Charging pile layout optimization method, system and equipment

Abstract

The invention provides a charging pile layout optimization method, system and device, and relates to the technical field of charging facility management, and the method comprises the steps: obtaining historical traffic flow and power grid node capacity, generating a charging pile coordinate set, and determining a related road section; wherein the related road section is a road section inevitably passed by a navigation path of each charging pile in an influence range; identifying potential congested road sections in each time period by using a traffic simulation model; if congestion exists, the total number of detained vehicles is calculated, and a time-varying charging load curve is generated in combination with the electric vehicle proportion and the single vehicle fast charging power; mapping the curve to a power grid node, evaluating whether the node voltage is lower than a threshold value, and confirming a to-be-repaired pile road section; generating a candidate coordinate set around the road section to be supplemented with the piles, screening out the position coordinates of the supplementary charging piles, and merging the position coordinates into the original coordinate set to form a complete layout scheme. According to the invention, optimization of the charging pile layout is realized, the utilization rate of the charging piles is improved, and stable operation of a power grid is ensured.

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, and then the fast charging demand of the vehicle is 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 voltage of the local power distribution node 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 wastes the 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: 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; 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 must-pass road segment within the influence range of each charging pile; determining whether there is a potential congestion road segment in each preset time period for all involved road segments of each charging pile through a traffic simulation model; 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; 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 congested 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; 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; incorporating the position coordinate of the supplementary charging pile into the coordinate set to obtain a charging pile layout scheme of the target region.

[0007] 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: 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; determining a net vehicle density of each grid in each time period according to the vehicle flow of each grid; obtaining a node capacity of all power grid nodes in the target region; determining a position coordinate of a charging pile and a mapping relationship between the charging pile and the 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; obtaining the coordinate set according to the mapping relationship and the position coordinate.

[0008] Optionally, the coordinate set of the charging pile is used to determine a plurality of involved road sections, including: obtaining a navigation path connected to each charging pile; defining an influence range centered on the position coordinate of each charging pile; 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.

[0009] Optionally, the traffic simulation model is used to determine whether there is a potential congested road section in each preset time period for all the involved road sections of each charging pile, including: inputting a historical vehicle speed and the historical traffic flow of each involved road section into the traffic simulation model, simulating by the traffic simulation model, and outputting an average vehicle speed of each involved road section in each preset time period; determining whether there is a potential congested road section for all the involved road sections of each charging pile according to the average vehicle speed; According to a preset electric vehicle proportion coefficient, determine the electric vehicle flow in each of the involved road sections; If the average speed of any of the involved road sections in the preset time period is lower than a preset congestion speed, the time delay of a vehicle arriving at the charging pile of the involved road section 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.

[0010] Optionally, if the involved road section is the potential congestion road section, according to the length of the potential congestion road section, determine the total number of vehicles stranded in the potential congestion road section in the corresponding preset time period, and combine a preset electric vehicle proportion coefficient and a single vehicle fast charging power to determine a time-varying charging load curve of the potential congestion road section corresponding to the charging pile, including: According to the average speed of the vehicle of the potential congestion road section and the length of the road section, determine the total number of vehicles stranded in the potential congestion road section in the corresponding preset time period; According to the preset electric vehicle proportion coefficient, select 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 road section in the corresponding preset time period; According to the peak charging demand of the potential congestion road section in the corresponding preset time period, generate the time-varying charging load curve of the charging pile in the preset time period.

[0011] Optionally, the mapping of the time-varying charging load curve to the power grid node corresponding to the charging pile obtains the node voltage of the power grid node, including: 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 a 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.

[0012] Optionally, the candidate coordinate set is generated with the to-be-supplemented road section as the center, and the position coordinates in the candidate coordinate set are screened to determine the position coordinates of the supplemental charging pile, including: Extend a preset radius range outward from the geometric center of the to-be-supplemented road section to form a candidate area of the supplemental charging pile; In the candidate area, a plurality of candidate coordinate points are generated according to a preset step length; obtain a residual capacity of the power grid node corresponding to each of the candidate coordinate points, and perform preliminary screening on the candidate coordinate points according to the residual capacity, to obtain residual candidate coordinate points; According to the minimum distance from the residual candidate coordinate points to the to-be-supplemented charging pile road section, the distance between the residual candidate coordinate points and other charging piles within a preset range, and the residual capacity margin of the power grid node, the residual candidate coordinate points are sorted through a multi-target screening mechanism. The position coordinates of the residual candidate coordinate points are selected from the sorting result as the position coordinates of the supplementary charging pile.

[0013] Optionally, the incorporation of the position coordinates of the supplementary charging pile into the coordinate set to obtain the charging pile layout scheme of the target region comprises: adding the position coordinates of the supplementary charging pile to the coordinate set; 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 to obtain the charging pile layout scheme of the target region.

[0014] In a second aspect, a charging pile layout optimization system is provided, comprising: A data acquisition module is configured to acquire historical traffic flow and power grid node capacity of a target region, and generate a coordinate set of charging piles in the target region 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 navigation path mandatory road section within the 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 the involved road sections of each charging pile through a traffic simulation model; A load calculation module is configured to, if the involved road section has the potential congestion road section, determine the total number of stranded vehicles in the corresponding preset time period for the potential congestion road section 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 a 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 charging 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 charging pile road section, and screen the position coordinates in the candidate coordinate set to determine the position coordinates of the supplementary charging pile. The 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.

[0015] In a third aspect, the electronic device comprises 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 when executing the computer program.

[0016] The charging pile layout optimization method, system and electronic device of the present application obtain the historical traffic flow and grid node capacity of the target area, and generate a coordinate set of charging piles based on the same, thereby providing key data support for subsequent analysis and optimization, ensuring that the initial layout takes into account the basic situation of traffic and power grid, determining the involved road sections based on the coordinate set of charging piles, wherein the involved road sections are the necessary road sections of the navigation path within the influence range of each charging pile, thereby ensuring the direct correlation between congestion analysis and charging demand, avoiding the interference of irrelevant congestion on charging pile layout optimization, improving the accuracy of congestion analysis, making the correlation between congestion and charging demand more closely, and using a traffic simulation model to determine whether there is potential congestion on these road sections at different time periods. By identifying the congested road sections in advance, the problem of concentrated charging demand caused by vehicle congestion can be predicted, thereby providing a basis for subsequent targeted treatment. For road sections with potential congestion, a time-varying charging load curve is determined by considering factors such as road length, electric vehicle proportion and single vehicle fast charging power, thereby accurately quantifying the charging load change under congestion, providing accurate data for evaluating the voltage of the grid node, and helping to better understand the load of the charging pile at different time periods. The time-varying charging load curve is mapped to the grid node, the node voltage is calculated, and it is determined whether it is lower than the preset threshold, thereby determining the to-be-supplemented pile road section, directly correlating the traffic flow change with the grid performance, and clearly determining the specific position that needs to be optimized, thereby realizing a complete evaluation chain from traffic to grid. A candidate coordinate set is generated around the to-be-supplemented pile road section, and screening is performed to determine the position coordinates of the supplementary charging pile. By reasonably supplementing the layout of the charging pile, it is ensured that the newly added charging pile can effectively solve the problem of charging demand caused by congestion, while avoiding resource waste. The position coordinates of the supplementary charging pile are incorporated into the original coordinate set to form a final charging pile layout scheme, thereby realizing the optimization and upgrading of the original layout, ensuring the utilization rate of the charging pile, improving the user experience, and reducing the waste of grid resources.

[0017] The present application realizes the optimization of charging pile layout through the close cooperation of each link, improves the utilization rate of charging piles, and at the same time ensures the stable operation of the power grid, effectively solving the problems of local grid node voltage drop, charging pile power reduction or shutdown caused by the time and space clustering accumulation of vehicle charging demand in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flow chart of a charging pile layout optimization method in an embodiment of the present application is shown. Figure 2 A schematic diagram of a charging pile layout optimization system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0019] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, but rather, these embodiments are provided to make the present application more thorough and complete. It should be understood that the drawings and embodiments of the present application are only for illustrative purposes, and are not intended to limit the scope of protection of the present application.

[0020] It should be understood that each step described in the method embodiments of the present application can be performed in different orders, and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0021] The term "comprising" and variations thereof as used herein are open-ended, that is, "comprising but not limited to"; the term "based on" is "based at least in part 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"; the term "optionally" means "optional embodiments". Related definitions will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in the present application are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0022] It should be noted that the modification of "one" or "multiple" mentioned in the present application is illustrative and not limiting, and those skilled in the art should understand that, unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0023] To address the problems in the above related technologies, the present embodiment provides a charging pile layout optimization method, system and device.

[0024] In combination Figure 1 As shown, the charging pile layout optimization method provided in an embodiment of the present application includes: Obtain 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.

[0025] Specifically, obtaining the historical traffic flow of the target area can be achieved by collecting vehicle passing data of road segments in the area at different time periods, which can come from a monitoring system of a traffic management department or a professional traffic flow monitoring device, and can reflect the vehicle density of different road segments at different time periods such as peak and off-peak. The data of the power grid node capacity need to be obtained from the power department, which covers the maximum power supply capacity, voltage level and other key information of each power grid node, and clearly defines the upper limit of the power grid under different loads. After obtaining the above two kinds of data, a specific algorithm or model is used, for example, combined with geographic information system (GIS) technology, to analyze the distribution relationship between the area with high traffic flow and the power grid node, to generate the coordinate set of the charging piles, so that the location of the charging piles preliminarily meets the traffic flow demand and is within the power grid carrying range, providing basic location information for subsequent layout optimization.

[0026] According to the coordinate set of the charging piles, a plurality of involved road segments are determined; wherein the involved road segment is a navigation path must-pass road segment in the influence range of each charging pile, for example, a road segment connected with the entrance of the charging pile.

[0027] Specifically, in order to determine the navigation path must-pass road segment in the influence range of each charging pile, first of all, based on the coordinate set of the charging piles, the geographic information system technology is used in combination with the road network data of the target area to delimit a preset range centered on each charging pile, for example, a circular area with the charging pile as the center and a radius of 1 kilometer. Then, by analyzing the navigation path data of vehicles from the surrounding main traffic nodes (such as highway exits, commercial centers, residential areas, etc.) to the charging piles, the must-pass road segments on the navigation path in the preset range are screened out. These road segments are the paths that vehicles must pass when going to the charging piles, and can truly reflect the driving route and charging demand path of the vehicles. At the same time, combined with the historical traffic flow data, the vehicle flow situation of these road segments at the charging pile use peak period is verified to ensure that the screened involved road segments have actual traffic flow and charging demand correlation, thereby providing accurate basic data support for subsequent congestion analysis and charging load prediction.

[0028] Whether there is a potential congestion road segment in each of the involved road segments of each charging pile in each preset time period is determined by a traffic simulation model.

[0029] Specifically, according to the generated charging pile coordinate set, the road sections involved are determined, mainly taking each charging pile as the center, and according to the preset distance threshold or traffic correlation degree, the road sections within a certain range are determined as the involved road sections of the charging pile. For example, all road sections within a range of 1 kilometer with the charging pile as the center can be set, or by analyzing the traffic flow direction, several main road sections commonly traveled by vehicles before entering the charging pile are determined, and these road sections are associated with the charging pile, thereby establishing the connection between the charging pile and the surrounding road network, and specifying the specific research scope for subsequent analysis of the influence of traffic conditions on the use of charging piles, to ensure that the subsequent evaluation of traffic and charging demand is targeted.

[0030] If the involved road section has the potential congestion road section, according to the road section length of the potential congestion road section, the total number of vehicles stranded in the potential congestion road section within the corresponding preset time period is determined, and in combination with the preset electric vehicle proportion coefficient and the single vehicle fast charging power, the time-varying charging load curve of the potential congestion road section corresponding to the charging pile is determined.

[0031] Specifically, whether there is a potential congestion road section in each preset time period for all involved road sections of each charging pile is determined by a traffic simulation model. First, a day needs to be divided into multiple preset time periods, such as morning and evening peak hours, flat peak hours, etc. Then, historical traffic flow data is input into the traffic simulation model, which simulates the driving of vehicles on different road sections based on pre-set traffic rules and algorithms, and calculates the traffic flow, vehicle speed, etc. of each road section in different time periods. When the simulation result shows that the vehicle speed of a road section in a specific time period is lower than the preset smooth vehicle speed threshold and the duration exceeds a certain length, it is determined that there is a potential congestion in the road section in the time period. For example, if the model calculates that the average vehicle speed of a road section in the morning peak period is lower than 20 kilometers / hour and lasts for more than 30 minutes, it is considered as a potential congestion road section. In this way, the congestion that may occur in the road sections around the charging pile can be predicted in advance, providing a basis for further analyzing the influence of congestion on charging demand.

[0032] 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, and if the node voltage of the power grid node is lower than the preset voltage threshold, the potential congestion road section is determined as a to-be-supplemented pile road section.

[0033] Specifically, after determining that there is a potential congestion road segment involved in the road segment, the total number of stranded vehicles is determined according to the length of the potential congestion road segment, which can be obtained by multiplying the length of the road segment by the estimated average stranded vehicle density. The estimated average stranded vehicle density can be statistically obtained from historical data, and different types of road segments and different time periods may have different stranded densities. For example, the vehicle stranded density of a city trunk road during peak hours may be 120 vehicles per kilometer. Then, the time-varying charging load curve of the potential congestion road segment in the corresponding preset time period is calculated by combining the preset electric vehicle proportion coefficient (which can be determined according to the proportion of electric vehicles in the region, such as an electric vehicle proportion of 30% in a certain region, and the coefficient is 0.3) and the single vehicle fast charging power (different vehicle models have different fast charging powers, and the average fast charging power of a common vehicle model can be taken or weighted according to the proportion of vehicle models, such as an average single vehicle fast charging power of 30kW).

[0034] The specific calculation formula is: time-varying charging load (kW) = potential congestion road segment length (km) x vehicle average stranded density (vehicles / km) x electric vehicle proportion coefficient x single vehicle fast charging power (kW / vehicle). Through this calculation process, the additional charging load variation of the charging pile due to congestion at different time periods can be obtained, providing accurate load data for subsequent evaluation of the voltage of the power grid node.

[0035] By establishing an electrical connection model between the charging pile and the power grid node, it is clear that the charging pile takes power from which power grid node and the electrical parameters between them, such as line impedance, etc. The time-varying charging load curve obtained is mapped to the corresponding power grid node of the charging pile, and then according to the power flow calculation method of the power grid, the charging load is input as a load of the power grid node. The node voltage of the power grid node at different time periods is calculated. If the calculated node voltage is lower than the preset voltage threshold (which is usually set by the power department according to the safe operation standard of the power grid, such as not lower than 95% of the rated voltage), it is determined that the potential congestion road segment is a to-be-supplemented pile road segment. It can accurately determine the area where the increase in charging load leads to excessively low voltage of the power grid node, affecting the normal operation of the charging pile, and provide a clear target area for subsequent selection of the location of the supplemented charging pile.

[0036] A candidate coordinate set is generated around the to-be-supplemented pile road segment, and the position coordinates in the candidate coordinate set are screened to determine the position coordinates of the supplemented charging pile.

[0037] Specifically, the candidate coordinate set is generated around the road section to be supplemented, a series of position coordinate points can be generated at a preset interval distance (such as every 200 meters) within a certain range around the road section and its periphery, or combined with the land use type, building distribution and other information around the road section, the candidate position coordinates suitable for building charging piles are screened out. When screening the position coordinates in the candidate coordinate set, multiple factors need to be considered comprehensively, such as the convenience of surrounding traffic (whether close to the road, etc.), the availability of land (whether it is idle land, whether it meets the requirements of urban planning, etc.), the power grid access conditions (distance from the power grid node, ease of access to the power grid, etc.). The weighted scoring method or other methods can be used to score each candidate coordinate, and finally determine the position coordinate of the supplementary charging pile to ensure that the newly added charging pile can meet the increase in charging demand caused by congestion to the greatest extent, and has feasibility in power grid access and construction conditions.

[0038] The position coordinate of the supplementary charging pile is incorporated into the coordinate set to obtain a charging pile layout scheme of the target area.

[0039] Specifically, the position coordinate of the supplementary charging pile determined by screening is 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 influence of historical traffic flow, power grid node capacity and potential congestion on charging demand and power grid, and through reasonable supplement of the position of charging pile, it can more effectively disperse the charging load, avoid voltage drop of local power grid node due to excessive load, thereby improving the utilization rate of charging piles in the whole target area, ensuring the safe and stable operation of the power grid, and improving the charging experience of users.

[0040] The charging pile layout optimization method, system and electronic equipment of the present application, by obtaining the historical traffic flow and power grid node capacity of the target area, and generating the coordinate set of the charging pile accordingly, provides key data support for subsequent analysis and optimization, ensuring that the initial layout takes into account the basic situation of traffic and power grid, determines the involved road sections based on the coordinate set of the charging pile, wherein the involved road sections are the necessary road sections of the navigation path within the influence range of each charging pile, ensuring the direct correlation of congestion analysis and charging demand, avoiding the interference of irrelevant congestion on charging pile layout optimization, improving the accuracy of congestion analysis, making the correlation between congestion and charging demand more closely, and using a traffic simulation model to determine whether there is potential congestion on these road sections at different time periods. By identifying congested road sections in advance, it can predict the problem of concentrated charging demand caused by vehicle congestion, providing a basis for subsequent targeted treatment. For road sections with potential congestion, determine the time-varying charging load curve by combining factors such as road length, electric vehicle proportion and single vehicle fast charging power, accurately quantify the charging load change under congestion, provide accurate data for evaluating power grid node voltage, and help to better understand the load of charging piles at different time periods. Map the time-varying charging load curve to the power grid node, calculate the node voltage and determine whether it is lower than the preset threshold, to determine the to-be-supplemented pile road section, directly link traffic flow changes with power grid performance, and clearly identify the specific location that needs to be optimized, realizing a complete evaluation chain from traffic to power grid. Generate a candidate coordinate set centered on the to-be-supplemented pile road section, and screen it to determine the position coordinates of the supplemented charging pile. By reasonably supplementing the layout of charging piles, it ensures that the newly added charging piles can effectively solve the problem of charging demand caused by congestion, while avoiding resource waste. Integrate the position coordinates of the supplemented charging pile into the original coordinate set to form the final charging pile layout scheme, realizing the optimization and upgrading of the original layout, ensuring the utilization rate of charging piles, improving user experience, and reducing the waste of power grid resources.

[0041] The present application realizes the optimization of charging pile layout through the close cooperation of each link, improves the utilization rate of charging piles, and at the same time ensures the stable operation of the power grid, effectively solving the problems of local power grid node voltage drop, charging pile power reduction or shutdown caused by the spatio-temporal clustering accumulation of vehicle charging demand in the prior art.

[0042] Optionally, the historical traffic flow and power grid node capacity of the target area are obtained, and the charging pile coordinate set in the target area is generated according to the historical traffic flow and the power grid node capacity, comprising: The target area is divided into grids, and the vehicle flow in each grid is determined according to the historical traffic flow and the preset time period; According to the vehicle flow of each grid, the net vehicle density of each grid in each time period is determined; obtaining node capacities of all the grid nodes in the target region; determining location coordinates of charging piles and mapping relationships between the charging piles and the grid nodes corresponding to the charging piles according to the node capacities of the grid nodes and the net vehicle density by a clustering-siting algorithm; obtaining the coordinate set according to the mapping relationships and the location coordinates.

[0043] Specifically, first, the target region is divided into grids. The grid size can be determined according to the geographical shape and size of the region, for example, a city region is divided into square grids with a side length of 500 meters. According to the historical traffic flow data collected, the number of vehicles passing through each grid is counted in a preset time period (for example, one hour), so as to determine the vehicle flow of each grid in different time periods. Then, the net vehicle density of the grid in each time period is calculated, which can be achieved by dividing the vehicle flow by the grid area, for example, a certain grid has 100 vehicles in a certain time period, and the grid area is 0.25 square kilometers, then the net vehicle density is 400 vehicles / square kilometer.

[0044] At the same time, the node capacity information of all the grid nodes in the target region is obtained, including the maximum power supply capacity of each grid node, the current load condition and other data. Then, a clustering-siting algorithm is used. The algorithm first clusters the grids according to the net vehicle density, and identifies the regions with high and concentrated vehicle density as potential charging pile layout hotspots. In the preferred embodiment of the present application, the clustering-siting algorithm is K-means algorithm. First, based on the grid division and net vehicle density data in the target region, K-means clustering algorithm is used to cluster analyze the regions with high vehicle density. K-means algorithm divides data points into K clusters, so that the data points in the cluster are as close to the cluster center as possible, and the distance between clusters is as far as possible. In this scenario, the net vehicle density of each grid is taken as a data point, and K-means algorithm is used to determine K cluster centers, which are the potential charging pile layout hotspots. Then, combined with the grid node capacity information, each cluster center is evaluated for site selection. The evaluation indexes include the electrical connection conditions between the cluster center and the surrounding grid nodes, the power supply capacity matching degree, etc. By calculating the distance from each cluster center to the nearest grid node and the remaining capacity of the grid node, the candidate positions that meet the power supply conditions are selected. Finally, according to the comprehensive score (such as the weighted sum of distance and capacity) of the candidate positions, the specific location coordinates of the charging piles are determined, and the mapping relationship between the charging piles and the corresponding grid nodes is established.

[0045] On the basis of clustering, combined with the capacity of the power grid node, the algorithm will evaluate the electrical connection conditions of each candidate location in the hotspot area and the surrounding power grid nodes, the matching degree of power supply capacity and other factors to determine the specific location coordinates of the charging pile and establish the mapping relationship between the charging pile and the corresponding power grid node, ensuring that the layout of the charging pile meets the traffic demand while also being within the power supply capacity of the power grid. Finally, according to the determined mapping relationship and location coordinates, the complete charging pile coordinate set is obtained.

[0046] In the embodiments of the present application, through grid division and vehicle flow analysis, the vehicle dense area can be accurately located, combined with the consideration of the capacity of the power grid node, to ensure that the charging pile layout not only meets the charging demand brought by traffic flow, but also avoids power supply problems caused by insufficient power grid capacity. The use of clustering-site selection algorithm improves the rationality and scientificity of the charging pile layout, enabling the charging pile to serve high-demand areas while effectively connecting with the power grid node, improving the utilization efficiency of power grid resources, reducing construction costs, and enhancing the feasibility and adaptability of the charging pile layout scheme.

[0047] Optionally, determining a plurality of involved road segments according to the coordinate set of the charging pile comprises: obtaining a navigation path connected to each charging pile; defining an influence range with the location coordinates of each charging pile as the center; screening the navigation path according to the influence range to obtain the navigation path must-pass road segment of each charging pile, and taking the navigation path must-pass road segment as the involved road segment.

[0048] Specifically, first, the coordinate set of the charging pile is combined with the vehicle navigation system (such as Gaode, Baidu Map API) to obtain the navigation path from the surrounding main traffic nodes (such as highway exits, commercial centers, residential areas, etc.) to each charging pile. These navigation paths reflect the actual driving route of the vehicle to the charging pile. Next, an influence range is defined with the location coordinates of each charging pile as the center. In the preferred embodiment of the present application, this range can be a circular area with a fixed radius (such as 1 kilometer), or a polygonal area, the specific shape of which is dynamically adjusted according to the road network and traffic flow distribution around the charging pile. Then, within the defined influence range, the obtained navigation paths are screened to extract the must-pass road segments on the navigation paths. These must-pass road segments are the paths that the vehicle must pass when going to the charging pile, and can truly reflect the driving route of the vehicle and the charging demand path. Finally, these navigation path must-pass road segments are taken as the involved road segments for subsequent congestion analysis and charging load prediction. In a preferred embodiment, the navigation path must-pass road segment is the road segment connected to the entrance of the charging pile.

[0049] In the embodiments of the present application, the actual service road of the charging pile is accurately locked by double constraints of spatial area and reachable time, avoiding missing high-frequency use road sections or including irrelevant roads, which not only compresses the subsequent simulation calculation amount, but also provides high credible input boundary for coupling analysis of charging load and traffic state, so that the layout optimization result is more close to real travel behavior and power grid operation constraints.

[0050] Optionally, the determining, by the traffic simulation model, whether all the involved road sections of each charging pile have potential congestion road sections in each preset time period comprises: inputting the historical vehicle speed and the historical traffic flow of each involved road section into the traffic simulation model, simulating by the traffic simulation model, and outputting the average vehicle speed of each involved road section in each preset time period; determining, according to the average vehicle speed, whether all the involved road sections of each charging pile have the potential congestion road sections; determining the electric vehicle flow in each involved road section according to a preset electric vehicle proportion coefficient; wherein, if the average vehicle speed of any involved road section in the preset time period is lower than a preset congestion vehicle speed, the time delay of the 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.

[0051] Specifically, historical vehicle speed and historical traffic flow data of each involved road section are collected, which can be obtained from a monitoring system of a traffic management department, intelligent traffic sensors, or historical traffic flow statistical reports. These data are arranged in time series format to match the time resolution required by the traffic simulation model. Then, these data are input into the traffic simulation model (such as commonly used SUMO, VISSIM, etc.). In the simulation model, parameters such as road network topology structure, traffic signal control logic, vehicle type distribution, etc. are set to match the actual situation to ensure the accuracy of the simulation results.

[0052] Then, the simulation model is run for simulation. During the simulation process, the model simulates the traffic flow operation of each involved road section in different preset time periods according to the input historical vehicle speed and traffic flow data. After the simulation is completed, the analysis is performed in combination with the time delay of the vehicle to the charging pile obtained by the simulation. The expected time of the vehicle from the involved road section to the charging pile is calculated through a navigation system or historical data, and is compared with the simulation arrival time. If the simulation arrival time delay exceeds a preset time threshold (for example, 15 minutes), it is further confirmed that the congestion of the road section has actually affected the charging demand.

[0053] Meanwhile, according to the preset electric vehicle proportion coefficient, the electric vehicle flow in each involved road section is calculated. The electric vehicle flow can be obtained by multiplying the total number of vehicles in the historical traffic flow data by the electric vehicle proportion coefficient. If the electric vehicle flow exceeds a preset flow threshold (for example, 30% of the total flow of the road section), it indicates that the congestion of the road section has a significant impact on the charging demand of the charging pile. That is, assuming that all electric vehicles in the congested road section have charging demand, the charging pile layout is performed based on this, so that the charging pile is set in a layout considering the maximum charging demand, which can ensure that different degrees of charging demand can be met.

[0054] In a preferred embodiment, since not all electric vehicles in the congested road section necessarily have charging demand, in order to avoid low utilization rate of the charging pile in daily use and thus avoid resource waste, the charging vehicle proportion in different time periods can be determined in advance. For example, the average value of the charging vehicle proportion of all electric vehicles in the different time periods of the navigation path that must pass through the existing charging piles in the target area is analyzed. The average value of the charging vehicle proportion, the electric vehicle proportion coefficient, and the total number of vehicles in the historical traffic flow data are multiplied to obtain the electric vehicle flow in the navigation path that must pass through the existing charging piles in the target area.

[0055] In a preferred embodiment, in order to improve the accuracy of the charging pile layout and further avoid resource waste, only the involved road sections corresponding to the existing charging piles in the target area can be analyzed. That is, for the involved road sections corresponding to the existing charging piles in the target area, whether there is congestion in the road section in different time periods can be analyzed through the historical traffic flow data, and subsequent pile supplementing processing is performed on the congested road section.

[0056] Finally, only when the above three conditions are met, i.e., the average vehicle speed is lower than the congestion vehicle speed threshold, the vehicle arrival time delay exceeds the preset time threshold, and the electric vehicle flow exceeds the preset flow threshold, the involved road section is determined as a potential congested road section in the preset time period. This comprehensive judgment method ensures that the identification of the congested road section is not only based on the vehicle speed, but also combined with the actual charging demand and the electric vehicle flow, thereby improving the accuracy and practicality of the congestion identification.

[0057] In the embodiments of the present application, by using the traffic simulation model to accurately simulate the traffic conditions of each involved road section in each preset time period, potential congested road sections can be found in advance. This method not only improves the accuracy of traffic congestion prediction, but also provides a key basis for subsequent charging pile layout optimization. By identifying potential congested road sections, the use demand and possible charging load pressure of the charging pile in different time periods can be more accurately evaluated, thereby helping to reasonably plan the layout and configuration of the charging pile, improve the utilization rate of the charging pile, reduce the impact on the power grid caused by the concentration of charging demand due to traffic congestion, enhance the scientificity and practicality of the charging pile layout scheme, and ensure that it better meets the actual traffic and charging demand.

[0058] Optionally, if the potential congestion road segment exists, according to the road segment length of the potential congestion road segment, the total number of vehicles stranded in the potential congestion road segment within the corresponding preset time period is determined, and in combination with a preset electric vehicle proportion coefficient and a single vehicle fast charging power, a time-varying charging load curve of the potential congestion road segment corresponding to the charging pile is determined, comprising: According to the average vehicle speed of the vehicle on the potential congestion road segment and the road segment length, the total number of vehicles stranded in the potential congestion road segment within the corresponding preset time period is determined; According to the preset electric vehicle proportion coefficient, the number of electric vehicles is screened out from the total number of stranded vehicles; The number of electric vehicles is multiplied by the single vehicle fast charging power to obtain the peak charging demand of the potential congestion road segment within the corresponding preset time period; According to the peak charging demand of the potential congestion road segment within the corresponding preset time period, the time-varying charging load curve of the charging pile in the preset time period is generated.

[0059] Specifically, first, the total number of stranded vehicles is determined according to the average speed and the length of the potential congestion road segment. Assuming that the estimated average vehicle density can be obtained according to historical data, different types of road segments and different time periods may have different densities. The formula for calculating the total number of stranded vehicles based on average speed (km / h) and road segment length (km) can be expressed as:

[0060] wherein the average length of the vehicle can be estimated according to the average length of the typical city vehicle, for example, about 5 meters. However, a more common method is to use the formula in traffic engineering, i.e. vehicle density (vehicles / km) is equal to 1000 divided by (average length of vehicle (m) + vehicle spacing (m)). In the case of congestion, the vehicle spacing is usually small, and the vehicle density can be approximated as high. For example, if the average speed is 10 km / h, the vehicle density obtained from the empirical formula or field measurement may be 120 vehicles / km, and when the road segment length is 2 km, the total number of stranded vehicles is 240 vehicles. Then, in combination with a preset electric vehicle proportion coefficient and a single vehicle fast charging power, a time-varying charging load curve is determined. Assuming that the preset electric vehicle proportion coefficient is 30% (i.e. 0.3) and the single vehicle fast charging power is 30kW, the number of electric vehicles is 240x0.3=72, and the peak charging demand is 72x30kW=2160kW. Finally, by connecting the peak charging demands of different time periods, the time-varying charging load curve of the charging pile can be generated.

[0061] If no potential congestion road segment is found during the analysis process, it is considered that the charging load of the charging pile within the preset time period is at a normal level, and the significant increase in charging demand due to traffic congestion will not occur. At this time, the charging load of the charging pile is mainly determined by the daily charging demand, which can be estimated through historical charging data. The system marks the charging pile as "low risk" state, and in the subsequent layout optimization, it focuses on other charging piles with potential congestion risk. This processing method ensures the comprehensiveness and pertinence of the optimization process, avoids over-optimization of charging piles without congestion risk, and concentrates resources to solve key problems that may affect the stability of the power grid and user experience.

[0062] In the embodiments of the present application, by accurately calculating the total number of stranded vehicles on potential congestion road segments, and combining the proportion of electric vehicles and the single vehicle fast charging power, the peak charging demand of the charging pile in different time periods can be predicted in detail. This method not only improves the accuracy of charging load prediction, but also provides a key basis for subsequent power grid node voltage evaluation and charging pile layout optimization. By identifying the peak charging load in different time periods, the capacity and layout of the charging pile can be more reasonably planned, avoiding the problem of power grid voltage drop caused by concentrated charging demand, thereby improving the utilization rate of the charging pile, improving user experience, and ensuring the safe and stable operation of the power grid.

[0063] Optionally, the mapping of the time-varying charging load curve to the power grid node corresponding to the charging pile to obtain the node voltage of the power grid node comprises: According to the mapping relationship between the charging pile and the power grid node corresponding to the charging pile, the time-varying charging load curve in the preset time period is superimposed on 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, the power flow calculation of the power grid node is performed to obtain the node voltage of the power grid node in the preset time period.

[0064] Specifically, first, according to the pre-established mapping relationship between the charging pile and the grid node, the grid node connected by each charging pile is determined. The time-varying charging load curve in the preset time period is superimposed on the corresponding grid node, and the historical average load of the grid stage in the preset time period is added to form the time period load of the grid node. This step needs to convert the charging load into the load increment of the grid node, considering the electrical connection parameters between the charging pile and the grid node, such as line impedance, transformer capacity, etc. Next, power system analysis software (such as MATPOWER, PSSE, etc.) is used for power flow calculation. Input the time period load of the grid node, the grid topology structure, the line parameters and other data, and run the power flow calculation algorithm. The power flow calculation will simulate the flow of electricity in the grid, and according to Kirchhoff's law and Ohm's law, the node voltage of each grid node in the preset time period is calculated.

[0065] In the embodiment of the application, by mapping the time-varying charging load curve of the charging pile to the grid node and performing power flow calculation, the influence of the charging load on the voltage of the grid node can be accurately evaluated. This method helps to identify in advance the grid nodes that may cause voltage drop due to increased charging load, thereby providing a scientific basis for optimizing the layout of charging piles and upgrading the grid. By reasonably planning the location and capacity of the charging pile, the problem of low voltage of the grid node can be effectively avoided, the power supply quality and stability of the grid can be improved, the reliable operation of the charging pile can be ensured, the user experience can be improved, and the waste of grid resources can be reduced.

[0066] Optionally, the candidate coordinate set is generated with the to-be-supplemented pile road section as the center, and the position coordinates of the candidate coordinate set are screened to determine the position coordinates of the supplementary charging pile, including: A candidate area of the supplementary charging pile is formed by extending a preset radius range outward from the geometric center of the to-be-supplemented pile road section as the origin; A plurality of candidate coordinate points are generated in the candidate area according to a preset step length; The residual capacity of the grid node corresponding to each candidate coordinate point is obtained, and the candidate coordinate points are preliminarily screened according to the residual capacity to obtain residual candidate coordinate points; Through a multi-objective screening mechanism, the residual candidate coordinate points are sorted according to the minimum distance from the residual candidate coordinate points to the to-be-supplemented pile road section, the distance between the residual candidate coordinate points and other charging piles within a preset range, and the residual capacity margin of the grid node; The position coordinates of the residual candidate coordinate points are selected from the sorting result as the position coordinates of the supplementary charging pile.

[0067] Specifically, taking the geometric center of the road section to be supplemented as the origin, a circular candidate area is formed by extending outward with a preset radius (such as 1 km) using a geographic information system tool. Within this area, grid points are generated as candidate coordinate points at a preset step size (such as every 50 meters). Further, terrain and land availability are combined for screening: terrain data is analyzed through GIS to exclude areas with a slope exceeding a certain threshold (such as 10%) to avoid construction on steep slopes or unsuitable terrain; at the same time, land use planning data is combined to screen out land that meets urban planning requirements and is available, such as idle land, public facility land, etc., to ensure the feasibility of the candidate coordinate points. Finally, suitable candidate coordinate points are selected from the grid points that meet the conditions for subsequent charging pile layout optimization. The remaining capacity data of the power grid node corresponding to each candidate coordinate point is obtained through the power grid API, and points with insufficient remaining capacity are excluded to complete the preliminary screening. Then a multi-objective screening mechanism is constructed, considering the minimum distance from the remaining candidate coordinate points to the road section to be supplemented (the closer the distance, the higher the priority), the spacing from the surrounding charging piles (the greater the spacing, the higher the priority to avoid excessive concentration), and the power grid node remaining capacity margin (the greater the margin, the higher the priority), and the remaining candidate coordinate points are sorted using a weighted summation method. Finally, the optimal remaining candidate coordinate point is selected from the sorting result as the position coordinate of the supplementary charging pile.

[0068] In the embodiments of the present application, candidate coordinate sets are generated by terrain and power grid dual constraints, combined with multi-dimensional screening indicators to ensure that the supplementary charging piles can not only be close to the road section to be supplemented with strong demand, but also guarantee reasonable spatial distribution and power grid access conditions. The spatial analysis function of GIS and the deep integration of power grid data improve the scientificity and accuracy of layout decision-making, enabling the newly added charging piles to efficiently share the load pressure of the original charging piles, alleviate the power grid node voltage drop problem, and improve the service level and power grid operation efficiency of the overall charging network.

[0069] Optionally, the method further comprises: adding the position coordinate of the supplementary charging pile to the coordinate set; updating the mapping relationship between the charging piles in the coordinate set and the power grid nodes according to the mapping relationship between the supplementary charging pile and the power grid nodes to obtain the charging pile layout scheme of the target area.

[0070] Specifically, to add the location coordinates of the supplementary charging piles to the coordinate set, the location coordinates of the supplementary charging piles obtained from the external storage device or the user input interface need to be read first, their formats need to be adjusted to a unified form, such as latitude and longitude coordinate format (latitude, longitude), and then these supplementary coordinates need to be added to the data structure of the original coordinate set, which can be a list, an array or a database table, etc., through program logic or manual operation, to ensure that all charging pile coordinates are stored and managed in the same data collection. In terms of updating the mapping relationship, according to the specific connection of the supplementary charging pile, the corresponding power grid node is determined. This needs to investigate the field or coordinate with the power grid department to obtain the power grid access point information of the supplementary charging pile, and to clarify its connection relationship with the power grid node. Then, the data structure or database operation is used to establish the mapping relationship between the supplementary charging pile and the corresponding power grid node. For example, in the database, there can be a mapping table containing charging pile ID, location coordinates and corresponding power grid node ID fields. Inserting these information of the supplementary charging pile into the mapping table completes the update of the mapping relationship.

[0071] In the embodiment of the application, by incorporating the location coordinates of the supplementary charging pile into the original coordinate set, a complete charging pile layout scheme is formed, providing accurate basic data for subsequent operation management. At the same time, the updated mapping relationship helps to accurately assess the impact of the supplementary charging pile on the power grid node, ensuring the safe and stable operation of the power grid. The integrated layout scheme can more scientifically guide the construction of charging piles, improve resource utilization, meet the charging needs of users, and improve the overall service quality of charging facilities and the satisfaction of users.

[0072] In combination with Figure 2 As shown in FIG. 1, a charging pile layout optimization system of the application comprises: 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 the involved road sections of each charging pile through a traffic simulation model; A load calculation module is configured to, if the potential congestion road section exists in the involved road section, determine a total number of stranded vehicles in the corresponding preset time period for the potential congestion road section according to a 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 a 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 pile-to-be-supplemented road section if the node voltage of the power grid node is lower than a preset voltage threshold. A pile-to-be-supplemented planning module is configured to generate a candidate coordinate set with the pile-to-be-supplemented road section as a center, and screen position coordinates in the candidate coordinate set to determine position coordinates of a supplementary charging pile. 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 region.

[0073] The charging pile layout optimization system of the present application has the same advantages as the charging pile layout optimization method of the present application, which will not be repeated here.

[0074] The electronic device of the present application comprises 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 as described above when executing the computer program.

[0075] The electronic device of the present application has the same advantages as the charging pile layout optimization method of the present application, which will not be repeated here.

[0076] Although the present application is disclosed as above, the protection scope of the present application is not limited to this. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, and these changes and modifications will fall within the protection scope of the present application.

Claims

1. A charging pile layout optimization method, characterized in that: include: Obtaining historical traffic flow and grid node capacity of a target area, and generating a coordinate set of charging piles within the target area based on the historical traffic flow and the grid node capacity; Determining a plurality of involved road sections according to the coordinate set of the charging pile; wherein the involved road sections are necessary road sections of the navigation path within the influence range of each charging pile; Determine whether there is a potential congested section in each preset time period on all the road sections involved in each charging pile through a traffic simulation model; If the involved road section includes the potential congested section, the total number of vehicles stranded on the potential congested section within the corresponding preset time period is determined based on the length of the potential congested section, and the time-varying charging load curve of the charging pile corresponding to the potential congested section is determined in combination with the preset electric vehicle ratio coefficient and the single-vehicle fast charging power; 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; if the node voltage of the grid node is lower than a preset voltage threshold, determining that the potential congested road section is a road section to be charged; Generate a candidate coordinate set with the road section to be supplemented as the center, and screen the position coordinates in the candidate coordinate set to determine the position coordinates of the supplementary charging pile; The location coordinates of the supplementary charging piles are incorporated into the coordinate set to obtain a charging pile layout plan for the target area.

2. The charging pile layout optimization method according to claim 1, characterized in that: The acquiring of 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 the grid node capacity, includes: Dividing the target area into grids, and determining the vehicle flow within each grid according to a preset time period based on the historical traffic flow; Determining the net vehicle density of the grid in each time period according to the vehicle flow of each grid; Obtaining node capacities of all the power grid nodes in the target area; Determining the location coordinates of the charging pile and the mapping relationship between the charging pile and the grid node corresponding to the charging pile based on the node capacity and the net vehicle density of the grid node through a clustering-site selection algorithm; The coordinate set is obtained according to the mapping relationship and the position coordinates.

3. The charging pile layout optimization method according to claim 2, characterized in that: The determining of a plurality of involved road sections according to the coordinate set of the charging pile includes: Obtaining a navigation path connected to each of the charging piles; Defining the influence range with the position coordinates of each charging pile as the center; According to the impact range, the navigation path is screened to obtain the necessary sections of the navigation path for each charging pile, and the necessary sections of the navigation path are used as the involved sections.

4. The charging pile layout optimization method according to claim 1, characterized in that: The determining, using a traffic simulation model, whether there is a potential congested section on all the road sections involved in each charging pile within each preset time period includes: Inputting the historical vehicle speed and the historical traffic flow of each of the involved road sections into the traffic simulation model, performing simulation through the traffic simulation model, and outputting the average vehicle speed of each of the involved road sections in each of the preset time periods; Determining, based on the average vehicle speed, whether all the involved road sections of each charging pile contain the potential congested road section; Determining the tram flow in each of the involved road sections according to a preset tram proportion coefficient; Among them, if the average vehicle speed of any of the involved sections within the preset time period is lower than the preset congestion speed, and the time delay for vehicles on the involved section to reach the charging pile exceeds the preset time threshold, and the electric vehicle flow exceeds the preset flow threshold, then the involved section is determined to be a potential congested section within the preset time period.

5. The charging pile layout optimization method according to claim 4, characterized in that: If the involved road section includes the potential congested section, the total number of vehicles stranded in the potential congested section within the corresponding preset time period is determined based on the length of the potential congested section, and the time-varying charging load curve of the charging pile corresponding to the potential congested section is determined in combination with the preset electric vehicle ratio coefficient and the single-vehicle fast charging power, including: Determining the total number of stranded vehicles on the potentially congested road section within the corresponding preset time period based on the average vehicle speed of the vehicles on the potentially congested road section and the length of the road section; According to the preset electric vehicle proportion coefficient, the number of electric vehicles is screened out from the total number of stranded vehicles; Multiplying the number of electric vehicles by the single-vehicle fast charging power to obtain the peak charging demand of the potential congested road section within the corresponding preset time period; 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 congested road section within the corresponding preset time period.

6. The charging pile layout optimization method according to claim 2, characterized in that: 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 includes: According to the mapping relationship between the charging pile and the grid node corresponding to the charging pile, the time-varying charging load curve within the preset time period is superimposed on the grid node corresponding to the charging pile to form the time period load of the grid node; The power flow calculation is performed on the power grid node according to the time period load of 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 generating of a candidate coordinate set with the road section to be supplemented as the center, and screening the position coordinates in the candidate coordinate set to determine the position coordinates of the supplementary charging pile includes: A candidate area for the additional charging pile is formed by extending a preset radius outward from the geometric center of the road section to be supplemented with charging piles as the origin; In the candidate area, generating multiple candidate coordinate points according to a preset step size; Obtaining the remaining capacity of the 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; The remaining candidate coordinate points are sorted by a multi-objective screening mechanism based on the minimum distance between the remaining candidate coordinate points and the road section to be filled with charging piles, the distance between the remaining candidate coordinate points and other charging piles within a preset range, and the remaining capacity margin of the grid node; The position coordinates of the remaining candidate coordinate points are selected from the sorting results as the position coordinates of the supplementary charging pile.

8. The charging pile layout optimization method according to claim 7, characterized in that: The step of incorporating the location coordinates of the supplementary charging pile into the coordinate set to obtain a charging pile layout plan for the target area includes: Adding the location coordinates of the supplementary charging pile to the coordinate set; The mapping relationship between the charging piles in the coordinate set and the above-mentioned grid nodes is updated according to the mapping relationship between the supplementary charging piles and the grid nodes to obtain the charging pile layout plan of the target area.

9. A charging pile layout optimization system, characterized in that: include: a data acquisition module, configured to acquire historical traffic flow and grid node capacity of a target area, and generate a coordinate set of charging piles within the target area based on the historical traffic flow and the grid node capacity; a road section determination module, configured to determine a plurality of involved road sections based on the coordinate set of the charging pile; wherein the involved road sections are necessary road sections of the navigation path within the influence range of each charging pile; A congestion judgment module, configured to judge whether there is a potential congested section in all the sections involved in each charging pile within each preset time period through a traffic simulation model; a load calculation module configured to determine, if the potential congested section exists on the involved road section, the total number of vehicles stranded on the potential congested section within the corresponding preset time period based on the length of the potential congested section, and determine, in combination with a preset electric vehicle ratio coefficient and a single-vehicle fast charging power, a time-varying charging load curve corresponding to the charging pile of the potential congested section; a voltage evaluation module, configured to map 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 if the node voltage of the grid node is lower than a preset voltage threshold, determining that the potentially congested road section is a section to be charged; A charging pile planning module is used to generate a candidate coordinate set with the road section to be supplemented as the center, and to screen the position coordinates in the candidate coordinate set to determine the position coordinates of the supplementary charging piles; A solution generation module is used to incorporate the location coordinates of the supplementary charging piles into the coordinate set to obtain a charging pile layout solution for the target area.

10. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the charging pile layout optimization method according to claim 8 when executing the computer program.

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