A charging pile layout optimization method, system and device

Abstract

The application provides a charging pile layout optimization method, system and equipment, relates to the technical field of charging facility management, and comprises the following steps: acquiring historical traffic flow and power grid node capacity, generating a charging pile coordinate set and determining involved road sections; wherein, the involved road sections are the necessary road sections of the navigation path within the influence range of each charging pile; potential congestion road sections in each period are identified by using a traffic simulation model; if there is congestion, the total number of stranded vehicles is calculated, a time-varying charging load curve is generated in combination with the electric vehicle proportion and the single-vehicle fast-charging power; the curve is mapped to the power grid node, whether the node voltage is lower than a threshold value is evaluated, and the pile-to-be-supplemented road section is confirmed; a candidate coordinate set is generated around the pile-to-be-supplemented road section, the supplementary charging pile position coordinates are screened out and are incorporated into the original coordinate set, and a complete layout scheme is formed. The application realizes the optimization of the charging pile layout, improves the utilization rate of the charging pile, and ensures the stable operation of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging facility management, in particular to a charging pile layout optimization method, system and device. BACKGROUND

[0002] At present, due to the wide use of electric vehicles, the corresponding charging piles also need to be reasonably configured. Before setting the charging piles, the layout of the charging piles in the target area needs to be planned. Usually, the position coordinates and power level of the charging piles are determined according to the topological relationship of the road network and the historical vehicle density, so as to form a layout scheme of the charging piles.

[0003] In the related art, when the charging vehicle flow stays for a long time on the road, the vehicle density is easily added and concentrated in the same space-time window, thereby causing the fast charging demand of the vehicle to be also synchronized and compressed to the same space-time window, so that the vehicle charging demand is clustered and added in space-time, the phenomenon of superimposition of large-power fast charging load is caused, the local power distribution node voltage drops rapidly, the charging pile is forced to reduce power until shutdown, so that the user cannot obtain the rated power even if he reaches the charging pile, which not only reduces the user experience, but also causes waste of power grid resources. SUMMARY

[0004] The problem solved by the present application is how to improve the utilization rate of charging piles.

[0005] To solve the above problems, the present application provides a charging pile layout optimization method, system and device.

[0006] In a first aspect, a charging pile layout optimization method of the present application comprises:

[0007] obtaining the historical traffic flow and the power grid node capacity of a target area, and generating a coordinate set of charging piles in the target area according to the historical traffic flow and the power grid node capacity;

[0008] determining a plurality of involved road segments according to the coordinate set of the charging piles; wherein the involved road segment is a navigation path required road segment within the influence range of each charging pile;

[0009] determining whether there is a potential congestion road segment in each preset time period for all the involved road segments of each charging pile through a traffic simulation model;

[0010] if the involved road segment has the potential congestion road segment, determining the total number of vehicles staying in the potential congestion road segment in the corresponding preset time period according to the road segment length of the potential congestion road segment, and determining the time-varying charging load curve of the potential congestion road segment corresponding to the charging pile in combination with a preset electric vehicle proportion coefficient and a single vehicle fast charging power;

[0011] mapping the time-varying charging load curve to a power grid node corresponding to the charging pile to obtain a node voltage of the power grid node, and determining that the potential congestion road section is a pile-to-be-supplemented road section if the node voltage of the power grid node is lower than a preset voltage threshold;

[0012] generating a candidate coordinate set centered on the pile-to-be-supplemented road section, and screening position coordinates in the candidate coordinate set to determine a position coordinate of a supplementary charging pile;

[0013] incorporating the position coordinate of the supplementary charging pile into the coordinate set to obtain a charging pile layout scheme of the target region.

[0014] Optionally, the historical traffic flow and the power grid node capacity of the target region are obtained, and a charging pile coordinate set in the target region is generated according to the historical traffic flow and the power grid node capacity, including:

[0015] dividing the target region into grids, and determining a vehicle flow in each grid according to the historical traffic flow and a preset time period;

[0016] determining a net vehicle density of each grid in each time period according to the vehicle flow of each grid;

[0017] obtaining a node capacity of all the power grid nodes in the target region;

[0018] determining a position coordinate of a charging pile and a mapping relationship between the charging pile and a power grid node corresponding to the charging pile according to the node capacity of the power grid node and the net vehicle density by a clustering-siting algorithm;

[0019] obtaining the coordinate set according to the mapping relationship and the position coordinate.

[0020] Optionally, the coordinate set of the charging pile is used to determine a plurality of involved road sections, including:

[0021] obtaining a navigation path connected to each charging pile;

[0022] defining an influence range centered on the position coordinate of each charging pile;

[0023] screening the navigation path according to the influence range to obtain a navigation path must-pass road section of each charging pile, and taking the navigation path must-pass road section as the involved road section.

[0024] Optionally, the traffic simulation model is used to determine whether there is a potential congestion road section in each preset time period for all the involved road sections of each charging pile, including:

[0025] inputting each of the historical vehicle speeds and the historical traffic flows of the involved road sections into the traffic simulation model, simulating by the traffic simulation model, and outputting an average vehicle speed of each of the involved road sections in each of the preset time periods;

[0026] judging whether each of the involved road sections of the charging pile has the potential congestion road section according to the average vehicle speed;

[0027] determining an electric vehicle flow in each of the involved road sections according to a preset electric vehicle proportion coefficient;

[0028] If the average vehicle speed of any of the involved road sections in the preset time period is lower than a preset congestion vehicle speed, the time delay of a vehicle of the involved road section to the charging pile exceeds a preset time threshold, and the electric vehicle flow exceeds a preset flow threshold, it is determined that the involved road section is a potential congestion road section in the preset time period.

[0029] Optionally, if the involved road section has the potential congestion road section, a total number of stranded vehicles of the potential congestion road section in the corresponding preset time period is determined according to a road section length of the potential congestion road section, and a time-varying charging load curve of the potential congestion road section corresponding to the charging pile is determined in combination with a preset electric vehicle proportion coefficient and a single vehicle fast charging power, including:

[0030] The total number of stranded vehicles of the potential congestion road section in the corresponding preset time period is determined according to the average vehicle speed of the vehicle of the potential congestion road section and the road section length;

[0031] The number of electric vehicles is screened out from the total number of stranded vehicles according to the preset electric vehicle proportion coefficient;

[0032] The peak charging demand of the potential congestion road section in the corresponding preset time period is obtained by multiplying the number of electric vehicles and the single vehicle fast charging power;

[0033] The time-varying charging load curve of the charging pile in the preset time period is generated according to the peak charging demand of the potential congestion road section in the corresponding preset time period.

[0034] Optionally, the mapping of the time-varying charging load curve to the power grid node corresponding to the charging pile obtains a node voltage of the power grid node, including:

[0035] The time-varying charging load curve in the preset time period is superimposed on the power grid node corresponding to the charging pile according to the mapping relationship between the charging pile and the power grid node corresponding to the charging pile, to form a time period load of the power grid node.

[0036] Based on the time-period load of the power grid node, power flow calculation is performed on the power grid node to obtain the node voltage of the power grid node within the preset time period.

[0037] Optionally, the step of generating a candidate coordinate set centered on the road section to be supplemented with charging piles, and filtering the position coordinates in the candidate coordinate set to determine the position coordinates of the supplementary charging piles, includes:

[0038] A predetermined radius range is extended outward from the geometric center of the road section to be supplemented with charging piles to form a candidate area for the supplementary charging piles;

[0039] Within the candidate region, multiple candidate coordinate points are generated according to a preset step size;

[0040] Obtain the remaining capacity of the power grid node corresponding to each candidate coordinate point, and perform preliminary screening of the candidate coordinate points based on the remaining capacity to obtain the remaining candidate coordinate points;

[0041] The remaining candidate coordinates are sorted by a multi-objective screening mechanism based on the minimum distance from the remaining candidate coordinates to the road section to be supplemented, the distance between the remaining candidate coordinates and other charging piles within a preset range, and the remaining capacity margin of the power grid node.

[0042] The location coordinates of the remaining candidate coordinate points are selected from the sorting results as the location coordinates of the supplementary charging pile.

[0043] Optionally, incorporating the location coordinates of the supplementary charging piles into the coordinate set to obtain a charging pile layout scheme for the target area includes:

[0044] Add the location coordinates of the supplementary charging station to the coordinate set;

[0045] The mapping relationship between the charging piles and the power grid nodes in the coordinate set is updated according to the mapping relationship between the supplementary charging piles and the power grid nodes to obtain the charging pile layout scheme of the target area.

[0046] Secondly, the present invention provides a charging pile layout optimization system, comprising:

[0047] The data acquisition module is used to acquire historical traffic flow and power grid node capacity of the target area, and generate a set of coordinates of charging piles in the target area based on the historical traffic flow and power grid node capacity.

[0048] The road segment determination module is used to determine multiple involved road segments based on the coordinate set of the charging pile; wherein, the involved road segments are the necessary road segments of the navigation path of each charging pile within the influence range;

[0049] The congestion assessment module is used to determine, through a traffic simulation model, whether there are potential congested road sections in all the road segments involved for each charging station within each preset time period.

[0050] The load calculation module is used to determine the total number of vehicles stranded in the potential congested road segment within the corresponding preset time period based on the road segment length of the potential congested road segment, and to determine the time-varying charging load curve of the charging pile corresponding to the potential congested road segment by combining the preset electric vehicle ratio coefficient and the fast charging power of a single vehicle.

[0051] The voltage assessment module is used to map the time-varying charging load curve to the power grid node corresponding to the charging pile, and obtain the node voltage of the power grid node. If the node voltage of the power grid node is lower than a preset voltage threshold, the potentially congested road section is determined to be a road section that needs to be recharged.

[0052] The charging pile replenishment planning module is used to generate a candidate coordinate set centered on the road section to be replenished, and to filter the location coordinates in the candidate coordinate set to determine the location coordinates of the replenished charging piles.

[0053] The scheme generation module is used to incorporate the location coordinates of the supplementary charging piles into the coordinate set to obtain the charging pile layout scheme for the target area.

[0054] Thirdly, an electronic device according to the present invention includes a memory and a processor;

[0055] The memory is used to store computer programs;

[0056] The processor is used to implement the charging pile layout optimization method as described above when executing the computer program.

[0057] The charging pile layout optimization method, system, and electronic equipment of this invention obtain historical traffic flow and power grid node capacity of the target area, and generate a coordinate set of charging piles accordingly. This provides key data support for subsequent analysis and optimization, ensuring that the initial layout considers the basic conditions of traffic and power grid. Based on the coordinate set of charging piles, the relevant road segments are determined. These road segments are the necessary navigation paths for each charging pile within its influence range, ensuring a direct correlation between congestion analysis and charging demand. This avoids interference from irrelevant congestion on charging pile layout optimization, improves the accuracy of congestion analysis, and strengthens the connection between congestion and charging demand. Furthermore, traffic simulation models are used to determine whether these road segments have potential congestion at different times. By identifying congested road segments in advance, the concentrated charging demand caused by vehicle congestion can be predicted, providing a basis for subsequent targeted treatment. For road segments with potential congestion, a time-varying charging load curve is determined by combining factors such as road segment length, electric vehicle ratio, and single-vehicle fast charging power. This accurately quantifies the changes in charging load under congestion conditions, providing precise data for assessing power grid node voltage and facilitating a deeper understanding of the charging pile load at different times. By mapping time-varying charging load curves to grid nodes, calculating node voltages, and determining whether they fall below preset thresholds, the sections requiring additional charging stations are identified. This directly links traffic flow changes to grid performance, clarifying specific locations for optimization and establishing a complete evaluation chain from traffic to the grid. A candidate coordinate set is generated centered on the sections requiring additional charging stations and then filtered to determine the location coordinates of supplementary charging stations. A reasonable supplementary layout optimizes the distribution of charging stations, ensuring that the new charging stations effectively address charging demand issues caused by congestion while avoiding resource waste. The location coordinates of the supplementary charging stations are incorporated into the original coordinate set to form the final charging station layout scheme, achieving an optimization and upgrade of the original layout. A reasonable layout ensures the utilization rate of charging stations, improves user experience, and reduces the waste of grid resources.

[0058] This invention optimizes the layout of charging piles through close coordination of all aspects, improves the utilization rate of charging piles, and ensures the stable operation of the power grid. It effectively solves the problems in the prior art, such as voltage drop at local power grid nodes, reduced power of charging piles, or shutdown caused by the spatiotemporal clustering accumulation of vehicle charging demand. Attached Figure Description

[0059] Figure 1 This is a flowchart of the charging pile layout optimization method in an embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of a charging pile layout optimization system in an embodiment of the present invention. Detailed Implementation

[0061] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0062] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0063] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0064] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0065] To address the problems existing in the aforementioned related technologies, this embodiment provides a method, system, and device for optimizing the layout of charging piles.

[0066] Combination Figure 1 As shown, a charging pile layout optimization method provided in this embodiment of the invention includes:

[0067] The historical traffic flow and power grid node capacity of the target area are obtained, and a set of coordinates of charging piles in the target area is generated based on the historical traffic flow and power grid node capacity.

[0068] Specifically, obtaining historical traffic flow data for the target area can be achieved by collecting vehicle traffic data for various road segments within the area at different times. This data can come from traffic management department monitoring systems or professional traffic flow monitoring equipment, reflecting the vehicle density of different road segments during peak and off-peak hours. Data on grid node capacity needs to be obtained from the power sector, covering key information such as the maximum power supply capacity and voltage level of each grid node, clarifying the grid's carrying capacity limit under different loads. After obtaining these two types of data, specific algorithms or models are used, such as combining Geographic Information System (GIS) technology, to analyze the distribution relationship between areas with high traffic flow and grid nodes, generating a coordinate set of charging piles. This ensures that the location of charging piles initially meets traffic flow demands and is within the grid's carrying capacity range, providing basic location information for subsequent layout optimization.

[0069] Based on the coordinate set of the charging pile, multiple road segments are determined; wherein, the road segments involved are the necessary road segments of the navigation path of each charging pile within the influence range, for example, the road segments connected to the entrance of the charging pile.

[0070] Specifically, to determine the essential road segments along the navigation path within the influence range of each charging station, a predefined area centered on each charging station is first delineated using Geographic Information System (GIS) technology and road network data of the target area, based on the coordinate set of the charging stations. For example, a circular area with a radius of 1 kilometer centered on the charging station. Next, by analyzing the navigation path data of vehicles from surrounding major traffic nodes (such as highway exits, commercial centers, and residential areas) to the charging stations, essential road segments along the navigation path within this predefined area are selected. These road segments are the paths that vehicles must take to reach the charging stations, accurately reflecting the vehicle's travel route and charging demand path. Simultaneously, historical traffic flow data is used to verify the vehicle flow conditions on these road segments during peak charging station usage periods, ensuring that the selected road segments have a correlation with actual traffic flow and charging demand. This provides accurate basic data support for subsequent congestion analysis and charging load prediction.

[0071] Traffic simulation models are used to determine whether there are potential congested sections on all the road segments involved in each charging station within each preset time period.

[0072] Specifically, the relevant road segments are determined based on the generated set of charging pile coordinates. This mainly involves using each charging pile as the center and, based on preset distance thresholds or traffic correlation, delineating a certain range of road segments as the relevant road segments for that charging pile. For example, all road segments within a 1-kilometer radius of the charging pile can be defined. Alternatively, by analyzing traffic flow direction, several adjacent main road segments frequently traveled by vehicles before entering the charging pile can be identified. These road segments are then associated with the charging pile, establishing a connection between the charging pile and the surrounding road network. This clarifies the specific research scope for subsequent analysis of the impact of traffic conditions on charging pile usage, ensuring that subsequent assessments of traffic and charging demand are targeted.

[0073] If the road segment in question contains a potentially congested section, then based on the length of the potentially congested section, the total number of vehicles stranded in the corresponding preset time period is determined, and combined with the preset electric vehicle ratio coefficient and the fast charging power of a single vehicle, the time-varying charging load curve of the charging pile corresponding to the potentially congested section is determined.

[0074] Specifically, a traffic simulation model is used to determine whether there are potential congested sections on all road segments involved in each charging station within a preset time period. First, the day is divided into multiple preset time periods, such as morning and evening rush hours and off-peak hours. Then, historical traffic flow data is input into the traffic simulation model. Based on pre-set traffic rules and algorithms, the model simulates vehicle travel on different road segments and calculates traffic flow, speed, and other indicators for each segment at different times. When the simulation results show that the speed on a certain road segment is lower than the preset smooth traffic speed threshold during a specific time period, and this condition persists for a certain duration, the segment is considered potentially congested during that period. For example, if the model calculates that the average speed on a certain road segment during the morning rush hour is lower than 20 km / h for more than 30 minutes, it is considered a potentially congested segment. In this way, potential congestion around charging stations can be predicted in advance, providing a basis for further analysis of the impact of congestion on charging demand.

[0075] The time-varying charging load curve is mapped to the power grid node corresponding to the charging pile to obtain the node voltage of the power grid node. If the node voltage of the power grid node is lower than a preset voltage threshold, the potentially congested road section is determined to be a road section that needs to be recharged.

[0076] Specifically, once a potentially congested road segment is identified, the total number of stranded vehicles is determined based on the length of the potential congestion segment. This can be achieved by multiplying the segment length by the estimated average vehicle congestion density. The estimated average vehicle congestion density can be derived from historical data, and different types of road segments and different time periods may have different congestion densities. For example, the vehicle congestion density on urban arterial roads during peak hours may be 120 vehicles per kilometer. Then, combined with a preset trolleybus ratio coefficient (which can be determined based on the proportion of trolleybuses in the area; for example, if the proportion of trolleybuses in an area is 30%, the coefficient is 0.3) and the fast charging power per vehicle (the fast charging power varies for different vehicle models; the average fast charging power of a common vehicle model can be used, or it can be calculated by weighting the power according to the vehicle model ratio; for example, the average fast charging power per vehicle is 30kW), the time-varying charging load curve of the potentially congested road segment within the corresponding preset time period is calculated.

[0077] The specific calculation formula is: Time-varying charging load (kW) = Length of potentially congested road segment (km) × Average vehicle dwell density (vehicles / km) × Electric vehicle ratio coefficient × Single vehicle fast charging power (kW / vehicle). Through this calculation process, the changes in additional charging load caused by congestion at charging piles at different times can be obtained, providing accurate load data for subsequent assessment of grid node voltage.

[0078] By establishing an electrical connection model between charging piles and grid nodes, the model clarifies which grid node the charging pile draws power from and the electrical parameters between them, such as line impedance. The resulting time-varying charging load curve is mapped to the corresponding grid node. Then, based on grid power flow calculation methods, the charging load is used as a load input to that grid node, and the node voltage at different times is calculated. If the calculated node voltage is lower than a preset voltage threshold (this threshold is usually set by the power department according to grid safety operation standards, such as not lower than 95% of the rated voltage), the potentially congested section is identified as a section requiring additional charging piles. This method can accurately identify areas where increased charging load leads to excessively low grid node voltage, affecting the normal operation of charging piles, providing a clear target area for selecting subsequent locations for additional charging piles.

[0079] A candidate coordinate set is generated centered on the road section to be supplemented with charging piles, and the location coordinates of the supplementary charging piles are determined by filtering the location coordinates of the candidate coordinate set.

[0080] Specifically, a candidate coordinate set is generated centered on the road section to be supplemented with charging piles. This can be achieved by generating a series of location coordinate points at preset intervals (e.g., every 200 meters) within a certain range of the road section and its surroundings, or by combining information such as surrounding land use types and building distribution to select suitable candidate location coordinates for charging pile construction. When filtering the location coordinates in the candidate coordinate set, multiple factors need to be considered, such as the convenience of surrounding transportation (whether it is close to roads, etc.), the usability of the land (whether it is idle land, whether it meets urban planning requirements, etc.), and grid connection conditions (distance from grid nodes, ease of grid connection, etc.). Weighted scoring methods can be used to score each candidate coordinate, ultimately determining the location coordinates of the supplementary charging piles. This ensures that the newly added charging piles can maximally meet the increased charging demand caused by congestion and are feasible in terms of grid connection and construction conditions.

[0081] The location coordinates of the supplementary charging piles are incorporated into the coordinate set to obtain the charging pile layout scheme for the target area.

[0082] Specifically, the location coordinates of the selected supplementary charging piles are incorporated into the original charging pile coordinate set to form a complete and updated charging pile layout scheme. This new layout scheme comprehensively considers the impact of historical traffic flow, grid node capacity, and potential congestion on charging demand and the power grid. By rationally supplementing the location of charging piles, the charging load can be more effectively distributed, avoiding voltage drops caused by excessive load on local grid nodes. This improves the utilization rate of charging piles throughout the target area, while ensuring the safe and stable operation of the power grid and enhancing the user's charging experience.

[0083] The charging pile layout optimization method, system, and electronic equipment of this invention obtain historical traffic flow and power grid node capacity of the target area, and generate a coordinate set of charging piles accordingly. This provides key data support for subsequent analysis and optimization, ensuring that the initial layout considers the basic conditions of traffic and power grid. Based on the coordinate set of charging piles, the relevant road segments are determined. These road segments are the necessary navigation paths for each charging pile within its influence range, ensuring a direct correlation between congestion analysis and charging demand. This avoids interference from irrelevant congestion on charging pile layout optimization, improves the accuracy of congestion analysis, and strengthens the connection between congestion and charging demand. Furthermore, traffic simulation models are used to determine whether these road segments have potential congestion at different times. By identifying congested road segments in advance, the concentrated charging demand caused by vehicle congestion can be predicted, providing a basis for subsequent targeted treatment. For road segments with potential congestion, a time-varying charging load curve is determined by combining factors such as road segment length, electric vehicle ratio, and single-vehicle fast charging power. This accurately quantifies the changes in charging load under congestion conditions, providing precise data for assessing power grid node voltage and facilitating a deeper understanding of the charging pile load at different times. By mapping time-varying charging load curves to grid nodes, calculating node voltages, and determining whether they fall below preset thresholds, the sections requiring additional charging stations are identified. This directly links traffic flow changes to grid performance, clarifying specific locations for optimization and establishing a complete evaluation chain from traffic to the grid. A candidate coordinate set is generated centered on the sections requiring additional charging stations and then filtered to determine the location coordinates of supplementary charging stations. A reasonable supplementary layout optimizes the distribution of charging stations, ensuring that the new charging stations effectively address charging demand issues caused by congestion while avoiding resource waste. The location coordinates of the supplementary charging stations are incorporated into the original coordinate set to form the final charging station layout scheme, achieving an optimization and upgrade of the original layout. A reasonable layout ensures the utilization rate of charging stations, improves user experience, and reduces the waste of grid resources.

[0084] This invention optimizes the layout of charging piles through close coordination of all aspects, improves the utilization rate of charging piles, and ensures the stable operation of the power grid. It effectively solves the problems in the prior art, such as voltage drop at local power grid nodes, reduced power of charging piles, or shutdown caused by the spatiotemporal clustering accumulation of vehicle charging demand.

[0085] Optionally, obtaining historical traffic flow and grid node capacity of the target area, and generating a set of charging pile coordinates within the target area based on the historical traffic flow and grid node capacity, includes:

[0086] The target area is divided into grids, and the vehicle flow rate within each grid is determined according to the historical traffic flow rate and a preset time period.

[0087] Based on the vehicle flow rate of each grid, determine the net vehicle density of the grid in each time period;

[0088] Obtain the node capacity of all power grid nodes within the target area;

[0089] Using a clustering-location algorithm, the location coordinates of the charging pile and the mapping relationship between the charging pile and the corresponding power grid node are determined based on the node capacity and net vehicle density of the power grid node.

[0090] The coordinate set is obtained based on the mapping relationship and the position coordinates.

[0091] Specifically, the target area is first divided into grids. The appropriate grid size can be determined based on the geographical shape and size of the area; for example, an urban area can be divided into square grids with sides of 500 meters. Using collected historical traffic flow data, the number of vehicles passing through each grid is counted according to preset time periods (e.g., one hour per period), thus determining the vehicle flow of each grid at different time periods. Next, the net vehicle density of each grid within each time period is calculated. This can be achieved by dividing the vehicle flow by the grid area. For example, if a grid has 100 vehicles in a certain time period and its area is 0.25 square kilometers, then the net vehicle density is 400 vehicles per square kilometer.

[0092] Simultaneously, the node capacity information of all power grid nodes within the target area is acquired, including the maximum power supply capacity and current load status of each power grid node. Then, a clustering-site selection algorithm is applied. This algorithm first clusters the grid based on net vehicle density, identifying areas with high and concentrated vehicle density as potential charging pile deployment hotspots. In a preferred embodiment of the invention, the clustering-site selection algorithm is the K-means algorithm. First, based on the grid division and net vehicle density data within the target area, the K-means clustering algorithm is used to perform cluster analysis on areas with high vehicle density. The K-means algorithm divides data points into K clusters, ensuring that data points within a cluster are as close as possible to the cluster center, while maximizing the distance between clusters. In this scenario, the net vehicle density of each grid is used as a data point, and the K-means algorithm determines K cluster centers, which are the potential charging pile deployment hotspots. Next, combined with the power grid node capacity information, a site selection evaluation is performed on each cluster center. Evaluation indicators include the electrical connection conditions between the cluster center and surrounding power grid nodes, and the matching degree of power supply capacity. By calculating the distance from each cluster center to the nearest grid node and the remaining capacity of the grid node, candidate locations that meet the power supply requirements are selected. Finally, the specific location coordinates of the charging piles are determined based on the comprehensive score of the candidate locations (such as a weighted sum of distance and capacity), and a mapping relationship between the charging piles and the corresponding grid nodes is established.

[0093] Based on clustering, the algorithm selects charging stations by considering the capacity of power grid nodes. It evaluates factors such as the electrical connection conditions and power supply capacity matching between each candidate location within a hotspot area and surrounding power grid nodes to determine the specific coordinates of the charging stations. A mapping relationship is established between the charging stations and their corresponding power grid nodes, ensuring that the layout of charging stations meets traffic demands while remaining within the power grid's supply capacity. Finally, based on the determined mapping relationship and location coordinates, a complete set of charging station coordinates is obtained.

[0094] In this embodiment of the invention, grid partitioning and vehicle flow analysis can accurately locate densely populated vehicle areas. Combined with consideration of power grid node capacity, this ensures that the charging pile layout meets the charging demand brought by traffic flow while avoiding power supply problems caused by insufficient power grid capacity. The application of clustering-site selection algorithms improves the rationality and scientific nature of charging pile layout, enabling charging piles to centrally serve high-demand areas while effectively connecting with power grid nodes, improving the utilization efficiency of power grid resources, reducing construction costs, and enhancing the feasibility and adaptability of the charging pile layout scheme.

[0095] Optionally, determining multiple relevant road segments based on the coordinate set of the charging pile includes:

[0096] Obtain the navigation path connected to each of the charging piles;

[0097] The influence range is defined with the location coordinates of each charging pile as the center.

[0098] Based on the range of influence, the navigation path is filtered to obtain the necessary road segments of the navigation path for each charging pile, and the necessary road segments of the navigation path are taken as the involved road segments.

[0099] Specifically, firstly, by using the coordinate set of the charging piles and combining it with a vehicle navigation system (such as Gaode or Baidu Maps API), navigation paths from surrounding major traffic nodes (such as highway exits, commercial centers, and residential areas) to each charging pile are obtained. These navigation paths reflect the actual driving routes of vehicles to the charging piles. Next, an influence area is delineated centered on the location coordinates of each charging pile. In a preferred embodiment of the invention, this area can be a circular region with a fixed radius (e.g., 1 kilometer) or a polygonal region, with the specific shape dynamically adjusted according to the road network and traffic flow distribution around the charging pile. Then, within the delineated influence area, the obtained navigation paths are filtered to extract the essential road segments on the navigation paths. These essential road segments are the paths that vehicles must take to reach the charging piles, accurately reflecting the vehicle's driving route and charging demand path. Finally, these essential road segments of the navigation paths are used as relevant road segments for subsequent congestion analysis and charging load prediction.

[0100] In a preferred embodiment, the navigation path must pass through a section of road that connects to the entrance of the charging station.

[0101] In this embodiment of the invention, the actual service roads of the charging piles are accurately locked by the dual constraints of spatial region and reachability time, avoiding the omission of high-frequency use road sections or the inclusion of irrelevant roads. This not only reduces the amount of subsequent simulation calculations, but also provides a highly reliable input boundary for the coupled analysis of charging load and traffic conditions, making the layout optimization results more consistent with real travel behavior and power grid operation constraints.

[0102] Optionally, the step of determining whether there are potentially congested road sections in all the road segments involved for each charging station within each preset time period using a traffic simulation model includes:

[0103] The historical vehicle speed and historical traffic flow of each of the involved road segments are input into the traffic simulation model, and the traffic simulation model is used to perform simulation to output the average vehicle speed of each of the involved road segments in each preset time period.

[0104] Based on the average vehicle speed, determine whether any of the potential congested road sections exist for each of the charging piles across all the road segments involved.

[0105] The trolley traffic flow in each of the aforementioned road segments is determined according to a preset trolley ratio coefficient.

[0106] If the average vehicle speed of any of the road segments involved in the preset time period is lower than the preset congestion speed, and the time delay for vehicles on the road segment involved to reach the charging pile exceeds a preset duration threshold, and the electric vehicle flow exceeds a preset flow threshold, then the road segment involved in the preset time period is determined to be a potential congested road segment.

[0107] Specifically, historical vehicle speed and traffic flow data are collected for each road segment involved. This data can typically be obtained from traffic management department monitoring systems, intelligent traffic sensors, or historical traffic flow statistics reports. This data is then formatted into a time series format to match the time resolution required by the traffic simulation model. Finally, this data is input into the traffic simulation model (such as commonly used models like SUMO and VISSIM). In the simulation model, parameters such as road network topology, traffic signal control logic, and vehicle type distribution are set to match actual conditions to ensure the accuracy of the simulation results.

[0108] Next, a simulation model is run to simulate the traffic flow of each road segment within different preset time periods, based on the input historical vehicle speed and traffic flow data. After the simulation, the time delay of vehicles arriving at the charging station is analyzed. Using navigation systems or historical data, the expected time for vehicles to travel from the affected road segment to the charging station is calculated and compared with the simulated arrival time. If the simulated arrival time delay exceeds a preset threshold (e.g., 15 minutes), it is further confirmed that congestion on that road segment has a real impact on charging demand.

[0109] Simultaneously, based on a preset trolley ratio coefficient, the trolley traffic flow in each relevant road segment is calculated. Trolley traffic flow can be obtained by multiplying the total number of vehicles in historical traffic flow data by the trolley ratio coefficient. If the trolley traffic flow exceeds a preset threshold (e.g., 30% of the total traffic flow in the road segment), it indicates that congestion in that road segment significantly impacts the charging demand of charging stations. In other words, it assumes that all trolleys in congested road segments have charging needs, and the charging station layout is based on this assumption, ensuring that the charging station placement considers maximum charging demand and guarantees that charging needs of varying degrees can be met.

[0110] In a preferred embodiment, since not all trolleybuses in congested sections of road have charging needs, in order to avoid low utilization of charging piles in daily life and thus avoid resource waste, the proportion of charging vehicles in different time periods can be pre-analyzed and determined. For example, the average proportion of charging vehicles of all trolleybuses in different time periods can be analyzed for each existing charging pile in the target area corresponding to the navigation route. The average proportion of charging vehicles, the trolleybus proportion coefficient, and the total number of vehicles in the historical traffic flow data are multiplied to obtain the trolleybus flow rate with charging needs in the navigation route.

[0111] In a preferred embodiment, in order to improve the accuracy of charging pile layout and further avoid resource waste, only the road sections corresponding to the existing charging piles in the target area can be analyzed. That is, for the road sections corresponding to the existing charging piles in the target area, the historical traffic flow data can be used to analyze whether there are congested road sections in different time periods, and subsequent charging pile addition processing can be carried out for the congested road sections.

[0112] Ultimately, only when all three conditions are met simultaneously—namely, the average vehicle speed is below the congestion speed threshold, the vehicle arrival time delay exceeds a preset duration threshold, and the trolleybus flow exceeds a preset flow threshold—is the affected road segment deemed a potential congested segment within a preset time period. This comprehensive judgment method ensures that congestion identification is based not only on vehicle speed but also on actual charging demand and trolleybus flow, improving the accuracy and practicality of congestion identification.

[0113] In this embodiment of the invention, by using a traffic simulation model to accurately simulate the traffic conditions of each road segment in different preset time periods, potential congestion sections can be identified in advance. This method not only improves the accuracy of traffic congestion prediction but also provides a crucial basis for subsequent optimization of charging pile layout. By identifying potentially congested road segments, the usage demand and potential charging load pressure of charging piles at different time periods can be more accurately assessed. This helps to rationally plan the layout and configuration of charging piles, improve their utilization rate, and reduce the impact on the power grid caused by concentrated charging demand due to traffic congestion. This enhances the scientific rigor and practicality of the charging pile layout scheme, ensuring it better meets actual traffic and charging needs.

[0114] Optionally, if the road segment in question contains a potentially congested section, then based on the length of the potentially congested section, the total number of vehicles stranded in the corresponding preset time period of the potentially congested section is determined, and combined with a preset electric vehicle ratio coefficient and the fast charging power per vehicle, the time-varying charging load curve of the charging pile corresponding to the potentially congested section is determined, including:

[0115] Based on the average vehicle speed of the vehicles in the potential congested road segment and the length of the road segment, determine the total number of vehicles stranded in the potential congested road segment within the corresponding preset time period;

[0116] According to the preset electric vehicle ratio coefficient, the number of electric vehicles is selected from the total number of stranded vehicles;

[0117] Multiply the number of electric vehicles by the fast charging power of a single vehicle to obtain the peak charging demand of the potentially congested road segment within the corresponding preset time period;

[0118] Based on the peak charging demand of the potential congested road segment within the corresponding preset time period, the time-varying charging load curve of the charging pile during the preset time period is generated.

[0119] Specifically, the total number of stranded vehicles is first determined based on the average vehicle speed and road length of potentially congested road segments. Assuming the estimated average vehicle congestion density can be obtained from historical data, and that different types of road segments and different time periods may have different congestion densities, the formula for calculating the total number of stranded vehicles based on average vehicle speed (km / h) and road segment length (km) can be expressed as:

[0120]

[0121] The average vehicle length can be estimated based on the average length of vehicles in a typical city, for example, around 5 meters. However, a more common method is to use the formula from traffic engineering: vehicle density (vehicles / km) equals 1000 divided by (average vehicle length (m) + vehicle spacing (m)). In congested conditions, vehicle spacing is usually small, so the vehicle density can be approximated as high. For example, if the average speed is 10 km / h, the vehicle density, based on empirical formulas or on-site measurements, might be 120 vehicles / km. Therefore, for a road segment of 2 km, the total number of stranded vehicles would be 240. Then, combining the preset electric vehicle ratio coefficient and the fast charging power per vehicle, the time-varying charging load curve is determined. Assuming the preset electric vehicle ratio coefficient is 30% (i.e., 0.3) and the fast charging power per vehicle is 30kW, the number of electric vehicles is 240 × 0.3 = 72, and the peak charging demand is 72 × 30kW = 2160kW. Finally, connecting the peak charging demands at different times generates the time-varying charging load curve for the charging pile.

[0122] If the analysis reveals no potential congestion sections in the relevant road segment, the charging load of the charging station is considered to be at a normal level within the preset time period, and will not significantly increase charging demand due to traffic congestion. In this case, the charging load of the charging station is mainly determined by daily charging demand, which can be estimated using historical charging data. The system marks this charging station as "low-risk" and prioritizes other charging stations with potential congestion risks in subsequent layout optimization. This approach ensures the comprehensiveness and targeting of the optimization process, avoids over-optimization of charging stations without congestion risk, and concentrates resources on addressing key issues that may affect grid stability and user experience.

[0123] In this embodiment of the invention, by accurately calculating the total number of vehicles stranded on potentially congested road sections and combining this with the proportion of electric vehicles and the fast charging power of individual vehicles, the peak charging demand of charging piles at different times can be predicted in detail. This method not only improves the accuracy of charging load prediction but also provides a crucial basis for subsequent grid node voltage assessment and charging pile layout optimization. By identifying the peak charging load at different times, the capacity and layout of charging piles can be planned more rationally, avoiding grid voltage drops caused by concentrated charging demand, thereby improving the utilization rate of charging piles, enhancing user experience, and ensuring the safe and stable operation of the power grid.

[0124] Optionally, mapping the time-varying charging load curve to the grid node corresponding to the charging pile to obtain the node voltage of the grid node includes:

[0125] Based on the mapping relationship between the charging pile and the corresponding power grid node, the time-varying charging load curve within the preset time period is superimposed onto the power grid node corresponding to the charging pile to form the time-period load of the power grid node;

[0126] Based on the time-period load of the power grid node, power flow calculation is performed on the power grid node to obtain the node voltage of the power grid node within the preset time period.

[0127] Specifically, firstly, based on the pre-established mapping relationship between charging piles and grid nodes, the grid node connected to each charging pile is identified. The time-varying charging load curve for a preset time period is superimposed onto the corresponding grid node and added to the historical average load of the grid during that preset time period to form the time-period load of the grid node. This step requires converting the charging load into the load increment of the grid node, considering electrical connection parameters between the charging pile and the grid node, such as line impedance and transformer capacity. Next, power flow calculations are performed using power system analysis software (such as MATPOWER, PSSE, etc.). The time-period load of the grid node, grid topology, line parameters, and other data are input, and the power flow calculation algorithm is run. The power flow calculation simulates the flow of electricity in the grid and calculates the node voltage of each grid node within the preset time period based on Kirchhoff's laws and Ohm's law.

[0128] In this embodiment of the invention, by mapping the time-varying charging load curve of the charging pile to the grid node and performing power flow calculations, the impact of charging load on the grid node voltage can be accurately assessed. This method helps to identify grid nodes where increased charging load may cause voltage drops in advance, thus providing a scientific basis for optimizing charging pile layout and grid upgrades. By rationally planning the location and capacity of charging piles, the problem of excessively low grid node voltage can be effectively avoided, improving the power supply quality and stability of the grid, ensuring the reliable operation of charging piles, enhancing user experience, and reducing grid resource waste.

[0129] Optionally, the step of generating a candidate coordinate set centered on the road section to be supplemented with charging piles, and filtering the position coordinates in the candidate coordinate set to determine the position coordinates of the supplementary charging piles, includes:

[0130] A predetermined radius range is extended outward from the geometric center of the road section to be supplemented with charging piles to form a candidate area for the supplementary charging piles;

[0131] Within the candidate region, multiple candidate coordinate points are generated according to a preset step size;

[0132] Obtain the remaining capacity of the power grid node corresponding to each candidate coordinate point, and perform preliminary screening of the candidate coordinate points based on the remaining capacity to obtain the remaining candidate coordinate points;

[0133] The remaining candidate coordinates are sorted by a multi-objective screening mechanism based on the minimum distance from the remaining candidate coordinates to the road section to be supplemented, the distance between the remaining candidate coordinates and other charging piles within a preset range, and the remaining capacity margin of the power grid node.

[0134] The location coordinates of the remaining candidate coordinate points are selected from the sorting results as the location coordinates of the supplementary charging pile.

[0135] Specifically, using the geometric center of the section to be supplemented with charging piles as the origin, a circular candidate area is formed by extending outwards by a preset radius (e.g., 1 kilometer) using Geographic Information System (GIS) tools. Within this area, grid points are generated as candidate coordinate points at preset step intervals (e.g., every 50 meters). Further, selection is performed based on terrain and land availability: terrain data is analyzed using GIS to exclude areas with slopes exceeding a certain threshold (e.g., 10%), avoiding construction on steep slopes or unsuitable terrain; simultaneously, land use planning data is used to select usable land that meets urban planning requirements, such as vacant land and public facility land, ensuring the feasibility of candidate coordinate points. Finally, suitable candidate coordinate points are selected from the qualified grid points for subsequent charging pile layout optimization. The remaining capacity data of the corresponding power grid node for each candidate coordinate point is obtained through the power grid API, and points with insufficient remaining capacity are eliminated, completing the initial screening. Then, a multi-objective screening mechanism is constructed, comprehensively considering the minimum distance from the remaining candidate coordinates to the road section to be supplemented with charging piles (the closer the distance, the higher the priority), the distance from the surrounding charging piles (the larger the distance, the higher the priority, to avoid excessive concentration), and the remaining capacity margin of the power grid node (the larger the margin, the higher the priority). The remaining candidate coordinates are sorted using a weighted summation method. Finally, the optimal remaining candidate coordinates are selected from the sorting results as the location coordinates of the supplementary charging pile.

[0136] In this embodiment of the invention, a candidate coordinate set is generated through dual constraints of terrain and power grid. Combined with multi-dimensional screening indicators, this ensures that supplementary charging piles are located close to road sections with high demand, while also guaranteeing reasonable spatial distribution and power grid access conditions. The deep integration of GIS spatial analysis functions and power grid data improves the scientific and accurate nature of layout decisions, enabling new charging piles to efficiently share the load pressure of existing charging piles, alleviate voltage drop problems at power grid nodes, and improve the overall service level of the charging network and the operating efficiency of the power grid.

[0137] Optionally, incorporating the location coordinates of the supplementary charging piles into the coordinate set to obtain the charging pile layout scheme for the target area includes:

[0138] Add the location coordinates of the supplementary charging station to the coordinate set;

[0139] The mapping relationship between the charging piles and the power grid nodes in the coordinate set is updated according to the mapping relationship between the supplementary charging piles and the power grid nodes to obtain the charging pile layout scheme of the target area.

[0140] Specifically, adding the location coordinates of supplementary charging piles to the coordinate set requires first reading the coordinates from external storage devices or user input interfaces, adjusting their format to match the original coordinate set (e.g., using latitude and longitude coordinates), and then adding these coordinates to the original coordinate set's data structure (list, array, or database table) through program logic or manual operation. This ensures all charging pile coordinates are stored and managed in the same data set. Regarding updating the mapping relationship, the corresponding grid node is determined based on the specific connection of the supplementary charging pile. This requires on-site surveys or coordination with the grid department to obtain the grid connection point information of the supplementary charging piles and clarify their connection relationship with the grid node. Then, using data structures or database operations, a mapping relationship is established between the supplementary charging piles and the corresponding grid node. For example, a mapping table in the database could contain fields such as charging pile ID, location coordinates, and the corresponding grid node ID. Inserting this information from the supplementary charging piles into the mapping table completes the update of the mapping relationship.

[0141] In this embodiment of the invention, by incorporating the location coordinates of supplementary charging piles into the original coordinate set, a complete charging pile layout scheme is formed, providing accurate basic data for subsequent operation and management. Simultaneously, the updated mapping relationship helps to accurately assess the impact of supplementary charging piles on grid nodes, ensuring the safe and stable operation of the grid. The integrated layout scheme can more scientifically guide the construction of charging piles, improve resource utilization, meet users' charging needs, and enhance the overall service quality of charging facilities and user satisfaction.

[0142] Combination Figure 2 As shown, a charging pile layout optimization system of the present invention includes:

[0143] The data acquisition module is used to acquire historical traffic flow and power grid node capacity of the target area, and generate a set of coordinates of charging piles in the target area based on the historical traffic flow and power grid node capacity.

[0144] The road segment determination module is used to determine multiple involved road segments based on the coordinate set of the charging pile; wherein, the involved road segments are the necessary road segments of the navigation path of each charging pile within the influence range;

[0145] The congestion assessment module is used to determine, through a traffic simulation model, whether there are potential congested road sections in all the road segments involved for each charging station within each preset time period.

[0146] The load calculation module is used to determine the total number of vehicles stranded in the potential congested road segment within the corresponding preset time period based on the road segment length of the potential congested road segment, and to determine the time-varying charging load curve of the charging pile corresponding to the potential congested road segment by combining the preset electric vehicle ratio coefficient and the fast charging power of a single vehicle.

[0147] The voltage assessment module is used to map the time-varying charging load curve to the power grid node corresponding to the charging pile, and obtain the node voltage of the power grid node. If the node voltage of the power grid node is lower than a preset voltage threshold, the potentially congested road section is determined to be a road section that needs to be recharged.

[0148] The charging pile replenishment planning module is used to generate a candidate coordinate set centered on the road section to be replenished, and to filter the location coordinates in the candidate coordinate set to determine the location coordinates of the replenished charging piles.

[0149] The scheme generation module is used to incorporate the location coordinates of the supplementary charging piles into the coordinate set to obtain the charging pile layout scheme for the target area.

[0150] The charging pile layout optimization system of the present invention has the same advantages over the prior art as the charging pile layout optimization method, and will not be repeated here.

[0151] An electronic device according to the present invention includes a memory and a processor;

[0152] The memory is used to store computer programs;

[0153] The processor is used to implement the charging pile layout optimization method as described above when executing the computer program.

[0154] The advantages of the electronic device of the present invention compared with the prior art are the same as the advantages of the charging pile layout optimization method compared with the prior art, and will not be repeated here.

[0155] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A charging pile layout optimization method, characterized in that, The method comprises the following steps: acquiring historical traffic flow and grid node capacity of a target area, and generating a coordinate set of charging piles in the target area according to the historical traffic flow and the grid node capacity; determining a plurality of involved road segments according to the coordinate set of the charging piles; wherein the involved road segment is a road segment that must be passed through on a navigation path within an influence range of each charging pile; judging whether there is a potential congestion road segment in each of the involved road segments of each charging pile within each preset time period through a traffic simulation model; if the involved road segment has the potential congestion road segment, determining a total number of vehicles stranded in the potential congestion road segment within the corresponding preset time period according to a road segment length of the potential congestion road segment, and determining a time-varying charging load curve of the potential congestion road segment corresponding to the charging pile in combination with a preset electric vehicle proportion coefficient and a single vehicle fast charging power; mapping the time-varying charging load curve to a grid node corresponding to the charging pile to obtain a node voltage of the grid node, and determining that the potential congestion road segment is a to-be-supplemented road segment if the node voltage of the grid node is lower than a preset voltage threshold; generating a candidate coordinate set with the to-be-supplemented road segment as the center, and screening position coordinates in the candidate coordinate set to determine position coordinates of a supplemented charging pile; wherein the method specifically comprises: extending a preset radius range outward from a geometric center of the to-be-supplemented road segment to form a candidate area of the supplemented charging pile; generating a plurality of candidate coordinate points in the candidate area according to a preset step length; acquiring a residual capacity of the grid node corresponding to each candidate coordinate point, and preliminarily screening the candidate coordinate points according to the residual capacity to obtain residual candidate coordinate points; sorting the residual candidate coordinate points according to a minimum distance from the residual candidate coordinate points to the to-be-supplemented road segment, a distance between the residual candidate coordinate points and other charging piles within a preset range, and a residual capacity margin of the grid node through a multi-objective screening mechanism; and selecting the position coordinates of the residual candidate coordinate points from the sorting result as the position coordinates of the supplemented charging pile; incorporating the position coordinates of the supplemented charging pile into the coordinate set to obtain a charging pile layout scheme of the target area.

2. The charging pile layout optimization method according to claim 1, characterized in that, The method of acquiring historical traffic flow and grid node capacity of a target area, and generating a coordinate set of charging piles in the target area according to the historical traffic flow and the grid node capacity, comprises the following steps: dividing the target area into grids, and determining vehicle flow in each grid according to a preset time period and the historical traffic flow; determining a net vehicle density of each grid within each time period according to the vehicle flow of each grid; acquiring node capacities of all grid nodes in the target area; determining position coordinates of charging piles and a mapping relationship between the charging piles and grid nodes corresponding to the charging piles through a clustering-siting algorithm according to the node capacities of the grid nodes and the net vehicle densities; obtaining the coordinate set according to the mapping relationship and the position coordinates.

3. The charging pile layout optimization method according to claim 2, characterized in that, The method comprises the following steps: Obtain a navigation path connected with each charging pile; Draw an influence range with the position coordinate of each charging pile as the center; According to the influence range, screen the navigation path to obtain the navigation path must-pass section of each charging pile, and take the navigation path must-pass section as the involved section.

4. The charging pile layout optimization method according to claim 1, characterized in that, The method comprises the following steps: Input the historical vehicle speed and the historical traffic flow of each involved section into the traffic simulation model, simulate through the traffic simulation model, and output the average vehicle speed of each involved section in each preset time period; According to the average vehicle speed, determine whether there is a potential congestion section in all involved sections of each charging pile; Determine the electric vehicle flow in each involved section according to a preset electric vehicle proportion coefficient; If the average vehicle speed of any involved section in the preset time period is lower than a preset congestion vehicle speed, the time delay of the vehicle of the involved section to the charging pile exceeds a preset time threshold, and the electric vehicle flow exceeds a preset flow threshold, it is determined that the involved section is a potential congestion section in the preset time period.

5. The charging pile layout optimization method according to claim 4, characterized in that, If the involved section has the potential congestion section, determine the total number of vehicles stranded in the potential congestion section in the corresponding preset time period according to the length of the potential congestion section, and determine the time-varying charging load curve of the potential congestion section corresponding to the charging pile in combination with the preset electric vehicle proportion coefficient and the single vehicle fast charging power. According to the average vehicle speed of the vehicle of the potential congestion section and the length of the potential congestion section, determine the total number of vehicles stranded in the potential congestion section in the corresponding preset time period; According to the preset electric vehicle proportion coefficient, screen out the number of electric vehicles from the total number of stranded vehicles; Multiply the number of electric vehicles by the single vehicle fast charging power to obtain the peak charging demand of the potential congestion section in the corresponding preset time period; According to the peak charging demand of the potential congestion section in the corresponding preset time period, generate the time-varying charging load curve of the charging pile in the preset time period.

6. The charging pile layout optimization method according to claim 2, characterized in that, The method comprises the following steps: According to the mapping relationship between the charging pile and the power grid node corresponding to the charging pile, superimpose the time-varying charging load curve in the preset time period to the power grid node corresponding to the charging pile to form the time period load of the power grid node; According to the time period load of the power grid node, perform power flow calculation on the power grid node to obtain the node voltage of the power grid node in the preset time period.

7. The charging pile layout optimization method according to claim 1, characterized in that, The method comprises the following steps: Add the position coordinate of the supplementary charging pile to the coordinate set; The method comprises the following steps: According to the mapping relationship between the supplementary charging pile and the power grid node, the mapping relationship between the charging pile in the coordinate set and the power grid node is updated, and the charging pile layout scheme of the target area is obtained.

8. A charging pile layout optimization system, characterized in that, Comprise: A data acquisition module is configured to acquire historical traffic flow and power grid node capacity of a target area, and generate a coordinate set of charging piles in the target area according to the historical traffic flow and the power grid node capacity; A road section determination module is configured to determine a plurality of involved road sections according to the coordinate set of the charging piles; wherein the involved road section is a necessary road section of a navigation path within an influence range of each charging pile; A congestion judgment module is configured to determine whether there is a potential congestion road section in each preset time period for all involved road sections of each charging pile through a traffic simulation model; A load calculation module is configured to determine the total number of vehicles stranded in the potential congestion road section in the corresponding preset time period according to the road section length of the potential congestion road section, and determine a time-varying charging load curve of the potential congestion road section corresponding to the charging pile in combination with a preset electric vehicle proportion coefficient and a single vehicle fast charging power; A voltage evaluation module is configured to map the time-varying charging load curve to the power grid node corresponding to the charging pile to obtain a node voltage of the power grid node, and determine that the potential congestion road section is a to-be-supplemented pile road section if the node voltage of the power grid node is lower than a preset voltage threshold; A pile supplement planning module is configured to generate a candidate coordinate set centered on the to-be-supplemented pile road section, and screen position coordinates in the candidate coordinate set to determine position coordinates of a supplementary charging pile; wherein, specifically comprising: taking the geometric center of the to-be-supplemented pile road section as the origin to extend a preset radius range outward to form a candidate area of the supplementary charging pile; generating a plurality of candidate coordinate points in the candidate area according to a preset step length; acquiring the remaining capacity of the power grid node corresponding to each candidate coordinate point, and preliminarily screening the candidate coordinate points according to the remaining capacity to obtain remaining candidate coordinate points; sorting the remaining candidate coordinate points according to the minimum distance from the remaining candidate coordinate points to the to-be-supplemented pile road section, the distance between the remaining candidate coordinate points and other charging piles within a preset range, and the remaining capacity margin of the power grid node through a multi-objective screening mechanism; and selecting the position coordinates of the remaining candidate coordinate points as the position coordinates of the supplementary charging pile from the sorting result; A scheme generation module is configured to incorporate the position coordinates of the supplementary charging pile into the coordinate set to obtain a charging pile layout scheme of the target area.

9. An electronic device, comprising: Comprise a memory and a processor; The memory is configured to store a computer program; The processor is configured to implement the charging pile layout optimization method of claims 1-7 when executing the computer program.

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