 new energy bicycles in 70 buildings are charged with unauthorized charging every day, resulting in an overall unauthorized charging rate of 21.68% for new energy bicycles in the area.

[0143] Figure 2 The chart shows the statistics of new energy bicycle ownership and the number of fly-wire charging in the urban village area. The horizontal axis is the number of days, and the vertical axis is the number of new energy bicycles. The solid line shows the trend of the number of new energy bicycles over time, and the dotted line shows the trend of the number of fly-wire charging over time. Figure 2 It can be seen that there are more new energy bicycles in the urban village area on the 1st and 5th days, and the number of flying wire charging reaches a peak on the 5th day, indicating that the ownership of new energy bicycles is more stable than that of flying wire charging.

[0144] Figure 3 The figure shows the distribution of the number of new energy bicycles owned by each residential building. The horizontal axis is the number of units owned, and the vertical axis is the number of buildings. Figure 3 As shown, it can be seen that the majority of residential buildings have less than 5 new energy bicycles, about 60 buildings, and only a very small number of residential buildings have more than 15 new energy bicycles, indicating that only a very small number of residential buildings have a high charging load.

[0145] Figure 4 The figure shows the distribution of flying wire charging in each residential building. The horizontal axis is the number of flying wire charging stations, and the vertical axis is the number of buildings. Figure 4 As shown in the figure, it can be seen that most residential buildings have only 0 or 1 flying wire charging, and only a very small number of residential buildings have more than 4 flying wire charging. Figure 3 It can be concluded that when a residential building has a large number of new energy bicycles, the probability of flying wire charging is higher.

[0146] Figure 5 This chart shows the daily changes in the number of new energy bikes docked and charging at various charging stations within the urban village. The chart displays data from eight charging stations. The solid line represents the number of new energy bikes charging, while the dashed line represents the number of new energy bikes parked at the station. The horizontal axis represents the number of days, and the vertical axis represents the number of new energy bikes.

[0147] Figure 6The chart shows the capacity and usage of each charging station, with the station number on the horizontal axis and the capacity on the vertical axis. The chart shows that the maximum capacity used by new energy bicycles when charging is far from the actual capacity of the charging station. For example, at charging station number 3, which has a capacity of 12, the maximum capacity used by new energy bicycles when charging is only 3. This indicates that the charging stations in this urban village area are not fully utilized and their utilization efficiency is low.

[0148] Figure 7 The figure shows the distribution of the flying line charging load impact coefficient of residential buildings in the urban village substation. The horizontal axis in the figure is the flying line charging load impact coefficient (that is, the flying line peak coefficient in the figure), and the vertical axis is the number of buildings. It can be seen from the figure that the flying line charging load impact coefficient is basically concentrated between 2 and 3, and only a very small number of residential buildings have higher values. If the flying line charging load impact coefficient of a residential building is 2.67, it means that the maximum impact load that may be caused by the flying line charging of new energy bicycles in the residential building on the power system is 2.67 times its average load. If the charging power of a new energy bicycle is 200W, then for this substation, the load generated by flying line charging can be as high as 64.08kW in extreme cases.

[0149] Figure 8 This is a spatial distribution diagram of the charging rate of the over-the-air charging system, mapped onto a map. The horizontal axis represents the horizontal length of the map, and the vertical axis represents the vertical length. The squares in the diagram represent residential buildings, and the color of the squares corresponds to the over-the-air charging rate of that building; darker colors indicate higher rates. The stars in the diagram represent the locations of charging stations.

[0150] Figure 9 This is a traffic topology map for an urban village area. The horizontal axis represents the horizontal length of the map, and the vertical axis represents the vertical length. Smaller black dots represent path nodes, larger gray dots represent residential buildings with access to charging stations, square dots represent residential buildings without access to charging stations, and star dots represent charging station locations. This map shows the road adjacency relationships between nodes.

[0151] Third embodiment

[0152] Furthermore, in order to implement the site selection and capacity determination system for new energy bicycle charging stations corresponding to the above method embodiment to achieve corresponding functions and technical effects, Figure 10 A structural diagram of a site selection and capacity determination system for a new energy bicycle charging station is provided. For ease of explanation, only the parts related to this embodiment are shown. The site selection and capacity determination system for a new energy bicycle charging station provided in this embodiment of the application includes:

[0153] The data collection module 201 is used to collect data on charging stations in urban villages and data on new energy bicycles in units of each residential building.

[0154] In the embodiment of the present application, the charging power of a new energy bicycle is generally between 100W and 500W, which is lower than that of electric vehicles. However, due to their large number, when they are charged simultaneously during local periods, it will still produce an obvious "peak" effect on the load curve of the substation, posing a great challenge to the scheduling of the distribution network.

[0155] Therefore, in order to characterize the spatial distribution of new energy bicycles and the charging behavior of residents in an urban village, the embodiment of the present application first collects the charging status of new energy bicycles and the status of residents holding new energy bicycles within a period, and obtains the charging station data and new energy bicycle data within this period.

[0156] This embodiment of the application uses a 7-day cycle to collect data on new energy bicycles for each residential building and charging station data for each charging station. The new energy data includes the number of new energy bicycles in each residential building, the number of daily fly-line charging times, the number of residents, and the longitude and latitude coordinates; the charging station data includes the number of charging piles at each charging station, the number of daily usage times, and the longitude and latitude coordinates.

[0157] This new energy data allows for building-by-building analysis of new energy bike ownership and unlicensed charging behavior. Each building typically houses multiple households, and whether or not a household owns a new energy bike is probabilistically distributed. If charging stations in urban villages are located far away or have limited capacity, they cannot effectively meet users' daily charging needs. This leads users to prefer privately charging their bikes with "unlicensed" cables. Therefore, the unlicensed charging rate of new energy bikes can be used as an indicator of the unlicensed charging tendency of users within a residential building.

[0158] As a formal charging facility, the service capacity of a new energy bicycle charging station is determined by the number of charging piles configured. By analyzing the number of charging piles within a cycle, the number of daily uses and the number of new energy bicycles using the charging station, the usage intensity of the charging station and the degree of supply and demand matching can be characterized.

[0159] In addition, a theodolite is used to actually measure the latitude and longitude coordinates of each residential building and charging station, and the map coordinate information is combined for position matching to obtain complete spatial positioning data.

[0160] The charging behavior index construction module 202 is used to quantify the flying wire charging behavior and new energy bicycle ownership of residents in the urban village area based on the new energy bicycle data, and construct a number of charging behavior indicators.

[0161] Extracting behavioral characteristic parameters related to residents' charging behavior from the new energy bicycle data; wherein the behavioral characteristic parameters include the number of new energy bicycles owned, the number of daily fly-line charging times, and the number of households;

[0162] Obtaining a new energy bicycle ownership rate based on the number of new energy bicycles owned and the number of households;

[0163] Obtaining a flying wire charging rate according to the daily flying wire charging times and the number of new energy bicycles owned;

[0164] By detecting the maximum value of the daily flying wire charging times within a data sampling period, a flying wire maximum load impact coefficient is obtained;

[0165] Combined with the maximum load impact coefficient of the flying wires of all residential buildings in the urban village substation area, the total flying wire impact coefficient of the substation area is obtained;

[0166] The charging behavior index is obtained based on the new energy bicycle ownership rate, the flying line charging rate, the flying line maximum load impact coefficient and the total flying line impact coefficient of the station area.

[0167] The map construction module 203 is used to obtain a traffic topology map of the urban village area by constructing the road adjacency relationship between preset path nodes and residential buildings and charging stations.

[0168] Consider the urban village area as a rectangular area, select the two buildings with the farthest diagonal distance in the urban village as the lower left corner and upper right corner of the map, and then project these two vertices into the plane rectangular coordinate system (0,0) ~ (L x ,L y ), L x is the horizontal length of the map, L y The vertical length of the map.

[0169] In the embodiment of the present application, L is set x =10000,L y =13000.

[0170] The coordinates of the path nodes on the map are then determined based on the relative positions between the preset path nodes and the lower left corner and upper right corner points, and the path nodes are marked on the map. The measured longitude and latitude coordinates of the residential buildings and charging stations are then compared and projected with the map vertices to determine the coordinates of the residential buildings and charging stations on the urban village map and mark them.

[0171] In the annotated urban village map, traverse each path node along the map road path. During the traversal process, calculate the minimum distance between the path node and the residential building node or charging station node to establish the corresponding road adjacency relationship. The spacing between each path node in the urban village map should not exceed 10m.

[0172] Specifically, the adjacency between path nodes is determined by the direction of the map road. Therefore, each path node is traversed sequentially along the direction of travel. For each path node, the distance between it and the surrounding charging station nodes or residential building nodes is calculated, and the path node with the smallest distance is selected to establish an adjacency relationship. Then, for all nodes with a road adjacency relationship, the Euclidean distance between the two points is calculated based on their coordinates.

[0173] For example, let the node set be V={BD i ,CS j ,RN k}, BD i For residential building nodes, CS j For charging station nodes, RN k is a path node. The edge set E contains all node pairs with road adjacency, and the weights of these node pairs are the Euclidean distances between the nodes.

[0174] The specific formula of the Euclidean distance is:

[0175]

[0176] Where, d uv is the Euclidean distance between node pairs, x u is the horizontal coordinate of node u, x v is the horizontal coordinate of node v, y u is the vertical coordinate of node u, y v is the vertical coordinate of node v.

[0177] Optionally, in other embodiments, the Floyd algorithm is also used to calculate the shortest path distance between two nodes that have a road adjacency relationship. If the two nodes do not have any road adjacency relationship, the distance between the two points is infinite.

[0178] Finally, based on the road adjacency relationship, a traffic topology map of the urban village area is constructed.

[0179] The coverage range determination module 204 is configured to map the charging behavior indicator and the charging station data to the traffic topology map, and determine the service coverage range of the charging station according to a preset acceptable distance for charging service.

[0180] Based on the actual needs of new energy bicycle users in urban villages, the acceptable distance of the charging service is set;

[0181] Based on the charging station data, the utilization rate of each charging station is marked on the traffic topology map, and based on the charging behavior indicator, the charging behavior of each residential building is marked on the traffic topology map;

[0182] Obtaining the road distance between the charging station and the residential building according to the marked traffic topology map;

[0183] The acceptable distance for charging service is compared with the road distance to determine the service coverage of each charging station in the urban village area.

[0184] The site selection and sizing plan generating module 205 is used to determine the site selection and sizing plan for the charging station in the urban village area according to the service coverage.

[0185] Based on the determined service coverage of each charging station, we can know whether the power capacity of the charging station can meet the needs of nearby users, whether there is a service blind spot in the urban village substation, where is the best location to set up the charging station in the service blind spot, and what capacity should be set to provide sufficient power for users, so as to obtain the charging station site selection and capacity determination plan for the urban village substation.

[0186] In some embodiments, the charging behavior indicator building module 202 specifically includes:

[0187] After obtaining the charging station data and the new energy bicycle data, the data was first cleaned and standardized, including filling in missing values, processing outliers, removing duplicates, unifying the data format, and converting data types. The data of different dimensions was then scaled to a uniform range and normalized to improve data quality.

[0188] After completing data preprocessing, behavioral characteristic parameters related to residents' charging behavior are extracted from the new energy bicycle data, including the number of new energy bicycles owned, the number of daily fly-line charging times, and the number of households (these data are all based on each residential building).

[0189] According to the number of new energy bicycles and the number of households, the new energy bicycle ownership rate is obtained, and the specific expression is:

[0190]

[0191] Where R v,i is the ownership rate of new energy bicycles in residential building i, N h,i is the number of households in residential building i, N v,i is the number of new energy bicycles owned by residential building i.

[0192] The new energy bicycle ownership rate is used to measure the popularity of new energy bicycles in a region and can be used as an important indicator to judge the regional new energy transportation penetration rate and charging demand base.

[0193] The flying wire charging rate is obtained according to the number of daily flying wire charging times and the number of new energy bicycles owned. The specific expression is:

[0194]

[0195] Where R f,i is the flying wire charging rate of residential building i, F i,t The number of daily flying wire charging times for residential building i, N v,i is the number of new energy bicycles in residential building i, T is the data collection period, and NAN represents the no-flying-wire charging rate when the number of new energy bicycles is 0.

[0196] The flying wire charging rate represents the proportion of new energy bicycle users who adopt flying wire charging due to limited infrastructure conditions, and reflects the flying wire risk level at the building level.

[0197] The number of new energy bicycles charging over the air per day in buildings with over-the-air charging is counted. The maximum value during the data collection period is divided by the average value to obtain the maximum load impact coefficient of over-the-air charging for the building. The specific expression is:

[0198]

[0199] Where, α i is the total flying line impact coefficient of the station area of ​​residential building i, T is the data collection period, F i,t is the number of flying wire charges for residential building i on day t, ε is the adjustment coefficient, and its value is very small to avoid the denominator being 0.

[0200] The maximum load impact coefficient of flying line charging is used to simulate the load level of the urban village power grid and the location and capacity determination of charging stations.

[0201] Then, the maximum load impact coefficient of the flying wires of all residential buildings in the urban village area is combined to obtain the total flying wire impact coefficient of the area. The specific expression is:

[0202]

[0203] Where, α zone is the total flying line impact coefficient of the substation area representing the urban village area, n is the total number of residential buildings, α i is the total flying line impact coefficient of the substation area of ​​residential building i.

[0204] The total flying line impact coefficient of the substation area represents the impact of the overall centralized flying line charging in the urban village substation area on the substation area power grid and can be used for load calculation.

[0205] In addition, the utilization rate of each charging station is calculated based on the charging station data. The specific expression is:

[0206]

[0207] Where U jis the utilization rate of charging station j, T is the data collection period, C j is the number of charging piles at charging station j, U j,t is the number of times charging station j is used per day.

[0208] The utilization rate of a charging station can indicate the balance between supply and demand for charging station facilities. A high utilization rate indicates high pressure on charging supply, while a low utilization rate may indicate problems such as wasted charging resources or misplaced charging station locations.

[0209] The charging behavior index is constructed based on the new energy bicycle ownership rate, flying line charging rate, flying line maximum load impact coefficient, total flying line impact coefficient of the substation and charging station utilization rate.

[0210] In some embodiments, the coverage range determination module 204 is specifically:

[0211] Based on the actual needs of new energy bicycle users in the urban village area, the acceptable distance for charging service is set. The acceptable distance for charging service is the longest distance to the charging station that the residents of the urban village can actually accept. In this embodiment of the application, it is set to 3000 (in map units).

[0212] Based on the charging station data obtained above, the utilization rate of each charging station is marked on the traffic topology map, and based on the charging behavior indicators, the charging behavior of each residential building is marked on the traffic topology map, such as the new energy bicycle ownership rate, the flying line charging rate, and the flying line maximum load impact coefficient.

[0213] According to the marked traffic topology map, the road distance between the charging station and the residential building is obtained, and then the set acceptable distance for charging service is compared with the road distance.

[0214] If there is D BDi,CSj ≤d a , then CS j ∈CSw i , that is, charging station node CS j BD for residential building node i Serviceable sites, residential building BD i Will be counted into the charging station CS j The serviceable user set BDw j Among them, D BDi,CSj For charging station CS j BD for residential buildings i The distance between a Acceptable distance for charging service, CSw i BD for residential building node i Available charging station nodes.

[0215] This set of serviceable users provides a quantitative basis for the site selection and capacity configuration of charging stations in urban villages. If a charging station node does not currently have a serviceable site for a residential building node, the residential building node is considered a service blind spot, and a new charging station needs to be set up near the residential building node to provide charging services to residents.

[0216] Furthermore, after comparing all road distances, the number of serviceable users is defined as:

[0217]

[0218] Where, is the number of serviceable users of charging station j, BDw j is the serviceable user set of charging station j, BD i is the residential building node, N h,i is the number of households in residential building i.

[0219] Define the number of new energy bicycles that can be serviced:

[0220]

[0221] Where, is the number of new energy bicycles that can be served by charging station j, BDw j is the serviceable user set of charging station j, BD i is the residential building node, N v,i is the number of new energy bicycles owned by residential building i.

[0222] Then, based on the number of serviceable users and the number of serviceable new energy bicycles, the serviceable and unserviceable residential buildings of the charging station are marked in the traffic topology map to obtain the service coverage of each charging station.

[0223] The implementation of the embodiments of the present application has the following beneficial effects:

[0224] The embodiment of the present application observes the charging behavior of new energy bicycles of urban village residents over a period of time and quantifies the observed data to obtain charging behavior indicators related to flying wire charging behavior and new energy bicycle ownership, providing data support for subsequent modeling. Then, through coordinate mapping and building geographic proximity relationships, an accurate traffic topology map between residential buildings, roads and charging stations is constructed, and the existing charging service distance and service range are accurately calculated in combination with the shortest path. The service accessibility and network connectivity are collaboratively optimized to obtain a charging station planning and layout plan that can provide sufficient electricity for the current urban village scenario, and provide a reasonable capacity configuration for the charging station, reducing the risk of excessive load in the substation area due to concentrated charging during local periods.

[0225] Further, Figure 11This is a structural diagram of a terminal device provided in one embodiment of the present application. Figure 11 As shown, the terminal device 3 of this embodiment includes: at least one processor 30 (in Figure 11 Only one is shown) and a memory 31 and a computer program 32 stored in the memory 31 and executable on the at least one processor. When the processor 30 executes the computer program 32, the steps of a site selection and sizing method for a new energy bicycle charging station as described in any one of the embodiments of the present application can be implemented.

[0226] The terminal device 3 may be a computing device such as a desktop computer, a cloud server, or a laptop computer. The computing device may include but is not limited to a processor 30 and a memory 31 . Figure 11 This is merely an example of the terminal device 3 and does not constitute a limitation on the terminal device 3 , which may include more or fewer components than those shown in the figure.

[0227] The specific embodiments described above further illustrate the purpose, technical solutions, and beneficial effects of this application. It should be understood that the above description is merely a specific embodiment of this application and is not intended to limit the scope of protection of this application. In particular, it should be noted that for those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. A method for site selection and capacity determination of new energy bicycle charging stations, characterized in that: include: Collect data on charging stations in urban villages and new energy bicycles in each residential building; Based on the new energy bicycle data, the flying charging behavior and new energy bicycle ownership of residents in the urban village area are quantified, and several charging behavior indicators are constructed; By constructing the road adjacency relationship between the preset path nodes and residential buildings and charging stations, the traffic topology map of the urban village area is obtained; Mapping the charging behavior indicator and the charging station data to the traffic topology map, and determining the service coverage of the charging station by using a preset acceptable distance for charging service; Determine the location and capacity plan of the charging station in the urban village area based on the service coverage and the charging behavior indicators.

2. The method for site selection and capacity determination of a new energy bicycle charging station according to claim 1, characterized in that: Based on the new energy bicycle data, the flying wire charging behavior and new energy bicycle ownership of residents in the urban village area are quantified, and several charging behavior indicators are constructed, specifically: Extracting behavioral characteristic parameters related to residents' charging behavior from the new energy bicycle data; wherein the behavioral characteristic parameters include the number of new energy bicycles owned, the number of daily fly-line charging times, and the number of households; Obtaining a new energy bicycle ownership rate based on the number of new energy bicycles owned and the number of households; Obtaining a flying wire charging rate according to the daily flying wire charging times and the number of new energy bicycles owned; By detecting the maximum value of the daily flying wire charging times within a data sampling period, a flying wire maximum load impact coefficient is obtained; Combined with the maximum load impact coefficient of the flying wires of all residential buildings in the urban village substation area, the total flying wire impact coefficient of the substation area is obtained; The charging behavior index is obtained based on the new energy bicycle ownership rate, the flying line charging rate, the flying line maximum load impact coefficient and the total flying line impact coefficient of the station area.

3. The method for site selection and capacity determination of a new energy bicycle charging station according to claim 2, characterized in that: The maximum load impact coefficient of the flying line and the total flying line impact coefficient of the station area are specifically: The expression of the maximum load impact coefficient of the flying line is: Where, α i is the total flying line impact coefficient of the station area of ​​residential building i, T is the data sampling period, F i,t is the number of flying wire charges of residential building i on day t, and ε is the adjustment coefficient; The expression of the total flying line impact coefficient of the station area is: Where, α zone is the total flying line impact coefficient of the substation area representing the urban village area, n is the total number of residential buildings, α i is the total flying line impact coefficient of the substation area of ​​residential building i.

4. The method for site selection and capacity determination of a new energy bicycle charging station according to claim 1, characterized in that: By constructing the road adjacency relationship between the preset path nodes and the residential buildings and charging stations, the traffic topology map of the urban village area is obtained, specifically: Select the two buildings with the farthest diagonal distance in the urban village area as the vertices of the map, project the vertices into the coordinate system, and construct the initial map; Marking preset path nodes, residential building nodes, and charging station nodes in the urban village map to obtain the urban village map; Traversing the path nodes in sequence in the urban village map, and building the road adjacency relationship by calculating the minimum distance between the path nodes and the residential building nodes and the charging station nodes; The traffic topology map is constructed according to the road adjacency relationship.

5. The method for site selection and capacity determination of a new energy bicycle charging station according to claim 4 is characterized in that: The preset path nodes, residential building nodes and charging station nodes are marked in the urban village map to obtain the urban village map, specifically: Based on the relative position between the path node and the vertex, marking the path node in the urban village map; Determine the coordinates of the residential building and the charging station within the urban village map based on the acquired latitude and longitude coordinates of the residential building and the relative positions of the latitude and longitude coordinates of the charging station and the vertex; The coordinates are projected onto the urban village map to complete the marking of residential building nodes and charging station nodes.

6. The method for site selection and capacity determination of a new energy bicycle charging station according to claim 1, characterized in that: The charging behavior indicator and the charging station data are mapped to the traffic topology map, and the service coverage of the charging station is determined by a preset acceptable distance for charging service, specifically: Based on the actual needs of new energy bicycle users in urban villages, the acceptable distance of the charging service is set; Based on the charging station data, the utilization rate of each charging station is marked on the traffic topology map, and based on the charging behavior indicator, the charging behavior of each residential building is marked on the traffic topology map; Obtaining the road distance between the charging station and the residential building according to the marked traffic topology map; The acceptable distance for charging service is compared with the road distance to determine the service coverage of each charging station in the urban village area.

7. The method for site selection and capacity determination of a new energy bicycle charging station according to claim 6, characterized in that: The comparison of the acceptable charging service distance with the road distance to determine the service coverage of each charging station in the urban village area is specifically as follows: If the road distance is less than or equal to the acceptable distance for charging service, the charging station is deemed to be able to provide services to the residential building, and the residential building is included in the serviceable user set of the charging station; After comparing all the road distances, determining the number of serviceable users and the number of serviceable new energy bicycles for each charging station based on the set of serviceable users; According to the number of serviceable users and the number of serviceable new energy bicycles, the residential buildings that can be served and those that cannot be served by the charging station are marked in the traffic topology map to obtain the service coverage.

8. The method for site selection and capacity determination of a new energy bicycle charging station according to claim 1, characterized in that: The statistics of charging station data in urban villages and new energy bicycle data per residential building are as follows: During a preset time period, the number of new energy bicycles owned, the number of daily fly-line charging times, the number of households, and the longitude and latitude coordinates of each residential building in the urban village area are collected; During the preset time period, the number of charging piles, daily usage times and longitude and latitude coordinates of each charging station in the urban village area are collected.

9. A site selection and capacity determination system for new energy bicycle charging stations, characterized in that: include: Data collection module, charging behavior indicator construction module, map construction module, coverage determination module and site selection and capacity solution generation module; The data collection module is used to collect data on charging stations in urban villages and new energy bicycle data for each residential building. The charging behavior index construction module is used to quantify the flying wire charging behavior and new energy bicycle ownership of residents in the urban village area based on the new energy bicycle data, and construct a number of charging behavior indicators; The map construction module is used to obtain the traffic topology map of the urban village area by constructing the road adjacency relationship between preset path nodes and residential buildings and charging stations; The coverage range determination module is used to map the charging behavior indicator and the charging station data to the traffic topology map, and determine the service coverage range of the charging station according to the preset acceptable distance of the charging service; The site selection and sizing plan generation module is used to determine the site selection and sizing plan for the charging station in the urban village area based on the service coverage and the charging behavior indicators.

10. A terminal device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the method implements the steps of a site selection and capacity determination method for a new energy bicycle charging station as described in any one of claims 1 to 8.

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