Charging-demand-oriented city charging facility constant volume planning visualization system

Abstract

The invention discloses a charging demand-oriented city charging facility constant volume planning visualization system, which comprises a demand prediction module, which combines the market sales volume, vehicle performance parameters and regional driving characteristics of all electric vehicle types, and constructs an electric quantity demand estimation model in a vehicle type, version, region and season division mode; obtaining a predicted charging demand; a capacity optimization configuration module introduces a multi-objective optimization framework to construct a charging facility constant volume planning model on the basis of the predicted charging demand, and realizes optimization configuration under the condition of satisfying multiple constraints; and the display module constructs an interactive visual interface, and displays an output result of the charging facility constant volume planning model and auxiliary decision analysis. According to the invention, through key technologies such as multi-source data fusion, demand modeling and dynamic simulation, accurate planning and optimal configuration of the capacity of the charging facility are realized. Meanwhile, a three-dimensional visualization function is integrated to visually display a planning result, and the understanding and decision-making efficiency of the scheme is improved.

Description

Technical Field

[0001] This invention belongs to the field of new energy infrastructure planning technology, specifically relating to a visualization system for urban charging facility capacity planning oriented towards charging demand. Background Technology

[0002] Currently, urban charging infrastructure planning mainly adopts two technical approaches: one is the allocation ratio estimation method based on macro indicators such as urban population, vehicle ownership, and land use; the other is the demand forecast-driven optimization method based on the integration of transportation, power system, and geographic information. The former quickly generates facility configuration schemes by setting fixed vehicle-to-charging-pile ratios or zoning allocation coefficients, which is simple to operate, low in cost, and suitable for the early layout stage. However, many problems have been exposed in the actual implementation process: First, the lack of characterization of micro-behavioral characteristics such as the heterogeneity of electric vehicle models and differences in power consumption leads to significant deviations in demand intensity estimation. For example, data from the main urban area of ​​a certain city in 2023 shows that the average annual charging volume of model A and model B differs by three times. If a uniform vehicle-to-charging-pile ratio is adopted, it is very easy to overload charging resources in the main urban area while wasting resources in the suburbs. Second, the actual physical carrying capacity and geographical boundary limitations of the power distribution network are not considered, often resulting in a situation where the planned scheme is "sufficient in total amount," but local areas are frequently overloaded in actual operation, affecting power supply security. Third, the planning results lack a dynamic evolution mechanism and cannot respond to changes in spatial and temporal demand caused by new energy vehicle promotion policies, electricity price fluctuations, or the development of charging technology, which can easily lead to layout lags or excess capacity.

[0003] In contrast, the second type of method emphasizes multi-source data fusion and dynamic optimization. It typically combines traffic simulation, power grid simulation, and metaheuristic algorithms (such as NSGA-II and MOEA / D) to construct a multi-objective optimization model, achieving coordinated planning for charging facility site selection, capacity determination, and power grid operation. This type of method has advantages in theoretical modeling accuracy and system adaptability, and can more comprehensively reflect the coupling relationship between urban traffic, power, and spatial elements. However, it still faces many challenges in practical applications. First, the model structure is highly complex, and the parameter settings are highly sensitive, making them susceptible to input disturbances. This results in insufficient consistency and reusability of planning results across different cities or time periods, limiting the model's application in cross-regional scenarios. Second, existing capacity planning models often employ linear simplification or static assumptions when facing multiple constraints such as power grid physical constraints, policy guidance, and regional equity. This makes it difficult to fully characterize the coupling relationship and dynamic changes between constraints, resulting in insufficient adaptability and scalability of the model in different regions and at different development stages. Finally, the presentation of results by existing optimization platforms remains relatively simplistic, generally limited to two-dimensional map overlays or static charts. This fails to intuitively demonstrate the multi-dimensional coupling relationship between grid capacity limitations, land suitability, and construction costs, hindering multi-departmental collaborative decision-making and real-time dynamic adjustments. Therefore, there is an urgent need to construct an optimization framework that combines stability, versatility, and strong visualization capabilities to support the transformation and upgrading of urban charging infrastructure from "data-driven" to "cognitive collaboration."

[0004] In summary, current urban charging infrastructure planning still faces systemic technical bottlenecks in theoretical modeling, charging facility capacity planning model design, and system integration capabilities. Therefore, it is urgent to construct a high-precision modeling system oriented towards charging demand, integrate capacity planning methods with multi-source data and regional characteristics, and support this with a high-performance visualization platform to promote the transformation and upgrading of urban charging infrastructure planning from "static estimation" to "dynamic coordination." Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a visualization system for urban charging facility capacity planning oriented towards charging demand. Through key technologies such as multi-source data fusion, demand modeling, and dynamic simulation, it achieves accurate planning and optimal configuration of charging facility capacity. Simultaneously, the system integrates 3D visualization capabilities, enabling intuitive display of planning results, improving the understanding of solutions and decision-making efficiency, and effectively solving problems such as insufficient response to charging demand, unreasonable capacity configuration, and unintuitive presentation of results in traditional manual planning.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention provides a visualization system for urban charging facility capacity planning oriented towards charging demand, comprising:

[0008] The seasonal charging demand prediction module based on vehicle type and regional characteristics is used to combine the market sales, vehicle performance parameters and regional driving characteristics of all electric vehicle models, and to build a power demand estimation model by vehicle type, version, region and season to obtain the predicted charging demand.

[0009] The charging facility capacity optimization and configuration module based on power grid and regional constraints is used to introduce a multi-objective optimization framework based on the predicted charging demand. It constructs a charging facility capacity planning model for urban scale based on constraints of cost control, power grid load balance and regional fairness, and achieves optimal configuration under multiple constraints.

[0010] The visualization and interactive display module for city-level charging planning results: Based on GIS spatial data and a Web 3D engine, an interactive visualization interface is built to display the output results of the charging facility capacity planning model and to assist in decision analysis.

[0011] Preferably, the seasonal charging demand prediction module based on vehicle type and regional characteristics performs the following operations:

[0012] Vehicle Model Identification and Version Classification: This feature integrates data on all new energy passenger vehicles currently on the market, including pure electric vehicles, plug-in hybrid electric vehicles, and range-extended electric vehicles. By collecting sales data for each model over historical periods, and combining this data with manufacturer announcements, product catalogs, and technical parameters, the feature identifies the specific version, powertrain configuration, and range of each model. This constructs a unified vehicle model-version mapping system, providing comprehensive data support for subsequent demand calculations. Here, "model" refers to the name of a specific vehicle product or series under a brand, while "version" represents different performance configurations within the same model.

[0013] Construction of Seasonal Charging Demand Calculation Model: For the i-th version of vehicle model j, determine the sales proportion γ of the i-th version of vehicle model j. i Based on sales share γ i And the sales volume of model j within a preset time period. j Calculate the total number of vehicles with the i-th version remaining;

[0014] Based on the pre-acquired battery energy of the i-th version of the vehicle. i And the NEDC range parameters i Calculate the theoretical power consumption per unit mileage for the i-th version of the vehicle;

[0015] Calculate the average annual mileage of vehicles in city k. kBased on the theoretical power consumption per unit mileage of the i-th version of the vehicle and the average annual mileage of the vehicle in the k-th city, calculate the total annual power consumption of the i-th version of the vehicle in the k-th city.

[0016] Introducing the spring and autumn energy consumption correction factor λ i And the summer and winter energy consumption correction coefficient ρ i Establish separate charging demand models for spring and autumn, and summer and winter:

[0017] Spring and Autumn Charging Demand Model:

[0018]

[0019] Where, ω i This represents the annual charging demand for the i-th version of the vehicle during the spring and autumn seasons.

[0020] Summer and winter charging demand model:

[0021]

[0022] Among them, v i This indicates the annual charging requirements for version i during the summer and winter seasons.

[0023] Calculation of annual total charging demand for a single vehicle model: Summarize the charging demand in different seasons to obtain the annual total charging demand (Ω) for a single version of the vehicle model. i :

[0024] Ω i =ω i +v i ,#(3)

[0025] Further calculate the total annual charging demand Θ for vehicle model j. j :

[0026]

[0027] Where l represents the total number of versions under model j;

[0028] Estimated Annual Total Charging Demand for a City: Based on the calculation results of the annual total charging demand, the total annual charging demand E for all vehicle types in city k is calculated. k :

[0029]

[0030] Where m represents the total number of vehicle types in city k;

[0031] Right now:

[0032]

[0033] Preferably, the charging facility capacity optimization configuration module based on grid and regional constraints performs the following operations:

[0034] A charging facility capacity planning model is constructed with the optimization objectives of total cost and grid load balance. The model constraints and objective function are as follows:

[0035] The objective functions include minimizing the total lifecycle cost of charging infrastructure and minimizing grid load fluctuations from a quarterly perspective;

[0036] The method for minimizing the total lifecycle cost of charging infrastructure includes:

[0037] Determine the initial investment cost C of a single pile in city administrative district i. cap,i And the annual operation and maintenance cost per pile C om,i And calculate the total life cycle cost of a single charging pile based on the pre-set capital recovery factor (CRF);

[0038] Based on the total number N of administrative districts in a city, the number of charging piles in each administrative district, and the total life cycle cost of a single charging pile, a calculation model minf1 is constructed to minimize the total life cycle cost of the charging facility. The formula is as follows:

[0039]

[0040] Where, x i The number of charging piles in city administrative district i;

[0041] The Capital Recovery Factor (CRF) is calculated based on the project life of the charging equipment and the discount rate. The calculation formula is as follows:

[0042]

[0043] Where r is the discount rate and n is the project life;

[0044] Based on the total grid load in the s-th quarter s In addition to the annual average load AvgLoad, a model minf2 is established to minimize the degree of power grid load fluctuation from a quarterly perspective. The formula is as follows:

[0045]

[0046] Among them, the total grid load in the s-th quarter is Load s The calculation is determined based on the average charging power per charging pile and the grid foundation load excluding charging piles. The calculation formula is as follows:

[0047]

[0048] Where, p i,sThe average charging power per charging pile for city administrative district i in the s quarter. The grid base load for the s-th quarter does not include charging piles;

[0049] Based on the total grid load in the s-th quarter s Calculate the annual average load (AvgLoad):

[0050]

[0051] The constraints include: total number of charging piles, quarterly charging demand satisfaction, quarterly grid load ceiling, regional allocation fairness, regional charging pile density, and priority guarantee for key areas.

[0052] The constraint on the total number of charging piles is expressed by the following formula:

[0053]

[0054] Among them, X total This refers to the total number of charging stations in the city.

[0055] Quarterly charging demand meets constraints, as shown below:

[0056]

[0057] Where, λ i,s Let ω be the average charging amount per charging pile in the city's administrative district during the s quarter, and let ω be the total charging demand of the entire city during the ω quarter.

[0058] The quarterly power grid load ceiling constraint is as follows:

[0059]

[0060] Among them, GridMargin s This represents the additional charging load that the power grid can handle in the s-th quarter;

[0061] The fairness constraints for allocating charging station distribution areas based on population size are as follows:

[0062]

[0063] Among them, D i Let D be the population of city administrative district i. total δ represents the total population of the city, and δ is the allocation deviation threshold.

[0064] The density of charging stations in a given area is constrained as follows:

[0065]

[0066] Among them, Area i Let α be the area of ​​city administrative district i, and α be the minimum charging pile density.

[0067] Priority protection and constraints are applied to key areas, as shown below:

[0068]

[0069] Where, x i Let β be the number of charging piles in urban administrative district i, and β be the allocation coefficient for key areas.

[0070] Finally, a charging facility capacity optimization configuration scheme that meets the above constraints and objective function is obtained.

[0071] Preferably, the visualization and interactive display module for city-level charging planning results, based on GIS spatial data and a Web 3D engine, constructs an interactive visualization interface to display the output results of the charging facility capacity planning model and assist in decision analysis, specifically performing the following operations:

[0072] 3D map display: Based on the city's administrative divisions, the density of charging piles, the utilization rate of power grid capacity, and the supply and demand matching rate are overlaid to generate an intuitive and interactive 3D visualization map.

[0073] Dynamically linked data charts: including bar charts, heatmaps, and pie charts, supporting switching and comparative analysis across different time and regional dimensions;

[0074] Multiple versions of planning schemes can be displayed and compared: Supports the display of multiple versions of planning results under different model parameter settings, assisting policymakers in conducting scenario simulations and sensitivity analyses;

[0075] User interaction feedback mechanism: Integrates site selection suggestion feedback and demand popularity collection functions for future model optimization input.

[0076] Preferably, it also includes adding an expansion module, which performs the following operations:

[0077] Obtain the occupancy time ratio of charging piles at each site under the current urban charging facility capacity planning, and generate a first spatial interpolation heat map on the visualization interface based on the occupancy time ratio.

[0078] Obtain the average daily distribution of vehicles in various areas of the city, and generate a second spatial interpolation heatmap on the visualization interface based on the vehicle distribution density;

[0079] Based on spatial overlay analysis, the first spatial interpolation heatmap and the second spatial interpolation heatmap are combined, and the following operations are performed:

[0080] Calculate local R using the geographically weighted regression model GWR 2 Using local R 2 Identify areas where vehicle distribution density strongly influences charging pile occupancy rate;

[0081] Ripley's K-function is used to calculate the cross K-function between vehicle distribution density and charging pile occupancy, analyze and determine the clustering correlation between vehicle distribution density and charging pile occupancy within a specific radius, and determine the maximum influence distance of vehicle distribution density on charging pile occupancy.

[0082] Based on the strong influence area and the maximum influence distance, an additional area is generated with the strong influence area as the core and the maximum influence distance as the expansion radius.

[0083] The addition of charging stations can be based on the division of the area to be added, or charging stations can be added by selecting charging stations.

[0084] Preferably, adding charging stations based on the division of the added area or selecting charging stations to add charging piles includes:

[0085] Based on the newly added areas, statistics were compiled on each charging station within the added areas and the percentage of time each charging station was occupied.

[0086] When the occupancy time of any charging station exceeds a preset value, it will be marked as a busy station.

[0087] The K-center algorithm is used to calculate the spatial location of busy stations in the newly added area to obtain the locations of K different cluster centers.

[0088] Each cluster center is used as an additional charging station location. The busiest station closest to the charging station is determined, and the straight-line distance between the busiest station and the charging station is calculated. If the distance is less than or equal to a preset distance threshold, the busiest station is selected to add the charging station.

[0089] If the distance is greater than the preset distance threshold, then the location to be added will be selected to add a charging station and a charging pile.

[0090] Preferably, the K-center point algorithm includes:

[0091] Step S1: Based on the area size of the added area and the preset table corresponding to the area of ​​the added area and the maximum number of added sites, determine the maximum number K of allowed added sites.

[0092] Step S2: Randomly select K busy stations from all busy stations in the added area as initial cluster centers;

[0093] Step S3: Calculate the distance between each busy site and all cluster centers, and assign each busy site to the nearest cluster center;

[0094] Step S4: For each cluster, calculate its centroid.

[0095] Step S5: Calculate the maximum distance from all busy stations to their center point in each cluster;

[0096] Step S6: If the maximum distance of each cluster is less than or equal to a predetermined threshold, the algorithm stops and outputs the cluster center locations of K different categories; otherwise, the busiest station with the farthest distance is taken as the new cluster center, and steps S3 to S6 are executed again.

[0097] Preferably, when adding charging piles to busy charging stations or newly established charging station sites, the following operations are performed:

[0098] Determine K distinct clusters, and the total occupancy time of all charging piles at all charging pile sites within each cluster;

[0099] Based on the total time occupied by each of the K clusters, weights are assigned to the K clusters.

[0100] Based on the weight of the clusters and the total number of charging piles to be added in advance, the number of charging piles allocated to each cluster is determined.

[0101] Based on the quantity quota, additional charging piles will be added to busy sites and newly established charging pile sites.

[0102] The beneficial effects of this invention are as follows:

[0103] 1. The system takes the charging demand of electric vehicles as the core driving factor, and combines the urban power grid capacity constraints and the high uncertainty of charging behavior in time and space. Through key technologies such as multi-source data fusion, demand modeling and dynamic simulation, it realizes the accurate planning and optimal configuration of charging facility capacity.

[0104] 2. The system integrates 3D visualization function, which can intuitively display planning results, improve the understanding of the scheme and decision-making efficiency, and effectively solve problems such as insufficient response to charging demand, unreasonable capacity configuration and unintuitive presentation of results in traditional manual planning.

[0105] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0106] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0107] Figure 1 This is a schematic diagram of a visualization system for urban charging facility capacity planning based on charging demand, as described in this invention.

[0108] Figure 2 Here is a flowchart of the steps for adding a region generation method in this invention;

[0109] Figure 3 This is a flowchart of the K-center point algorithm in this invention. Detailed Implementation

[0110] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0111] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0112] Unless otherwise stated, the term "multiple" means two or more.

[0113] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0114] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0115] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0116] To achieve the above objectives, the present invention provides the following technical solution, with reference to... Figures 1 to 3 :

[0117] This invention provides a visualization system for urban charging facility capacity planning based on charging demand, referring to... Figure 1 ,include:

[0118] The seasonal charging demand prediction module based on vehicle type and regional characteristics is used to combine the market sales, vehicle performance parameters and regional driving characteristics of all electric vehicle models, and to build a power demand estimation model by vehicle type, version, region and season to obtain the predicted charging demand.

[0119] The charging facility capacity optimization and configuration module based on power grid and regional constraints is used to introduce a multi-objective optimization framework based on the predicted charging demand. It constructs a charging facility capacity planning model for urban scale based on constraints of cost control, power grid load balance and regional fairness, and achieves optimal configuration under multiple constraints.

[0120] The visualization and interactive display module for city-level charging planning results: Based on GIS spatial data and a Web 3D engine, an interactive visualization interface is built to display the output results of the charging facility capacity planning model and to assist in decision analysis.

[0121] The working principle and beneficial effects of the above technical solution are as follows: This invention constructs a charging demand prediction model based on different vehicle models, integrates sales data, energy consumption parameters, and regional characteristics, accurately depicts the seasonal electric vehicle charging load demand in different regions, and improves the accuracy and adaptability of demand-side modeling; it introduces a multi-objective optimization framework, comprehensively considering cost control, grid load balance, and regional fairness, and constructs a city-scale charging facility capacity planning model to achieve optimal configuration under multiple constraints such as grid capacity and policy guidance; the system adopts a modular design and standardized data interface, supporting efficient coupling and rapid iterative updates of demand data and planning models; a visualization platform is built based on a 3D GIS engine to realize multi-dimensional linkage display of information such as the spatial distribution of planning schemes, grid utilization, and supporting costs, significantly improving the understandability of model results and decision support capabilities. This system can effectively solve the problems of "demand-capacity disconnect," "layout-grid incoordination," and "unintuitive results-expression" existing in charging infrastructure planning, and is applicable to the layout planning of electric vehicle charging infrastructure in multiple regions and types of cities, with good versatility, scalability, and promotional application value.

[0122] In a preferred embodiment, the seasonal charging demand prediction module based on vehicle type and regional characteristics specifically performs the following operations:

[0123] Vehicle Model Identification and Version Classification: This feature integrates data on all new energy passenger vehicles currently on the market, including pure electric vehicles, plug-in hybrid electric vehicles, and range-extended electric vehicles. By collecting sales data for each model over historical periods, and combining this data with manufacturer announcements, product catalogs, and technical parameters, the feature identifies the specific version, powertrain configuration, and range of each model. This constructs a unified vehicle model-version mapping system, providing comprehensive data support for subsequent demand calculations. Here, "model" refers to the name of a specific vehicle product or series under a brand, while "version" represents different performance configurations within the same model.

[0124] Construction of Seasonal Charging Demand Calculation Model: For the i-th version of vehicle model j, determine the sales proportion γ of the i-th version of vehicle model j. i Based on sales share γ i And the sales volume of model j within a preset time period. j Calculate the total number of vehicles with the i-th version remaining;

[0125] Based on the pre-acquired battery energy of the i-th version of the vehicle. i And the NEDC range parameters i Calculate the theoretical power consumption per unit mileage for the i-th version of the vehicle;

[0126] Calculate the average annual mileage of vehicles in city k. k Based on the theoretical power consumption per unit mileage of the i-th version of the vehicle and the average annual mileage of the vehicle in the k-th city, calculate the total annual power consumption of the i-th version of the vehicle in the k-th city.

[0127] Introducing the spring and autumn energy consumption correction factor λ i And the summer and winter energy consumption correction coefficient ρ i Establish separate charging demand models for spring and autumn, and summer and winter:

[0128] Spring and Autumn Charging Demand Model:

[0129]

[0130] Where, ω i This represents the annual charging demand for the i-th version of the vehicle during the spring and autumn seasons.

[0131] Summer and winter charging demand model:

[0132]

[0133] Among them, υ i This indicates the annual charging requirements for version i during the summer and winter seasons.

[0134] Calculation of annual total charging demand for a single vehicle model: Summarize the charging demand in different seasons to obtain the annual total charging demand (Ω) for a single version of the vehicle model. i :

[0135] Ω i =ω i +υ i ,#(3)

[0136] Further calculate the total annual charging demand Θ for vehicle model j. j :

[0137]

[0138] Where l represents the total number of versions under model j;

[0139] Estimated Annual Total Charging Demand for a City: Based on the calculation results of the annual total charging demand, the total annual charging demand E for all vehicle types in city k is calculated. k :

[0140]

[0141] Where m represents the total number of vehicle types in city k;

[0142] Right now:

[0143]

[0144] The working principle and beneficial effects of the above technical solution are as follows: By fully identifying vehicle models and classifying versions, it integrates data on all new energy passenger vehicles in the market, covering pure electric vehicles, plug-in hybrid electric vehicles, and range-extended electric vehicles. It collects historical sales data and, combined with vehicle manufacturer announcements, product catalogs, and technical parameters, identifies the specific versions, powertrain configurations, and range capabilities of each model, constructing a unified model-version mapping system to provide complete data support for subsequent demand calculations. Next, it determines the sales proportion of the i-th version vehicle within the vehicle model range, and calculates the total number of i-th version vehicles remaining based on the sales proportion and the sales volume of the model within a preset time period. It calculates the theoretical power consumption per unit mileage of the i-th version vehicle based on the pre-obtained battery energy and range parameters. It then calculates the average annual mileage of vehicles in city i, and calculates the average annual power consumption of the i-th version vehicle in city i based on the theoretical power consumption per unit mileage and the average annual mileage of vehicles in city i. Finally, it introduces energy consumption correction coefficients for spring / autumn and summer / winter seasons to establish charging demand models for spring / autumn and summer / winter seasons, respectively. This technical solution summarizes charging demand data from different seasons to obtain the total annual charging demand for a single vehicle model, and further calculates the total annual charging demand for model j. Finally, it calculates the total annual charging demand for all vehicle models in the city to obtain the city's total annual charging demand. This technical solution systematically integrates vehicle model and regional characteristic data, and accurately predicts the charging demand of different vehicle models in a city through a seasonal charging demand calculation model. This solution can accurately grasp the distribution of charging demand for different vehicle models in a city, providing a scientific basis for the rational layout of charging piles; the seasonal correction coefficient improves the accuracy of prediction and better adapts to seasonal changes in actual use; it helps optimize the allocation of charging facility resources, improve the utilization efficiency of charging facilities, and meet the charging needs of new energy vehicles in the city.

[0145] In a preferred embodiment, the charging facility capacity optimization configuration module based on grid and regional constraints specifically performs the following operations:

[0146] A charging facility capacity planning model is constructed with the optimization objectives of total cost and grid load balance. The model constraints and objective function are as follows:

[0147] The objective functions include minimizing the total lifecycle cost of charging infrastructure and minimizing grid load fluctuations from a quarterly perspective;

[0148] The method for minimizing the total lifecycle cost of charging infrastructure includes:

[0149] Determine the initial investment cost C of a single pile in city administrative district i. cap,i And the annual operation and maintenance cost per pile C om,i And calculate the total life cycle cost of a single charging pile based on the pre-set capital recovery factor (CRF);

[0150] Based on the total number N of administrative districts in a city, the number of charging piles in each administrative district, and the total life cycle cost of a single charging pile, a calculation model minf1 is constructed to minimize the total life cycle cost of the charging facility. The formula is as follows:

[0151]

[0152] Where, x i The number of charging piles in city administrative district i;

[0153] The Capital Recovery Factor (CRF) is calculated based on the project life of the charging equipment and the discount rate. The calculation formula is as follows:

[0154]

[0155] Where r is the discount rate and n is the project life;

[0156] Based on the total grid load in the s-th quarter s In addition to the annual average load AvgLoad, a model minf2 is established to minimize the degree of power grid load fluctuation from a quarterly perspective. The formula is as follows:

[0157]

[0158] Among them, the total grid load in the s-th quarter is Load s The calculation is determined based on the average charging power per charging pile and the grid foundation load excluding charging piles. The calculation formula is as follows:

[0159]

[0160] Where, p i,s The average charging power per charging pile for city administrative district i in the s quarter. The grid base load for the s-th quarter does not include charging piles;

[0161] Based on the total grid load in the s-th quarter s Calculate the annual average load (AvgLoad):

[0162]

[0163] The constraints include: total number of charging piles, quarterly charging demand satisfaction, quarterly grid load ceiling, regional allocation fairness, regional charging pile density, and priority guarantee for key areas.

[0164] The constraint on the total number of charging piles is expressed by the following formula:

[0165]

[0166] Among them, X total This refers to the total number of charging stations in the city.

[0167] Quarterly charging demand meets constraints, as shown below:

[0168]

[0169] Where, λ i,s Q represents the average charging amount per charging pile for city administrative district i in the s quarter. s The total charging demand in the city for the sth quarter;

[0170] The quarterly power grid load ceiling constraint is as follows:

[0171]

[0172] Among them, GridMargin s This represents the additional charging load that the power grid can handle in the s-th quarter;

[0173] The fairness constraints for allocating charging station distribution areas based on population size are as follows:

[0174]

[0175] Among them, D i Let D be the population of city administrative district i. total δ represents the total population of the city, and δ is the allocation deviation threshold.

[0176] The density of charging stations in a given area is constrained as follows:

[0177]

[0178] Among them, Area i Let α be the area of ​​city administrative district i, and α be the minimum charging pile density.

[0179] Priority protection and constraints are applied to key areas, as shown below:

[0180]

[0181] Where, x i Let β be the number of charging piles in urban administrative district i, and β be the allocation coefficient for key areas.

[0182] Finally, a charging facility capacity optimization configuration scheme that meets the above constraints and objective function is obtained.

[0183] The working principle and beneficial effects of the above technical solution are as follows: By constructing a charging facility capacity planning model with the optimization objective of balancing total cost and grid load, both economic efficiency and grid stability are comprehensively considered. The model's objective functions include minimizing the total lifecycle cost of charging facilities and minimizing grid load fluctuations. When calculating the total lifecycle cost, the initial investment cost per charging pile, annual operation and maintenance costs, and capital recovery factors are considered to ensure the comprehensiveness and accuracy of cost estimation. Simultaneously, by analyzing the relationship between quarterly total grid load and annual average load, a model that can effectively reduce grid load fluctuations is established, improving the stability of grid operation. Constraints cover multiple aspects, including the total number of charging piles, meeting charging demand, grid load limits, regional allocation fairness, charging pile density, and priority protection for key areas, ensuring that the layout of charging facilities meets both urban needs and grid security and regional development requirements. This technical solution coordinates various factors through mathematical models and optimization algorithms to achieve the optimal configuration of charging facility capacity. This technical solution reduces the total cost of construction and operation of charging facilities and improves investment efficiency; it balances the grid load, reduces the impact of charging facility access on the grid, and enhances the reliability and security of the grid; it ensures the reasonable distribution of charging facilities in various urban areas, improves the fairness and accessibility of services, meets the charging needs of different areas, especially prioritizes key areas, and improves the overall service level and user satisfaction of charging facilities.

[0184] In a preferred embodiment, the visualization analysis and interactive display module for city-level charging planning results, based on GIS spatial data and a Web 3D engine, constructs an interactive visualization interface to display the output results of the charging facility capacity planning model and assist in decision analysis, specifically performing the following operations:

[0185] 3D map display: Based on the city's administrative divisions, the density of charging piles, the utilization rate of power grid capacity, and the supply and demand matching rate are overlaid to generate an intuitive and interactive 3D visualization map.

[0186] Dynamically linked data charts: including bar charts, heatmaps, and pie charts, supporting switching and comparative analysis across different time and regional dimensions;

[0187] Multiple versions of planning schemes can be displayed and compared: Supports the display of multiple versions of planning results under different model parameter settings, assisting policymakers in conducting scenario simulations and sensitivity analyses;

[0188] User interaction feedback mechanism: Integrates site selection suggestion feedback and demand popularity collection functions for future model optimization input.

[0189] The working principle and beneficial effects of the above technical solution are as follows: An interactive visualization interface built based on GIS spatial data and a Web 3D engine enables intuitive display and in-depth analysis of the output results of the charging facility capacity planning model. The 3D map display function uses urban administrative divisions as a foundation, overlaying key indicators such as charging pile deployment density, grid capacity utilization, and supply-demand matching rate to generate an interactive 3D visualization map, allowing users to clearly understand the distribution of charging facilities and their matching degree with the grid and demand. Dynamic data chart linkage supports switching and comparative analysis across different time and regional dimensions through various formats such as bar charts, heat maps, and pie charts, enabling users to observe and understand data changes and trends from multiple perspectives. The multi-version display and comparison function of planning schemes allows the display of multiple versions of planning results under different model parameter settings, providing policymakers with tools for scenario simulation and sensitivity analysis to help them make better decisions. The user interaction feedback mechanism integrates site selection suggestion feedback and demand heat collection functions, enabling users to participate in the charging facility planning process and provide valuable feedback. This feedback will be used to optimize model input in the future, further improving the scientific nature and practicality of the planning. This technical solution presents complex charging facility planning data in an intuitive and visual way, while providing interactive functions and user feedback channels, making the planning process more transparent, scientific, and closely aligned with actual needs. This solution improves the transparency and understandability of charging facility planning, enabling government decision-makers, urban planners, and the public to intuitively understand the layout and rationality of charging facilities. Through multi-dimensional data display and comparative analysis, it provides decision-makers with comprehensive reference information, helping to formulate more scientific and reasonable charging facility development plans. The user interaction and feedback mechanism effectively collects opinions and suggestions from all parties, promoting continuous optimization and improvement of the planning scheme, enhancing the rationality of charging facility layout and user satisfaction, thereby promoting the efficient construction and sustainable development of urban charging facilities.

[0190] In a preferred embodiment, refer to Figure 2 It also includes adding an extension module, which performs the following operations:

[0191] Obtain the occupancy time ratio of charging piles at each site under the current urban charging facility capacity planning, and generate a first spatial interpolation heat map on the visualization interface based on the occupancy time ratio.

[0192] Obtain the average daily distribution of vehicles in various areas of the city, and generate a second spatial interpolation heatmap on the visualization interface based on the vehicle distribution density;

[0193] Based on spatial overlay analysis, the first spatial interpolation heatmap and the second spatial interpolation heatmap are combined, and the following operations are performed:

[0194] Calculate local R using the geographically weighted regression model GWR 2 Using local R 2 Identify areas where vehicle distribution density strongly influences charging pile occupancy rate;

[0195] Ripley's K-function is used to calculate the cross K-function between vehicle distribution density and charging pile occupancy, analyze and determine the clustering correlation between vehicle distribution density and charging pile occupancy within a specific radius, and determine the maximum influence distance of vehicle distribution density on charging pile occupancy.

[0196] Based on the strong influence area and the maximum influence distance, an additional area is generated with the strong influence area as the core and the maximum influence distance as the expansion radius.

[0197] The addition of charging stations can be based on the division of the area to be added, or charging stations can be added by selecting charging stations.

[0198] The working principle and beneficial effects of the above technical solution are as follows: By adding an extension module, the occupancy rate of charging piles at various urban sites and the average daily distribution of vehicles in different areas are obtained. Based on this data, first and second spatial interpolation heatmaps are generated to visually display the occupancy of charging piles and the distribution of vehicles. A geographically weighted regression (GWR) model is used to calculate the local R-squared value. 2 This technology accurately identifies areas where vehicle density significantly impacts charging pile occupancy. Simultaneously, it uses Ripley's K-function to calculate the cross-K-function, analyzing the maximum influence distance between vehicle density and charging pile occupancy. Combining the areas of strong influence and the maximum influence distance, it determines areas for additional charging piles, with the strong influence areas serving as the core and the maximum influence distance as the edge. This results in an overall distribution of areas for additional charging piles, determined by a smooth distribution of vehicle density, and allows for the planning of charging pile sites or the increase in the number of charging piles. This technical solution combines charging pile occupancy data with vehicle distribution data, using spatial analysis techniques to accurately locate areas where additional charging piles are needed, ensuring that the charging pile layout closely matches actual demand and improving the service level and user experience of urban charging facilities.

[0199] In a preferred embodiment, adding charging stations based on the division of the added area or selecting charging stations to add charging piles includes:

[0200] Based on the newly added areas, statistics were compiled on each charging station within the added areas and the percentage of time each charging station was occupied.

[0201] When the occupancy time of any charging station exceeds a preset value, it will be marked as a busy station.

[0202] The K-center algorithm is used to calculate the spatial location of busy stations in the newly added area to obtain the locations of K different cluster centers.

[0203] Each cluster center is used as an additional charging station location. The busiest station closest to the charging station is determined, and the straight-line distance between the busiest station and the charging station is calculated. If the distance is less than or equal to a preset distance threshold, the busiest station is selected to add the charging station.

[0204] If the distance is greater than the preset distance threshold, then the location to be added will be selected to add a charging station and a charging pile.

[0205] The working principle and beneficial effects of the above technical solution are as follows: By statistically analyzing the charging station locations and the occupancy rate of charging stations within each station in the added area, busy stations are identified. The K-center algorithm is used to perform cluster analysis on the busy stations, determining K cluster center locations as potential addition points. The distance between each cluster center location and the nearest busy station is determined. If the distance is less than or equal to a preset threshold and there are conditions for adding charging stations at that busy station, then a charging station is added at the nearest busy station; otherwise, a new charging station is built at the addition point. This method can accurately locate areas with high charging demand and insufficient existing facilities, achieving reasonable addition of charging stations, improving the scientific and rational layout of urban charging facilities, and increasing the coverage and user experience of charging services.

[0206] In one specific embodiment, refer to Figure 3 The K-center algorithm includes:

[0207] Step S1: Based on the area size of the added area and the preset table corresponding to the area of ​​the added area and the maximum number of added sites, determine the maximum number K of allowed added sites.

[0208] Step S2: Randomly select K busy stations from all busy stations in the added area as initial cluster centers;

[0209] Step S3: Calculate the distance between each busy site and all cluster centers, and assign each busy site to the nearest cluster center;

[0210] Step S4: For each cluster, calculate its centroid.

[0211] Step S5: Calculate the maximum distance from all busy stations to their center point in each cluster;

[0212] Step S6: If the maximum distance of each cluster is less than or equal to a predetermined threshold, the algorithm stops and outputs the cluster center locations of K different categories; otherwise, the busiest station with the farthest distance is taken as the new cluster center, and steps S3 to S6 are executed again.

[0213] In a preferred embodiment, when adding charging piles to busy charging stations and newly added charging station sites, the following operations are performed:

[0214] Determine K distinct clusters, and the total occupancy time of all charging piles at all charging pile sites within each cluster;

[0215] Based on the total time occupied by each of the K clusters, weights are assigned to the K clusters.

[0216] Based on the weight of the clusters and the total number of charging piles to be added in advance, the number of charging piles allocated to each cluster is determined.

[0217] Based on the quantity quota, additional charging piles will be added to busy sites and newly established charging pile sites.

[0218] The working principle and beneficial effects of the above technical solution are as follows: By determining K different clusters and their total occupancy time, a weight is assigned to each cluster. Based on the weight and the planned total number, the allocation of charging piles within each cluster is determined, and charging piles are added to busy and newly added sites accordingly. This method ensures that the allocation of charging piles closely matches actual demand, improves the rationality of charging facility layout, meets the needs of high-demand areas, optimizes user experience, and improves the overall efficiency of the facilities.

[0219] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A visualization system for urban charging facility capacity planning oriented towards charging demand, characterized in that, include: The seasonal charging demand prediction module based on vehicle type and regional characteristics is used to combine the market sales, vehicle performance parameters and regional driving characteristics of all electric vehicle models, and to build a power demand estimation model by vehicle type, version, region and season to obtain the predicted charging demand. The charging facility capacity optimization and configuration module based on power grid and regional constraints is used to introduce a multi-objective optimization framework based on the predicted charging demand. It constructs a charging facility capacity planning model for urban scale based on constraints of cost control, power grid load balance and regional fairness, and achieves optimal configuration under multiple constraints. The visualization and interactive display module for city-level charging planning results: Based on GIS spatial data and a Web 3D engine, an interactive visualization interface is built to display the output results of the charging facility capacity planning model and to assist in decision analysis.

2. The visualization system for urban charging facility capacity planning based on charging demand as described in claim 1, characterized in that: The seasonal charging demand forecasting module based on vehicle type and regional characteristics performs the following operations: Vehicle Model Identification and Version Classification: This feature integrates data on all new energy passenger vehicles currently on the market, including pure electric vehicles, plug-in hybrid electric vehicles, and range-extended electric vehicles. By collecting sales data for each model over historical periods, and combining this data with manufacturer announcements, product catalogs, and technical parameters, the feature identifies the specific version, powertrain configuration, and range of each model. This constructs a unified vehicle model-version mapping system, providing comprehensive data support for subsequent demand calculations. Here, "model" refers to the name of a specific vehicle product or series under a brand, while "version" represents different performance configurations within the same model. Construction of Seasonal Charging Demand Calculation Model: For the i-th version of vehicle model j, determine the sales proportion γ of the i-th version of vehicle model j. i Based on sales percentage γ i And the sales volume of model j within a preset time period. j Calculate the total number of vehicles with the i-th version remaining; Based on the pre-acquired battery energy of the i-th version of the vehicle. i And the NEDC range parameters i Calculate the theoretical power consumption per unit mileage for the i-th version of the vehicle; Calculate the average annual mileage of vehicles in city k. k Based on the theoretical power consumption per unit mileage of the i-th version of the vehicle and the average annual mileage of the vehicle in the k-th city, calculate the total annual power consumption of the i-th version of the vehicle in the k-th city. Introducing the spring and autumn energy consumption correction factor λ i And the summer and winter energy consumption correction factor ρ i Establish separate charging demand models for spring and autumn, and summer and winter: Spring and Autumn Charging Demand Model: Where, ω i This represents the annual charging demand for the i-th version of the vehicle during the spring and autumn seasons. Summer and winter charging demand model: Among them, v i This indicates the annual charging requirements for version i during the summer and winter seasons. Calculation of annual total charging demand for a single vehicle model: Summarize the charging demand in different seasons to obtain the annual total charging demand (Ω) for a single version of the vehicle model. i : Oh i =ω i +v i ,#(3) Further calculate the total annual charging demand Θ for vehicle model j. j : Where l represents the total number of versions under model j; Estimated Annual Total Charging Demand for a City: Based on the calculation results of the annual total charging demand, the total annual charging demand E for all vehicle types in city k is calculated. k : Where m represents the total number of vehicle types in city k; Right now:

3. The visualization system for urban charging facility capacity planning based on charging demand as described in claim 1, characterized in that: The charging facility capacity optimization configuration module based on grid and regional constraints performs the following operations: A charging facility capacity planning model is constructed with the optimization objectives of total cost and grid load balance. The model constraints and objective function are as follows: The objective functions include minimizing the total lifecycle cost of charging infrastructure and minimizing grid load fluctuations from a quarterly perspective; The method for minimizing the total lifecycle cost of charging infrastructure includes: Determine the initial investment cost C of a single pile in city administrative district i. cap,i And the annual operation and maintenance cost per pile C om,i And calculate the total life cycle cost of a single charging pile based on the pre-set capital recovery factor (CRF); Based on the total number N of administrative districts in a city, the number of charging piles in each administrative district, and the total life cycle cost of a single charging pile, a calculation model minf1 is constructed to minimize the total life cycle cost of the charging facility. The formula is as follows: Where, x i The number of charging piles in city administrative district i; The Capital Recovery Factor (CRF) is calculated based on the project life of the charging equipment and the discount rate. The calculation formula is as follows: Where r is the discount rate and n is the project life; Based on the total grid load in the s-th quarter s In addition to the annual average load AvgLoad, a model minf2 is established to minimize the degree of power grid load fluctuation from a quarterly perspective. The formula is as follows: Among them, the total grid load in the s-th quarter is Load s The calculation is determined based on the average charging power per charging pile and the grid foundation load excluding charging piles. The calculation formula is as follows: Where, p i,s The average charging power per charging pile for city administrative district i in the s quarter. The grid base load for the s-th quarter does not include charging piles; Based on the total grid load in the s-th quarter s Calculate the annual average load (AvgLoad): The constraints include: total number of charging piles, quarterly charging demand satisfaction, quarterly grid load ceiling, regional allocation fairness, regional charging pile density, and priority guarantee for key areas. The constraint on the total number of charging piles is expressed by the following formula: Among them, X total This refers to the total number of charging stations in the city. Quarterly charging demand meets constraints, as shown below: Where, λ i,s Q represents the average charging amount per charging pile for city administrative district i in the s quarter. s The total charging demand in the city for the sth quarter; The quarterly power grid load ceiling constraint is as follows: Among them, GridMargin s This represents the additional charging load that the power grid can handle in the s-th quarter; The fairness constraints for allocating charging station distribution areas based on population size are as follows: Among them, D i Let D be the population of city administrative district i. total δ represents the total population of the city, and δ is the allocation deviation threshold. The density of charging stations in a given area is constrained as follows: Among them, Area i Let α be the area of ​​city administrative district i, and α be the minimum charging pile density. Priority protection and constraints are applied to key areas, as shown below: Where, x i Let β be the number of charging piles in urban administrative district i, and β be the allocation coefficient for key areas. Finally, a charging facility capacity optimization configuration scheme that meets the above constraints and objective function is obtained.

4. A visualization system for urban charging facility capacity planning based on charging demand, as described in claim 1, is characterized in that: The visualization analysis and interactive display module for the city-level charging planning results, based on GIS spatial data and a Web 3D engine, constructs an interactive visualization interface to display the output results of the charging facility capacity planning model and assist in decision analysis. Specifically, it performs the following operations: 3D map display: Based on the city's administrative divisions, the density of charging piles, the utilization rate of power grid capacity, and the supply and demand matching rate are overlaid to generate an intuitive and interactive 3D visualization map. Dynamically linked data charts: including bar charts, heatmaps, and pie charts, supporting switching and comparative analysis across different time and regional dimensions; Multiple versions of planning schemes can be displayed and compared: Supports the display of multiple versions of planning results under different model parameter settings, assisting policymakers in conducting scenario simulations and sensitivity analyses; User interaction feedback mechanism: Integrates site selection suggestion feedback and demand popularity collection functions for future model optimization input.

5. A visualization system for urban charging facility capacity planning based on charging demand, as described in claim 1, is characterized in that... It also includes adding an extension module, which performs the following operations: Obtain the occupancy time ratio of charging piles at each site under the current urban charging facility capacity planning, and generate a first spatial interpolation heat map on the visualization interface based on the occupancy time ratio. Obtain the average daily distribution of vehicles in various areas of the city, and generate a second spatial interpolation heatmap on the visualization interface based on the vehicle distribution density; Based on spatial overlay analysis, the first spatial interpolation heatmap and the second spatial interpolation heatmap are combined, and the following operations are performed: Calculate local R using the geographically weighted regression model GWR 2 Using local R 2 Identify areas where vehicle distribution density strongly influences charging pile occupancy rate; Ripley's K-function is used to calculate the cross K-function between vehicle distribution density and charging pile occupancy, analyze and determine the clustering correlation between vehicle distribution density and charging pile occupancy within a specific radius, and determine the maximum influence distance of vehicle distribution density on charging pile occupancy. Based on the strong influence area and the maximum influence distance, an additional area is generated with the strong influence area as the core and the maximum influence distance as the expansion radius. The addition of charging stations can be based on the division of the area to be added, or charging stations can be added by selecting charging stations.

6. A visualization system for urban charging facility capacity planning based on charging demand, as described in claim 5, is characterized in that... Adding charging stations based on designated areas or selecting existing charging stations includes: Based on the newly added areas, statistics were compiled on each charging station within the added areas and the percentage of time each charging station was occupied. When the occupancy time of any charging station exceeds a preset value, it will be marked as a busy station. The K-center algorithm is used to calculate the spatial location of busy stations in the newly added area to obtain the locations of K different cluster centers. Each cluster center is used as an additional charging station location. The busiest station closest to the charging station is determined, and the straight-line distance between the busiest station and the charging station is calculated. If the distance is less than or equal to a preset distance threshold, the busiest station is selected to add the charging station. If the distance is greater than the preset distance threshold, then the location to be added will be selected to add a charging station and a charging pile.

7. A visualization system for urban charging facility capacity planning based on charging demand, as described in claim 6, is characterized in that... The K-center point algorithm includes: Step S1: Based on the area size of the added area and the preset table corresponding to the area of ​​the added area and the maximum number of added sites, determine the maximum number K of allowed added sites. Step S2: Randomly select K busy stations from all busy stations in the added area as initial cluster centers; Step S3: Calculate the distance between each busy site and all cluster centers, and assign each busy site to the nearest cluster center; Step S4: For each cluster, calculate its centroid. Step S5: Calculate the maximum distance from all busy stations to their center point in each cluster; Step S6: If the maximum distance of each cluster is less than or equal to a predetermined threshold, the algorithm stops and outputs the cluster center locations of K different categories; otherwise, the busiest station with the farthest distance is taken as the new cluster center, and steps S3 to S6 are executed again.

8. A visualization system for urban charging facility capacity planning based on charging demand, as described in claim 6, is characterized in that... When adding charging piles to busy charging stations or newly added charging station sites, perform the following operations: Determine K distinct clusters, and the total occupancy time of all charging piles at all charging pile sites within each cluster; Based on the total time occupied by each of the K clusters, weights are assigned to the K clusters. Based on the weight of the clusters and the total number of charging piles to be added in advance, the number of charging piles allocated to each cluster is determined. Based on the quantity quota, additional charging piles will be added to busy sites and newly established charging pile sites.

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