Fast charging station planning method, system and equipment suitable for multiple scenes and medium

Abstract

The invention discloses a fast charging station planning method, system, equipment and medium suitable for multiple scenes, and the method comprises the steps: dividing a to-be-planned region into a plurality of to-be-planned scenes, and quantifying the number of fast charging piles in the scene according to the charging demand data of various types of electric vehicles in each to-be-planned scene; quantifying the demand data of the fast charging station in the scene by combining the capacity of the fast charging pile in various scenes; taking point locations with charging station construction conditions in various scenes as candidate sites to construct candidate site sets in various scenes, selecting a plurality of candidate sites from the candidate sites to form a plurality of site combinations, and constructing site combination optimization models in various scenes based on the site combinations; the station combination optimization models are solved through an optimization algorithm, the optimal station combination is obtained and combined, a fast charging station planning scheme of the to-be-planned area is generated and executed, and fast charging station layout can be reasonably and scientifically achieved by distinguishing charging requirements in different scenes and point positions with fast charging station construction conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of fast charging station layout, and in particular to a fast charging station planning method, system, device and medium applicable to multiple scenarios. Background Art

[0002] With the popularity of electric vehicles, the demand for charging facilities is also increasing. The reasonable layout of fast charging stations is crucial to meet user needs, reduce costs and improve operational efficiency. At present, the planning of charging stations mainly takes the time and spatial distribution of electric vehicle charging loads in the planning area, the power quality of the power grid and the construction cost as the optimization objectives to determine the optimal location of electric vehicle charging stations. However, this method is difficult to meet the actual application needs in different scenarios. It lacks accurate prediction of multi-dimensional needs and cannot achieve layout design based on comprehensive optimization objectives.

[0003] It can be seen that how to plan fast charging stations in multiple scenarios has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] The present invention provides a fast charging station planning method, system, device and medium applicable to multiple scenarios, which solves the problem of how to plan fast charging stations in multiple scenarios.

[0005] In order to solve the above technical problems, the first aspect of the present invention provides a fast charging station planning method applicable to multiple scenarios, including: Performing scenario analysis on the area to be planned to obtain multiple scenarios to be planned, and quantifying charging demand data of multiple types of electric vehicles under each of the scenarios to be planned; Quantify the number of fast charging piles in each of the scenarios to be planned according to each of the charging demand data, and combine it with the capacity of the fast charging piles in each of the scenarios to be planned to quantify the fast charging station demand data in each of the scenarios to be planned; Based on the demand data of each fast charging station, the points that meet the conditions for building a charging station in each of the scenarios to be planned are taken as candidate sites to construct a set of candidate sites in each of the scenarios to be planned; Selecting a plurality of candidate sites from each of the candidate site sets to form a plurality of site combinations, and constructing a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations; The optimization model of each site combination is solved by an optimization algorithm to obtain the best site combination in each scenario to be planned for combination, and a fast charging station planning plan for the area to be planned is generated and executed.

[0006] As one of the preferred solutions, the scenario analysis is performed on the area to be planned to obtain multiple scenarios to be planned, and the charging demand data of multiple types of electric vehicles in each of the scenarios to be planned are quantified, including: Performing scenario analysis on the area to be planned according to the function to obtain multiple scenarios to be planned; the scenarios to be planned include residential areas, commercial areas, office areas and tourist areas; Quantify the average daily power consumption data of various types of electric vehicles and their ownership in each of the scenarios to be planned, and combine them with the external charging ratio of various types of electric vehicles to quantify the average daily charging demand data of various types of electric vehicles in each of the scenarios to be planned; The daily average charging demand data of various types of electric vehicles in each of the scenarios to be planned are combined to obtain the charging demand data of all types of electric vehicles in each of the scenarios to be planned.

[0007] As one of the preferred solutions, the number of fast charging piles in each of the scenarios to be planned is expressed by the following formula: In the formula, is the number of fast charging piles in the jth scenario to be planned; is the charging demand data under the jth scenario to be planned; The charging power of each fast charging pile; The utilization rate of fast charging piles; , are respectively the duration of the charging peak and the charging peak demand coefficient under the jth scenario to be planned.

[0008] As one of the preferred solutions, based on the demand data of each fast charging station, the points with charging station construction conditions in each of the scenarios to be planned are taken as candidate sites to construct a set of candidate sites in each of the scenarios to be planned, including: Obtaining geographic location data, construction cost data, and fast charging pile capacity of each of the scenarios to be planned to construct an evaluation index system, and taking points that meet the conditions for charging station construction in each of the scenarios to be planned as candidate sites; Evaluate each of the candidate sites according to the evaluation index system to obtain preliminary candidate sites for each of the scenarios to be planned according to the evaluation results; Based on the geographical location data of each of the scenarios to be planned, cluster analysis is performed on each of the preliminary candidate sites by using a K-means clustering algorithm to obtain clustered candidate sites under each of the scenarios to be planned; Constructing constraint conditions according to the demand data of each fast charging station and the capacity of each fast charging pile, and constructing an objective function through the construction cost data; Each of the cluster candidate sites is used as an input of the particle swarm optimization algorithm for iterative search, and in each iteration, the pros and cons of each of the cluster candidate sites are evaluated according to the objective function and the constraint conditions, so as to select the optimal cluster candidate site combination in each of the scenarios to be planned as the final candidate site set.

[0009] As one of the preferred solutions, the evaluation index system includes location index, cost index and capacity index; the evaluation result includes location index evaluation result, cost index evaluation result and capacity index evaluation result; wherein, The selecting a plurality of candidate sites from each of the candidate site sets to form a plurality of site combinations, and constructing a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations, includes: The index evaluation result, cost index evaluation result and capacity index evaluation result of each of the scenarios to be planned are respectively used as the first attribute, the second attribute and the third attribute of the corresponding candidate site set, and a number of candidate sites are respectively selected from the candidate site set under each of the scenarios to be planned to construct a site combination under each of the scenarios to be planned; The weighted average distances of all candidate sites in each site combination to the charging demand points in the scenario to be planned, the total cost of each site combination, and the revenue of each site combination per unit time are quantified, and weighted by the first attribute, the second attribute, and the third attribute of the corresponding candidate site set, respectively, to obtain a site combination optimization model for each scenario to be planned.

[0010] As one of the preferred solutions, the site combination optimization model is expressed by the following formula: In the formula, is the site combination optimization model for the jth scenario to be planned; is the weighted average distance from all candidate sites in the site combination under the jth scenario to be planned to the charging demand point under this scenario; is the total cost of the site combination under the jth scenario to be planned; is the revenue per unit time of the site combination in the jth scenario to be planned; are the first attribute, second attribute and third attribute of the candidate site set in the jth scenario to be planned, respectively; is the candidate site number; X is the demand data of fast charging stations in the jth scenario to be planned; is the number of charging demand points; Y is the number of charging demand points in the jth scenario to be planned; Candidate sites in the site combination Meeting charging needs The proportion of charging demand; is the candidate site in the site combination under the jth scenario to be planned land cost; is the candidate site in the site combination under the jth scenario to be planned The procurement cost of fast charging equipment; is the candidate site in the site combination under the jth scenario to be planned The cost of retrofitting to the grid; is the candidate site in the site combination under the jth scenario to be planned Operation and maintenance costs; T is the unit time; The utilization rate of fast charging piles; is the capacity of the fast charging pile in the jth scenario to be planned; For charging price.

[0011] As one of the preferred solutions, solving the site combination optimization model by an optimization algorithm to obtain the best site combination in each of the scenarios to be planned includes: Solving the optimization model of each site combination by genetic algorithm to obtain the initial site combination and the initial combination optimization value under each scenario to be planned, and using each initial site combination as the current combination under each scenario to be planned to perform simulated annealing operation; Randomly selecting a candidate site from each of the candidate site sets to replace a candidate site in each of the current combinations, generating a neighborhood combination under each of the scenarios to be planned, and quantifying the neighborhood combination based on each of the site combination optimization models to obtain an optimization value of the neighborhood combination under each of the scenarios to be planned; The optimization value of each neighborhood combination is compared with the optimization value of each initial combination, and the current combination in each scenario to be planned is updated based on the result of the first comparison, so as to make a second comparison between the optimal combination formed by the updated current combination and the initial site combination in each scenario to be planned, and the optimal combination is updated according to the result of the second comparison, and the neighborhood combination generation step and the optimal combination update step are iteratively executed according to the preset annealing condition to obtain the best site combination in each scenario to be planned.

[0012] A second aspect of the present invention provides a fast charging station planning system applicable to multiple scenarios, including: The first demand quantification module is used to perform scenario analysis on the area to be planned, obtain multiple scenarios to be planned, and quantify the charging demand data of multiple types of electric vehicles in each of the scenarios to be planned; A second demand quantification module is used to quantify the number of fast charging piles in each of the scenarios to be planned according to each of the charging demand data, and combine it with the capacity of the fast charging piles in each of the scenarios to be planned to quantify the fast charging station demand data in each of the scenarios to be planned; A site set construction module, for taking points with charging station construction conditions in each of the scenarios to be planned as candidate sites based on the demand data of each of the fast charging stations, so as to construct a candidate site set in each of the scenarios to be planned; An optimization model building module, used to select a number of candidate sites from each of the candidate site sets to form a plurality of site combinations, and to build a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations; The optimization model solving module is used to solve the optimization model of each site combination through an optimization algorithm, obtain the best site combination under each scenario to be planned for combination, generate a fast charging station planning plan for the area to be planned, and execute it.

[0013] A third aspect of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the fast charging station planning method applicable to multiple scenarios as described above is implemented.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the fast charging station planning method applicable to multiple scenarios as described above is implemented.

[0015] Yet another embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the fast charging station planning method applicable to multiple scenarios as described above.

[0016] Compared with the prior art, the embodiments of the present invention have the following advantages: (1) Through scenario analysis and charging demand quantification, we can more accurately grasp the characteristics of charging demand in the planned area and provide a scientific basis for the planning of fast charging stations. Combined with the planning of the number and capacity of fast charging piles, we can ensure that the construction of fast charging stations can meet actual needs and avoid waste or shortage of resources. (2) By screening candidate sites and optimizing site combinations, the optimal site layout plan can be selected to improve the coverage and utilization efficiency of fast charging stations. By distinguishing the charging needs in different scenarios and the locations that meet the conditions for building fast charging stations, the layout of fast charging stations can be reasonably and scientifically implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the implementation mode will be briefly introduced below. Obviously, the drawings described below are only some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flowchart of a fast charging station planning method applicable to multiple scenarios provided by a certain embodiment of the present invention; Figure 2 It is a structural diagram of a fast charging station planning system applicable to multiple scenarios provided by a certain embodiment of the present invention; Figure 3 It is a structural diagram of an electronic device provided by a certain embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0021] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two components. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0022] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0023] In one embodiment, if Figure 1 As shown, the first aspect of the present invention provides a fast charging station planning method applicable to multiple scenarios, including: S1. Perform scenario analysis on the area to be planned to obtain multiple scenarios to be planned, and quantify charging demand data of multiple types of electric vehicles under each of the scenarios to be planned; In one embodiment, step S1 includes: Performing scenario analysis on the area to be planned according to the function to obtain multiple scenarios to be planned; the scenarios to be planned include residential areas, commercial areas, office areas and tourist areas; Quantify the average daily power consumption data of various types of electric vehicles and their ownership in each of the scenarios to be planned, and combine them with the external charging ratio of various types of electric vehicles to quantify the average daily charging demand data of various types of electric vehicles in each of the scenarios to be planned; The daily average charging demand data of various types of electric vehicles in each of the scenarios to be planned are combined to obtain the charging demand data of all types of electric vehicles in each of the scenarios to be planned.

[0024] Specifically, the present invention classifies the scenes of the planned area according to its function, so as to divide it into multiple scenes to be planned, such as residential area, commercial area, office area and tourist area, which can also be directly referred to as scenes; wherein the residential area is used to meet the living needs of residents, the commercial area is used to meet the diversified shopping needs of consumers, the office area is used to meet the centralized office needs of enterprises, and the tourist area has rich tourism resources and a good natural environment. In addition, the scene division of the planned area is not limited to the function, but can also be carried out according to the geographical location, population distribution, economic activities and urban planning needs, etc., which will not be elaborated here.

[0025] Then, in various scenarios, according to the registration data of various types of electric vehicles and their vehicle distribution patterns, the number of electric vehicles of this type is calculated, as shown in the following formula: In the formula, The registration data of the i-th type electric vehicle; is the vehicle distribution law coefficient of the i-th type of electric vehicles in the j-th scenario.

[0026] The distribution coefficients of different types of electric vehicles can be determined through research, such as the vehicle distribution coefficient of private cars. It is related to the population size and is expressed by the following formula: In the formula, is the population data of the jth scenario, It is the total population data in the region, which can be obtained through communities, streets, statistics bureaus and other channels.

[0027] The vehicle distribution law coefficient of taxis is It is stated that the distribution of vehicles is mainly determined by the intensity of travel demand and the characteristics of the time period in the scene: under typical proportions, the distribution of online-hailing vehicles in residential areas accounts for 20%-30% of the total number of online-hailing vehicles; the distribution of online-hailing vehicles in commercial areas accounts for 25%-35% of the total number of online-hailing vehicles; the distribution of online-hailing vehicles in office areas accounts for 15%-25% of the total number of online-hailing vehicles; the distribution of online-hailing vehicles in tourist areas accounts for 10%-15% of the total number of online-hailing vehicles; the distribution of online-hailing vehicles in industrial areas accounts for 5%-10% of the total number of online-hailing vehicles; and the distribution of online-hailing vehicles in transportation hubs accounts for 10%-20% of the total number of online-hailing vehicles. In addition, the vehicle distribution regularity coefficient of online-hailing vehicles is expressed as It means that the distribution pattern of its vehicles is the same as that of taxis.

[0028] The vehicle distribution law coefficient of logistics vehicles is It means that the vehicle distribution pattern is related to the number of enterprises, shops, and logistics sites in the scene, which can be expressed by the following formula: In the formula, is the number of stores such as enterprises, shops, and logistics stations in the jth scenario; Indicates the total number of the above stores in the region.

[0029] Then, in various scenarios, the average daily power consumption of the i-th type of electric vehicle is calculated based on the average daily mileage and vehicle energy efficiency of the i-th type of electric vehicle. , as shown below: In the formula, is the average daily mileage of the i-th type of electric vehicles; is the vehicle energy efficiency of the i-th type electric vehicle.

[0030] Based on the number of electric vehicles of type i in various scenarios, the average daily power consumption of electric vehicles of type i, and the external charging ratio of electric vehicles of type i, the average daily charging demand of electric vehicles of type i in this scenario is calculated. , as shown below: In the formula, is the external charging proportion of the i-th electric vehicle in the j-th scenario. This data can be estimated based on survey data or historical charging records.

[0031] Finally, the average daily charging demand of all types of electric vehicles in the same scenario is summarized to obtain the charging demand data of all types of electric vehicles in the scenario - average daily charging demand , which is shown in the following formula: Where n is the number of electric vehicle types.

[0032] By dividing the area to be planned into multiple specific scenarios to be planned, the present invention can more accurately grasp the functional characteristics of different areas and the characteristics of electric vehicle use, which is helpful to formulate a charging facility construction plan that is more in line with actual needs; by quantitatively analyzing the average daily power consumption data of various electric vehicles and their ownership in various scenarios, a solid foundation is provided for subsequent charging demand prediction, and combined with the external charging ratio, the average daily charging demand of various electric vehicles in various scenarios can be more accurately calculated, providing data support for the layout and scale setting of charging facilities; combining the charging demand data of various electric vehicles (such as private cars, buses, taxis, etc.) ensures the comprehensiveness and accuracy of the analysis, which helps to avoid omissions and deviations in planning; the charging demand data obtained through quantitative analysis can provide a scientific basis for the construction and operation of charging facilities, ensuring that the number, type and layout of charging facilities can meet actual needs.

[0033] S2. quantify the number of fast charging piles in each of the scenarios to be planned according to each of the charging demand data, and combine it with the capacity of the fast charging piles in each of the scenarios to be planned to quantify the fast charging station demand data in each of the scenarios to be planned; Specifically, in various scenarios, the proportion of charging during peak demand periods To calculate the peak charging demand factor in this scenario , as shown below: Then, the number of fast charging piles in each scenario is quantified based on the charging facility demand, charging power, and fast charging pile utilization rate in each scenario. , which can be expressed by the following formula: In the formula, The charging power of each fast charging pile; The utilization rate of the fast charging pile depends on the scene characteristics. Its value range is between 0-1, and is usually 0.5-0.8; is the duration of the charging peak in the jth scenario to be planned; is the peak-hour charging capacity of the charging station under the jth scenario obtained through investigation; is the full-day charging capacity of the charging station under the jth scenario obtained through investigation.

[0034] Then, based on the number of fast charging piles in various scenarios and the fast charging pile capacity of a single station, the number of fast charging stations required in the scenario is calculated. , as shown below: In the formula, is the capacity of the fast charging pile at a single site in the jth scenario. This value depends on the site scale of different scenarios.

[0035] S3. Based on the demand data of each fast charging station, the points that meet the conditions for building a charging station in each of the scenarios to be planned are taken as candidate sites to construct a set of candidate sites in each of the scenarios to be planned; In one embodiment, step S3 includes: Obtaining geographic location data, construction cost data, and fast charging pile capacity of each of the scenarios to be planned to construct an evaluation index system, and taking points that meet the conditions for charging station construction in each of the scenarios to be planned as candidate sites; wherein the evaluation index system includes location index, cost index, and capacity index; Evaluate each of the candidate sites according to the evaluation index system to obtain preliminary candidate sites for each of the scenarios to be planned according to the evaluation results; wherein the evaluation results include location index evaluation results, cost index evaluation results and capacity index evaluation results; Based on the geographical location data of each of the scenarios to be planned, cluster analysis is performed on each of the preliminary candidate sites by using a K-means clustering algorithm to obtain clustered candidate sites under each of the scenarios to be planned; Constructing constraint conditions according to the demand data of each fast charging station and the capacity of each fast charging pile, and constructing an objective function through the construction cost data; Each of the cluster candidate sites is used as an input of the particle swarm optimization algorithm for iterative search, and in each iteration, the pros and cons of each of the cluster candidate sites are evaluated according to the objective function and the constraint conditions, so as to select the optimal cluster candidate site combination in each of the scenarios to be planned as the final candidate site set.

[0036] Specifically, the present invention constructs an evaluation index system by acquiring geographic location data (such as longitude and latitude, traffic conditions, etc.), construction cost data (including land cost, equipment cost, construction cost, etc.) and fast charging pile capacity data of various scenarios. The system includes location indicators (accessibility of the evaluation site, surrounding traffic conditions, etc.), cost indicators (construction cost, operating cost, etc. of the evaluation site) and capacity indicators (the number of fast charging piles and total charging capacity that the evaluation site can provide). The above indicators can be expressed by formulas, models, etc., so as to use these evaluation indicators to screen candidate sites in various scenarios and obtain evaluation values ​​corresponding to each candidate site for characterizing various evaluation results.

[0037] Identify points that meet the conditions for building charging stations in various scenarios and use them as candidate sites for the scenario. The conditions for building charging stations should meet the following conditions: there is a public ground parking lot, and the number of parking spaces is greater than the set number of vehicles, and the area of ​​a single parking space is not less than the set area, such as the set number of vehicles is greater than or equal to 30 and the set area is not less than 4.5 square meters; then, according to the evaluation index system, each candidate site in various scenarios is evaluated to obtain the location index evaluation results, cost index evaluation results and capacity index evaluation results of each candidate site, and the evaluation results of these three indicators are combined to compare them with the preset location, cost and capacity thresholds, and then the preliminary candidate sites in various scenarios are screened out according to the comparison results.

[0038] Then, based on the geographic location data of various scenarios, the K-means clustering algorithm is used to perform cluster analysis on the preliminary candidate sites in various scenarios: determine the K value (i.e. the number of clusters), randomly select K preliminary candidate sites as the initial cluster centers, and calculate the distance from each preliminary candidate site to each cluster center in various scenarios, and assign it to the cluster where the nearest cluster center is located; recalculate the mean of each cluster as the new cluster center, and repeat the above steps until the cluster center no longer changes or reaches the preset number of iterations, and various. By clustering these sites, they can be relatively concentrated in geographical location, which is convenient for subsequent optimization and planning.

[0039] Based on the fast charging station demand data and fast charging pile capacity data, constraints are constructed. These conditions include the charging demand that each charging station needs to meet, the number and capacity limit of fast charging piles, etc., and the construction cost data is used to construct the objective function to minimize the fixed cost, equipment cost, grid access cost and operating cost of the candidate site, which is expressed by the following formula: In the formula, is the total cost of the site combination under the jth scenario; is the candidate site number; X is the fast charging station demand data under the jth scenario; is the candidate site in the site combination under the jth scenario land cost; is the candidate site in the site combination under the jth scenario The procurement cost of fast charging equipment; is the candidate site in the site combination under the jth scenario The cost of retrofitting to the grid; is the candidate site in the site combination under the jth scenario operation and maintenance costs.

[0040] Finally, the candidate clustering sites under various scenarios are used as the input of the particle swarm optimization algorithm. The particle swarm algorithm is used for iterative search. In each iteration, the objective function and constraints are used to evaluate the pros and cons of each candidate clustering site to adjust the speed and position of the particles so that they continuously approach the optimal solution. Finally, the optimal combination of candidate clustering sites under each planning scenario is selected as the final candidate site set output.

[0041] By constructing an evaluation index system, the present invention can quickly screen out qualified candidate sites, reducing the blindness and uncertainty in the planning process; using the K-means clustering algorithm to perform cluster analysis on candidate sites, the sites are more concentrated and reasonable in geographical location, and the coverage and utilization efficiency of charging facilities are improved; by searching for the optimal site combination through the particle swarm optimization algorithm, the total construction cost of the charging station can be minimized while meeting the charging demand, thereby improving the economy of the planning; the optimized site layout and reasonable fast charging pile capacity configuration can shorten the user's charging waiting time and improve the convenience and satisfaction of the charging service.

[0042] S4, selecting a plurality of candidate sites from each of the candidate site sets to form a plurality of site combinations, and constructing a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations; In one embodiment, step S4 includes: The index evaluation result, cost index evaluation result and capacity index evaluation result of each of the scenarios to be planned are respectively used as the first attribute, the second attribute and the third attribute of the corresponding candidate site set, and a number of candidate sites are respectively selected from the candidate site set under each of the scenarios to be planned to construct a site combination under each of the scenarios to be planned; The weighted average distances of all candidate sites in each site combination to the charging demand points in the scenario to be planned, the total cost of each site combination, and the revenue of each site combination per unit time are quantified, and weighted by the first attribute, the second attribute, and the third attribute of the corresponding candidate site set, respectively, to obtain a site combination optimization model for each scenario to be planned.

[0043] Specifically, the present invention uses the evaluation values ​​representing the index evaluation results, cost index evaluation results and capacity index evaluation results in various scenarios as the first attribute, second attribute and third attribute of the candidate site set in the scenario, and selects a number of candidate sites from the candidate site sets in various scenarios according to preset selection rules (such as optimal solution, greedy algorithm, etc.), constructs the site combination in each scenario to be planned, considers the mutual influence between the site combinations (such as overlapping coverage, increased cost, etc.) when selecting, optimizes the site combination, and ensures that the constructed site combination achieves the lowest cost and the maximum benefit while meeting the needs.

[0044] Then, for the site combinations in various scenarios, the weighted average distance, total cost and revenue per unit time of all candidate sites to the charging demand points in the scenarios to be planned are quantified: Since the charging demand of each vehicle is fully met by the nearby fast charging stations, the location where electric vehicles are more distributed is regarded as a charging demand point. According to the vehicle distribution heat map, the number of fast charging station demand points in various scenarios can be determined, and according to the distance from the candidate sites in the site combination to the charging demand points, the proportion of the charging demand points that meet the candidate sites in the site combination can be determined (it must satisfy the constraint that the sum of all proportions is 1). Then the weighted average distance from all candidate sites in the site combination in various scenarios to the charging demand points in the scenario It can be expressed by the following formula: In the formula, is the number of charging demand points; Y is the number of charging demand points in the jth scenario to be planned; The candidate sites in the site combination under the corresponding scenario Meeting charging needs The proportion of charging demand.

[0045] The total cost of the site combination in various scenarios is the same as the cost described by the objective function above, which will not be elaborated here. The benefits generated by the candidate sites in the site combination in various scenarios per unit time are It can be expressed by the following formula: In the formula, is the unit time; The utilization rate of fast charging piles; is the capacity of the fast charging pile in the jth scenario to be planned; For charging price.

[0046] Finally, according to the first attribute, second attribute and third attribute of the candidate site set in various scenarios, the distance quantification result, cost quantification result and benefit quantification result in the scenario are weighted respectively, and the site combination optimization model in the scenario can be obtained, which is expressed by the following formula: In the formula, is the site combination optimization model for the jth scenario to be planned; are the first attribute, second attribute and third attribute of the candidate site set in the jth scenario to be planned.

[0047] in, The smaller the value, the more the selected site combination meets the site selection requirements. In addition, the site combination optimization model can also be obtained by weighting the distance quantification results, cost quantification results, and benefit quantification results in various scenarios through three different weight coefficients. The weight coefficient here is used to balance the convenience, economy, and profitability of various scenarios. Its specific value is adjusted according to the site consideration requirements.

[0048] The present invention converts complex site planning problems into specific mathematical models through quantitative evaluation and weighted processing, which facilitates fast and accurate calculation and analysis, thereby improving planning efficiency; according to the distribution of charging demand points, cost constraints and capacity requirements in each planning scenario, candidate sites are reasonably selected and site combinations are constructed to achieve optimal resource allocation; by optimizing the site combination, the coverage and quality of charging services are improved, thereby increasing the revenue per unit time; the solution can be flexibly adjusted and optimized according to actual needs, such as considering the dynamic changes of charging demand points, the elastic adjustment of site capacity, etc., to enhance the adaptability and flexibility of planning.

[0049] S5. Solve the optimization model of each site combination by an optimization algorithm to obtain the best site combination under each of the scenarios to be planned for combination, generate a fast charging station planning scheme for the area to be planned, and execute it; In one embodiment, solving each of the site combination optimization models by an optimization algorithm to obtain the best site combination in each of the scenarios to be planned includes: Solving the optimization model of each site combination by genetic algorithm to obtain the initial site combination and the initial combination optimization value under each scenario to be planned, and using each initial site combination as the current combination under each scenario to be planned to perform simulated annealing operation; Randomly selecting a candidate site from each of the candidate site sets to replace a candidate site in each of the current combinations, generating a neighborhood combination under each of the scenarios to be planned, and quantifying the neighborhood combination based on each of the site combination optimization models to obtain an optimization value of the neighborhood combination under each of the scenarios to be planned; The optimization value of each neighborhood combination is compared with the optimization value of each initial combination, and the current combination in each scenario to be planned is updated based on the result of the first comparison, so as to make a second comparison between the optimal combination formed by the updated current combination and the initial site combination in each scenario to be planned, and the optimal combination is updated according to the result of the second comparison, and the neighborhood combination generation step and the optimal combination update step are iteratively executed according to the preset annealing condition to obtain the best site combination in each scenario to be planned.

[0050] The present invention optimizes the site combination in various scenarios by using genetic algorithm and simulated annealing algorithm, specifically: First, the site combination optimization model of various scenarios is solved by genetic algorithm to obtain the initial site combination and initial combination optimization value under various scenarios: W site combinations are selected from the candidate data sets under various scenarios, each of which includes Q candidate sites, and the Q value is equal to the number of fast charging stations required in the scenario; the fitness value of the initial site combination under various scenarios is solved according to the site combination optimization model, and P site combinations with high fitness are selected to enter the next generation, and then W site combinations are randomly generated for iterative calculations. The iteration is stopped after the set conditions are met, and the combination with high fitness is used as the preliminary site combination under the jth scenario, and the fitness value of the combination is used as the initial combination optimization value, where W, Q, and P are positive integers greater than or equal to 1, and P is less than W; the specific steps are as follows: Initialize the population and randomly generate W site combinations from the candidate site sets in various scenarios as the first generation of data. The number of candidate sites in each site combination is the number of fast charging sites Q; The fitness value of each site combination is calculated according to the site combination optimization model, that is, the candidate value Z of Q candidate sites, and W candidate values ​​are calculated for W site combinations; Select the site combination with high fitness, that is, select the P site combinations with the smallest candidate value Z among the W candidate values, and enter the next generation of data; Generate a new generation of data through crossover and mutation to obtain new site combinations to increase the diversity of solutions; Repeat the selection, crossover, and mutation steps until the preset number of iterations is reached or the fitness value converges; Output the optimal solution, which is the primary site combination for the corresponding scenario , and use the fitness value of the combination as the initial combination optimization value.

[0051] Then, the initial site combination in various scenarios is used as the current combination of the scenario to perform simulated annealing operations, and the optimal site combination in various scenarios can be obtained: Combine the primary sites obtained by genetic algorithm As the current combination in the simulated annealing algorithm , the initial combination optimization value is used as the current combination optimization value , in the initial stage, with the current combination The best combination ,Right now , the corresponding current combined optimization value Optimize the value for the best combination ,Right now , and set the initial temperature , termination temperature and cooling rate α; Randomly select a candidate site from the candidate site set in various scenarios to replace the current combination in that scenario A candidate site in the , and calculate the neighborhood combination based on the site combination optimization model in this scenario The neighborhood combination optimization value , and obtain the optimal value of the neighborhood combination in various scenarios; Calculate the difference between the neighboring combined optimization value in various scenarios and the current combined optimization value in the scenario If the difference is less than 0, it means that the neighboring combination in this scenario is better, and the neighboring combination is used as the current combination, and then the current combination is assigned , ; If the difference is less than or equal to 0, it means that the neighboring combination in this scenario is poor and may be a suboptimal solution. The probability of the current temperature in this scenario is calculated, and the suboptimal solution is accepted with the probability at the current temperature. The probability at the current temperature is as shown in the following formula: In the formula, is the probability of the current temperature.

[0052] Setting random numbers ,like , then accept the suboptimal solution, update the current combination with the neighboring combination in this scenario, and use the neighboring combination optimization value as the current combination optimization value, that is, assign the current combination , ;like , the current combination remains unchanged, that is, the current combination is not updated. The purpose of accepting suboptimal solutions is to explore more solution spaces in the initial stage, avoiding falling into the local optimal solution too early and being unable to get out.

[0053] The updated current combination is compared with the optimal combination. When the updated current combination optimization value is less than the optimal combination optimization value, the updated current combination replaces the optimal combination as the updated optimal combination. When, update , . And according to the cooling rate α, the temperature is reduced , iteratively executes the steps of neighborhood construction, current combination update, and optimal combination update until the preset annealing condition, i.e., temperature, is reached. , the optimal combination after refinement and optimization is returned as the optimal site combination in this scenario; according to the above method, the optimal site combination in all scenarios in the area to be planned is calculated for combination, and the fast charging station planning scheme for the area to be planned can be obtained and executed. In addition, it should be noted that the optimal site combination can be optimized not only by using the above scheme, but also by using machine learning algorithms or other optimization algorithms, such as greedy algorithms and local search, heuristic algorithms and meta-heuristic algorithms, etc.

[0054] The present invention can effectively perform global search in the solution space through the selection, crossover and mutation operations of the genetic algorithm to avoid falling into the local optimal solution; through the neighborhood search and acceptance probability mechanism of the simulated annealing algorithm, it can perform fine local optimization on the basis of the initial solution obtained by the genetic algorithm to further improve the quality of the solution; the scheme combines the advantages of the genetic algorithm and the simulated annealing algorithm, has certain robustness for parameters such as the selection of the initial population, the crossover mutation operation, and the annealing temperature setting, can maintain good performance in different scenarios, and then perform adaptive adjustment according to different site combination optimization models, and is suitable for a variety of complex site planning problems; through the global search of the genetic algorithm and the local optimization of the simulated annealing algorithm, the scheme can find high-quality site combination solutions to meet actual needs, and maintain high computing efficiency while ensuring the quality of the solution.

[0055] In the embodiment of the present application, based on the problem of how to plan fast charging stations in multiple scenarios, a fast charging station planning method suitable for multiple scenarios is designed, which realizes scenario analysis of the planned area, obtains multiple scenarios to be planned, and quantifies the charging demand data of various types of electric vehicles in each scenario to be planned; quantifies the number of fast charging piles in each scenario to be planned according to each charging demand data, and combines it with the capacity of the fast charging piles in each scenario to be planned to quantify the fast charging station demand data in each scenario to be planned; based on the demand data of each fast charging station, points that meet the conditions for charging station construction in each scenario to be planned are used as candidate sites to construct a candidate site set in each scenario to be planned; selects a number of candidate sites from each candidate site set to form multiple site combinations, and constructs the site combination in each scenario to be planned based on each site combination Optimization model; solving the site combination optimization model through the optimization algorithm, obtaining the best site combination under each of the to-be-planned scenarios for combination, generating a fast charging station planning scheme for the to-be-planned area and executing the technical scheme; by analyzing different scenarios, calculating the number of fast charging stations under different scenarios, ensuring the reasonable number of fast charging stations under different scenarios, setting up a set of candidate sites with the conditions for charging station construction, analyzing the average distance from the candidate site to the vehicle demand point, the cost of the candidate site and the revenue of the candidate site, and achieving comprehensive consideration of the candidate sites, by establishing a site combination optimization model, using genetic algorithms and simulated annealing algorithms, obtaining the best site combination under different scenarios from the candidate site set, improving the rationality and scientificity of the fast charging station layout, and distinguishing the charging needs under different scenarios and the points with the conditions for building fast charging stations, so as to reasonably and scientifically realize the layout of fast charging stations.

[0056] It should be noted that although the steps in the above flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0057] In another embodiment, if Figure 2 As shown, the second aspect of the present invention provides a fast charging station planning system applicable to multiple scenarios, including: The first demand quantification module 10 is used to perform scenario analysis on the area to be planned, obtain multiple scenarios to be planned, and quantify charging demand data of multiple types of electric vehicles in each of the scenarios to be planned; The second demand quantification module 20 is used to quantify the number of fast charging piles in each of the scenarios to be planned according to each of the charging demand data, and combine it with the capacity of the fast charging piles in each of the scenarios to be planned to quantify the fast charging station demand data in each of the scenarios to be planned; A site set construction module 30 is used to select points with charging station construction conditions in each of the scenarios to be planned as candidate sites based on the demand data of each fast charging station, so as to construct a candidate site set in each of the scenarios to be planned; The optimization model building module 40 is used to select a plurality of candidate sites from each of the candidate site sets to form a plurality of site combinations, and to build a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations; The optimization model solving module 50 is used to solve the optimization model of each site combination through an optimization algorithm, obtain the best site combination under each of the scenarios to be planned for combination, generate a fast charging station planning plan for the area to be planned, and execute it.

[0058] It should be noted that each module in the above-mentioned fast charging station planning system suitable for multiple scenarios can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of a fast charging station planning system suitable for multiple scenarios, please refer to the above definition of a fast charging station planning method suitable for multiple scenarios. The two have the same functions and effects, which will not be repeated here.

[0059] A third aspect of the present invention provides an electronic device, the electronic device comprising: processor, memory, and bus; The bus is used to connect the processor and the memory; The memory is used to store operation instructions; The processor is used to call the operation instruction, and the executable instruction enables the processor to perform operations corresponding to a fast charging station planning method applicable to multiple scenarios as shown in the first aspect of the present application.

[0060] In an alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The electronic device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, such as through a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that in actual applications, the transceiver 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation on the embodiments of the present application.

[0061] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0062] The bus 5002 may include a path to transmit information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0063] The memory 5003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0064] The memory 5003 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0065] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0066] The fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, a fast charging station planning method suitable for multiple scenarios as shown in the first aspect of the present application is implemented.

[0067] Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0068] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0069] In summary, the present invention relates to the technical field of fast charging station layout, and discloses a fast charging station planning method, system, equipment and medium suitable for multiple scenarios, which divides the area to be planned into multiple scenarios to be planned, and quantifies the number of fast charging piles in the scenario according to the charging demand data of various types of electric vehicles in each scenario to be planned, and quantifies the fast charging station demand data in the scenario in combination with the fast charging pile capacity in various scenarios; takes points with charging station construction conditions in various scenarios as candidate sites to construct candidate site sets in various scenarios, and selects several candidate sites to form multiple site combinations, so as to construct site combination optimization models in various scenarios based on these site combinations; solves these site combination optimization models through optimization algorithms, obtains the best site combination and combines it, generates a fast charging station planning scheme for the area to be planned for execution, and can reasonably and scientifically realize the layout of fast charging stations by distinguishing the charging needs in different scenarios and the points with the conditions for building fast charging stations.

[0070] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0071] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A fast charging station planning method applicable to multiple scenarios, characterized in that: include: Performing scenario analysis on the area to be planned to obtain multiple scenarios to be planned, and quantifying charging demand data of multiple types of electric vehicles under each of the scenarios to be planned; Quantify the number of fast charging piles in each of the scenarios to be planned according to each of the charging demand data, and combine it with the capacity of the fast charging piles in each of the scenarios to be planned to quantify the fast charging station demand data in each of the scenarios to be planned; Based on the demand data of each fast charging station, the points that meet the conditions for building a charging station in each of the scenarios to be planned are taken as candidate sites to construct a set of candidate sites in each of the scenarios to be planned; Selecting a plurality of candidate sites from each of the candidate site sets to form a plurality of site combinations, and constructing a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations; The optimization model of each site combination is solved by an optimization algorithm to obtain the best site combination in each scenario to be planned for combination, and a fast charging station planning plan for the area to be planned is generated and executed.

2. A fast charging station planning method applicable to multiple scenarios according to claim 1, characterized in that: The scenario analysis is performed on the area to be planned to obtain multiple scenarios to be planned, and charging demand data of multiple types of electric vehicles in each of the scenarios to be planned are quantified, including: Performing scenario analysis on the area to be planned according to the function to obtain multiple scenarios to be planned; the scenarios to be planned include residential areas, commercial areas, office areas and tourist areas; Quantify the average daily power consumption data of various types of electric vehicles and their ownership in each of the scenarios to be planned, and combine them with the external charging ratio of various types of electric vehicles to quantify the average daily charging demand data of various types of electric vehicles in each of the scenarios to be planned; The daily average charging demand data of various types of electric vehicles in each of the scenarios to be planned are combined to obtain the charging demand data of all types of electric vehicles in each of the scenarios to be planned.

3. According to claim 1, a fast charging station planning method applicable to multiple scenarios is characterized in that: The number of fast charging piles in each of the scenarios to be planned is expressed by the following formula: In the formula, is the number of fast charging piles in the jth scenario to be planned; is the charging demand data under the jth scenario to be planned; The charging power of each fast charging pile; The utilization rate of fast charging piles; , are respectively the duration of the charging peak and the charging peak demand coefficient under the jth scenario to be planned.

4. According to claim 1, a fast charging station planning method applicable to multiple scenarios is characterized in that: Based on the demand data of each fast charging station, the points with charging station construction conditions in each of the scenarios to be planned are taken as candidate sites to construct a set of candidate sites in each of the scenarios to be planned, including: Obtaining geographic location data, construction cost data, and fast charging pile capacity of each of the scenarios to be planned to construct an evaluation index system, and taking points that meet the conditions for charging station construction in each of the scenarios to be planned as candidate sites; Evaluate each of the candidate sites according to the evaluation index system to obtain preliminary candidate sites for each of the scenarios to be planned according to the evaluation results; Based on the geographical location data of each of the scenarios to be planned, cluster analysis is performed on each of the preliminary candidate sites by using a K-means clustering algorithm to obtain clustered candidate sites under each of the scenarios to be planned; Constructing constraint conditions according to the demand data of each fast charging station and the capacity of each fast charging pile, and constructing an objective function through the construction cost data; Each of the cluster candidate sites is used as an input of the particle swarm optimization algorithm for iterative search, and in each iteration, the pros and cons of each of the cluster candidate sites are evaluated according to the objective function and the constraint conditions, so as to select the optimal cluster candidate site combination in each of the scenarios to be planned as the final candidate site set.

5. A fast charging station planning method applicable to multiple scenarios according to claim 4, characterized in that: The evaluation index system includes location index, cost index and capacity index; the evaluation results include location index evaluation results, cost index evaluation results and capacity index evaluation results; wherein, The selecting a plurality of candidate sites from each of the candidate site sets to form a plurality of site combinations, and constructing a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations, includes: The index evaluation result, cost index evaluation result and capacity index evaluation result of each of the scenarios to be planned are respectively used as the first attribute, the second attribute and the third attribute of the corresponding candidate site set, and a number of candidate sites are respectively selected from the candidate site set under each of the scenarios to be planned to construct a site combination under each of the scenarios to be planned; The weighted average distances of all candidate sites in each site combination to the charging demand points in the scenario to be planned, the total cost of each site combination, and the revenue of each site combination per unit time are quantified, and weighted by the first attribute, the second attribute, and the third attribute of the corresponding candidate site set, respectively, to obtain the site combination optimization model for each scenario to be planned.

6. A method for planning fast charging stations applicable to multiple scenarios according to claim 5, characterized in that: The site combination optimization model is expressed by the following formula: In the formula, is the site combination optimization model for the jth scenario to be planned; is the weighted average distance from all candidate sites in the site combination under the jth scenario to be planned to the charging demand point under this scenario; is the total cost of the site combination under the jth scenario to be planned; is the revenue per unit time of the site combination in the jth scenario to be planned; are the first attribute, second attribute and third attribute of the candidate site set in the jth scenario to be planned, respectively; is the candidate site number; X is the demand data of fast charging stations in the jth scenario to be planned; is the number of charging demand points; Y is the number of charging demand points in the jth scenario to be planned; Candidate sites in the site combination Meeting charging needs The proportion of charging demand; is the candidate site in the site combination under the jth scenario to be planned land cost; is the candidate site in the site combination under the jth scenario to be planned The procurement cost of fast charging equipment; is the candidate site in the site combination under the jth scenario to be planned The cost of retrofitting to the grid; is the candidate site in the site combination under the jth scenario to be planned Operation and maintenance costs; T is the unit time; The utilization rate of fast charging piles; is the capacity of the fast charging pile in the jth scenario to be planned; For charging price.

7. A method for planning fast charging stations applicable to multiple scenarios according to claim 1, characterized in that: Solving the site combination optimization model by using an optimization algorithm to obtain the best site combination in each of the scenarios to be planned includes: Solving the optimization model of each site combination by genetic algorithm to obtain the initial site combination and the initial combination optimization value under each scenario to be planned, and using each initial site combination as the current combination under each scenario to be planned to perform simulated annealing operation; Randomly selecting a candidate site from each of the candidate site sets to replace a candidate site in each of the current combinations, generating a neighborhood combination under each of the scenarios to be planned, and quantifying the neighborhood combination based on each of the site combination optimization models to obtain an optimization value of the neighborhood combination under each of the scenarios to be planned; The optimization value of each neighborhood combination is compared with the optimization value of each initial combination, and the current combination in each scenario to be planned is updated based on the result of the first comparison, so as to make a second comparison between the optimal combination formed by the updated current combination and the initial site combination in each scenario to be planned, and the optimal combination is updated according to the result of the second comparison, and the neighborhood combination generation step and the optimal combination update step are iteratively executed according to the preset annealing condition to obtain the best site combination in each scenario to be planned.

8. A fast charging station planning system suitable for multiple scenarios, characterized in that: include: The first demand quantification module is used to perform scenario analysis on the area to be planned, obtain multiple scenarios to be planned, and quantify the charging demand data of multiple types of electric vehicles in each of the scenarios to be planned; A second demand quantification module is used to quantify the number of fast charging piles in each of the scenarios to be planned according to each of the charging demand data, and combine it with the capacity of the fast charging piles in each of the scenarios to be planned to quantify the fast charging station demand data in each of the scenarios to be planned; A site set construction module, for taking points with charging station construction conditions in each of the scenarios to be planned as candidate sites based on the demand data of each of the fast charging stations, so as to construct a candidate site set in each of the scenarios to be planned; An optimization model building module, used to select a number of candidate sites from each of the candidate site sets to form a plurality of site combinations, and to build a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations; The optimization model solving module is used to solve the optimization model of each site combination through an optimization algorithm, obtain the best site combination under each scenario to be planned for combination, generate a fast charging station planning plan for the area to be planned, and execute it.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the fast charging station planning method applicable to multiple scenarios as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the fast charging station planning method applicable to multiple scenarios as described in any one of claims 1 to 7 is implemented.

Images