A fast charging station planning method, system, device and medium applicable to multiple scenarios

Abstract

The present invention discloses a fast charging station planning method, system, device and medium applicable to multiple scenarios. The area to be planned is divided into multiple scenarios to be planned, and the number of fast charging piles in each scenario is quantified according to the charging demand data of multiple types of electric vehicles in each scenario to be planned. The fast charging station demand data in each scenario is quantified by combining the capacities of the fast charging piles in various scenarios. The points with the conditions for building charging stations in various scenarios are used as candidate sites to construct candidate site sets in various scenarios, and several candidate sites are selected from them to form multiple site combinations, so as to construct site combination optimization models in various scenarios based on these site combinations. The optimization algorithm is used to solve these site combination optimization models to obtain the best site combination and combine them to generate a fast charging station planning scheme for the area to be planned for execution. By distinguishing the charging demands in different scenarios and the points with the conditions for building fast charging stations, the layout of fast charging stations can be realized reasonably and scientifically.

Description

Technical Field

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

[0002] With the popularization of electric vehicles, the demand for charging facilities is also increasing continuously. Reasonably arranging fast charging stations is crucial for meeting user needs, reducing costs and improving operation efficiency. At present, for the planning problem of charging stations, the optimization objectives mainly include the time and space distribution of the charging load of electric vehicles in the planned area, the power quality of the power grid and the construction cost, so as to determine the optimal selection of the reasonable location of electric vehicle charging stations. However, this method is difficult to meet the actual application needs in different scenarios, lacks accurate prediction of multi-dimensional needs, and cannot realize the layout design based on comprehensive optimization objectives.

[0003] Therefore, how to plan fast charging stations in multiple scenarios has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

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

[0005] To solve the above technical problem, the first aspect of the present invention provides a fast charging station planning method applicable to multiple scenarios, including:

[0006] Conduct scenario analysis on the area to be planned to obtain multiple scenarios to be planned, and quantify the charging demand data of various types of electric vehicles in each of the scenarios to be planned;

[0007] Quantify the number of fast charging piles in each of the scenarios to be planned according to the charging demand data, and combine it with the capacity of the fast charging piles in each of the scenarios to be planned, so as to quantify the fast charging station demand data in each of the scenarios to be planned;

[0008] Based on the fast charging station demand data, take the points with the conditions for building charging stations in each of the scenarios to be planned as candidate sites, so as to construct a candidate site set in each of the scenarios to be planned;

[0009] Select several candidate sites from each of the candidate site sets to form multiple site combinations, and construct a site combination optimization model in each of the scenarios to be planned based on each of the site combinations;

[0010] Solve each of the site combination optimization models through an optimization algorithm to obtain the best site combination in 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.

[0011] As one of the preferred solutions, perform scenario analysis on the area to be planned to obtain multiple scenarios to be planned, and quantify the charging demand data of various types of electric vehicles under each of the scenarios to be planned, including:

[0012] Perform scenario analysis on the area to be planned according to functions to obtain multiple scenarios to be planned; the scenarios to be planned include residential areas, commercial areas, office areas, and tourist areas;

[0013] Quantify the daily power consumption data of various types of electric vehicles and their ownership under each of the scenarios to be planned, and combine with the off-board charging ratio of various types of electric vehicles to quantify the daily charging demand data of various types of electric vehicles under each of the scenarios to be planned;

[0014] Combine the daily charging demand data of various types of electric vehicles under each of the scenarios to be planned to obtain the charging demand data of all types of electric vehicles under each of the scenarios to be planned.

[0015] As one of the preferred solutions, the number of fast charging piles under each of the scenarios to be planned is expressed by the following formula:

[0016]

[0017] In the formula, is the number of fast charging piles under the j-th scenario to be planned; is the charging demand data under the j-th scenario to be planned; is the charging power of each fast charging pile; is the utilization rate of the fast charging pile; , are the duration of the charging peak and the charging peak demand coefficient under the j-th scenario to be planned, respectively.

[0018] As one of the preferred solutions, based on the demand data of each fast charging station, take the points where charging stations can be built under each of the scenarios to be planned as candidate sites to construct a set of candidate sites for each of the scenarios to be planned, including:

[0019] Obtain the geographical 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 take the points where charging stations can be built under each of the scenarios to be planned as candidate sites;

[0020] Evaluate each of the candidate sites according to the evaluation index system to obtain the preliminary candidate sites for each of the scenarios to be planned based on the evaluation results;

[0021] Based on the geographical location data of each of the to-be-planned scenarios, perform clustering analysis on each of the preliminary candidate sites through the K-means clustering algorithm to obtain the clustering candidate sites under each of the to-be-planned scenarios;

[0022] Construct constraint conditions according to each of the fast charging station demand data and each of the fast charging pile capacities, and construct an objective function through each of the construction cost data;

[0023] Use each of the clustering candidate sites as the input of the particle swarm optimization algorithm for iterative search, and in each iteration, evaluate the advantages and disadvantages of each of the clustering candidate sites according to the objective function and the constraint conditions, so as to select the optimal combination of clustering candidate sites under each of the to-be-planned scenarios as the final candidate site set.

[0024] As one of the preferred solutions, the evaluation index system includes a location index, a cost index, and a capacity index; the evaluation results include a location index evaluation result, a cost index evaluation result, and a capacity index evaluation result; among them,

[0025] Selecting several candidate sites from each of the candidate site sets to form multiple site combinations, and constructing a site combination optimization model under each of the to-be-planned scenarios based on each of the site combinations, includes:

[0026] Respectively use the index evaluation result, the cost index evaluation result, and the capacity index evaluation result of each of the to-be-planned scenarios as the first attribute, the second attribute, and the third attribute of the corresponding candidate site set, and select several candidate sites from each of the candidate site sets under each of the to-be-planned scenarios to construct a site combination under each of the to-be-planned scenarios;

[0027] Quantify the weighted average distance from all candidate sites in each of the site combinations to the charging demand points under their respective to-be-planned scenarios, the total cost of each of the site combinations, and the revenue of each of the site combinations per unit time, and perform weighting through the first attribute, the second attribute, and the third attribute of the corresponding candidate site set respectively to obtain the site combination optimization model under each of the to-be-planned scenarios.

[0028] As one of the preferred solutions, the site combination optimization model is represented by the following formula:

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, is the site combination optimization model for the j-th scenario to be planned; is the weighted average distance from all candidate sites in the site combination for the j-th scenario to be planned to the charging demand points in this scenario; is the total cost of the site combination for the j-th scenario to be planned; is the revenue of the site combination for the j-th scenario to be planned per unit time; are respectively the first attribute, the second attribute, and the third attribute of the candidate site set for the j-th scenario to be planned; is the candidate site number; X is the fast charging station demand data for the j-th scenario to be planned; is the charging demand point number; Y is the number of charging demand points for the j-th scenario to be planned; is the candidate site in the site combination meets the charging demand point the proportion of charging demand; is the candidate site in the site combination for the j-th scenario to be planned the land cost; is the candidate site in the site combination for the j-th scenario to be planned the procurement cost of fast charging equipment; is the candidate site in the site combination for the j-th scenario to be planned the transformation cost of connecting to the power grid; is the candidate site in the site combination for the j-th scenario to be planned the operation and maintenance cost; T is the unit time; is the utilization rate of fast charging piles; is the capacity of fast charging piles for the j-th scenario to be planned; is the charging price.

[0034] As one of the preferred solutions, solving each of the site combination optimization models through an optimization algorithm to obtain the best site combination for each of the scenarios to be planned includes:

[0035] Solving each of the site combination optimization models through a genetic algorithm to obtain the initial site combination and the initial combination optimization value for each of the scenarios to be planned, and taking each of the initial site combinations as the current combination for each of the scenarios to be planned to perform simulated annealing operations;

[0036] Randomly select a candidate site from each of the candidate site sets to replace a candidate site in each of the current combinations, generate the neighborhood combinations for each of the scenarios to be planned, and quantify the neighborhood combinations based on each of the site combination optimization models to obtain the neighborhood combination optimization values for each of the scenarios to be planned;

[0037] Compare each of the neighborhood combination optimization values with each of the initial combination optimization values once, update the current combination under each of the scenarios to be planned based on the result of the one-time comparison, so as to perform a secondary comparison with the optimal combination formed by the updated current combination and the initial site combination under each of the scenarios to be planned, update the optimal combination according to the result of the secondary comparison, and iteratively execute the neighborhood combination generation step and the optimal combination update step according to the preset annealing condition to obtain the best site combination under each of the scenarios to be planned.

[0038] The second aspect of the present invention provides a fast charging station planning system applicable to multiple scenarios, including:

[0039] A first demand quantification module, configured to perform scenario analysis on the area to be planned to obtain a plurality of scenarios to be planned, and quantify the charging demand data of various types of electric vehicles under each of the scenarios to be planned;

[0040] A second demand quantification module, configured to quantify the number of fast charging piles under each of the scenarios to be planned according to each of the charging demand data, and combine with the capacity of the fast charging piles under each of the scenarios to be planned, so as to quantify the fast charging station demand data under each of the scenarios to be planned;

[0041] A site set construction module, configured to, based on each of the fast charging station demand data, use the points with the conditions for building a charging station under each of the scenarios to be planned as candidate sites, so as to construct a candidate site set under each of the scenarios to be planned;

[0042] An optimization model construction module, configured to respectively select several candidate sites from each of the candidate site sets to form a plurality of site combinations, and construct a site combination optimization model under each of the scenarios to be planned based on each of the site combinations;

[0043] An optimization model solving module, configured to solve each of the site combination optimization models 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 scheme for the area to be planned and execute it.

[0044] The third aspect of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the fast charging station planning method applicable to multiple scenarios as described above.

[0045] The fourth aspect of the present invention provides a computer-readable storage medium, which includes a stored computer program. When the device where the computer-readable storage medium is located executes the computer program, it implements the fast charging station planning method applicable to multiple scenarios as described above.

[0046] Another embodiment of the present invention provides a computer program product, including computer programs / instructions, which, when executed by a processor, implement the steps of the fast charging station planning method applicable to multiple scenarios as described above.

[0047] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0048] (1) Through scenario analysis and quantification of charging demands, the characteristics of charging demands in the area to be planned can be grasped more accurately, providing a scientific basis for the planning of fast charging stations; combined with the planning of the number and capacity of fast charging piles, it can ensure that the construction of fast charging stations can meet the actual demands, avoiding waste or shortage of resources;

[0049] (2) Through the screening of candidate sites and the optimization of site combinations, the optimal site layout plan can be selected, improving the coverage rate and utilization efficiency of fast charging stations. By distinguishing the charging demands in different scenarios and the locations where fast charging stations can be built, the layout of fast charging stations can be realized reasonably and scientifically. Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for implementation will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 is a flowchart of a fast charging station planning method applicable to multiple scenarios provided by an embodiment of the present invention;

[0052] Figure 2 is a structural diagram of a fast charging station planning system applicable to multiple scenarios provided by an embodiment of the present invention;

[0053] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present invention. Detailed Embodiments

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all 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 those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0055] In the description of this application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0056] In the description of this application, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", "coupled" 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 directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for illustrative purposes and do not indicate or imply that the indicated system or component must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more of the 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.

[0057] In the description of this application, it should be noted that unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by those skilled in the technical field to which this invention belongs. The terms used in the specification of this invention are only for the purpose of describing specific embodiments and are not intended to limit this invention. 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.

[0058] In one embodiment, as Figure 1 shown, the first aspect of the present invention provides a fast charging station planning method applicable to multiple scenarios, including:

[0059] S1. Conduct a scenario analysis on the area to be planned to obtain a plurality of scenarios to be planned, and quantify the charging demand data of various types of electric vehicles under each of the scenarios to be planned;

[0060] In one embodiment, step S1 includes:

[0061] Conduct a scenario analysis on the area to be planned according to functions to obtain a plurality of scenarios to be planned; the scenarios to be planned include residential areas, commercial areas, office areas, and tourist areas;

[0062] 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;

[0063] 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.

[0064] 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.

[0065] 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:

[0066]

[0067] 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.

[0068] 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:

[0069]

[0070] 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.

[0071] The vehicle distribution regularity 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.

[0072] 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:

[0073]

[0074] 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.

[0075] 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:

[0076]

[0077] 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.

[0078] 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:

[0079]

[0080] 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.

[0081] 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:

[0082]

[0083] Where n is the number of electric vehicle types.

[0084] 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.

[0085] 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;

[0086] 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:

[0087]

[0088] 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:

[0089]

[0090]

[0091] 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 volume of the charging station in the j-th scenario obtained through research; is the all-day charging volume of the charging station in the j-th scenario obtained through research.

[0092] Subsequently, according to the number of fast-charging piles and the capacity of fast-charging piles at a single site in various scenarios, calculate the number of fast-charging stations required in this scenario , as shown in the following formula:

[0093]

[0094] In the formula, is the capacity of the fast-charging piles at a single site in the j-th scenario, and this value depends on the site scale of different scenarios.

[0095] S3. Based on the demand data of each fast-charging station, use the points that meet the conditions for building a charging station in each to-be-planned scenario as candidate sites to construct a set of candidate sites for each to-be-planned scenario;

[0096] In one embodiment, step S3 includes:

[0097] Obtain the geographical location data, construction cost data, and fast-charging pile capacity of each to-be-planned scenario to construct an evaluation index system, and use the points that meet the conditions for building a charging station in each to-be-planned scenario as candidate sites; among them, the evaluation index system includes a location index, a cost index, and a capacity index;

[0098] Evaluate each candidate site according to the evaluation index system to obtain the preliminary candidate sites for each to-be-planned scenario based on the evaluation results; among them, the evaluation results include the evaluation results of the location index, the cost index, and the capacity index;

[0099] Based on the geographical location data of each to-be-planned scenario, perform clustering analysis on each preliminary candidate site through the K-means clustering algorithm to obtain the clustered candidate sites for each to-be-planned scenario;

[0100] Construct constraint conditions according to the demand data of each fast-charging station and the capacity of each fast-charging pile, and construct an objective function through the construction cost data of each;

[0101] Use each clustered candidate site as the input of the particle swarm optimization algorithm for iterative search, and in each iteration, evaluate the advantages and disadvantages of each clustered candidate site according to the objective function and the constraint conditions to select the optimal combination of clustered candidate sites for each to-be-planned scenario as the final set of candidate sites.

[0102] Specifically, the present invention constructs an evaluation index system by obtaining geographical 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 for various scenarios. This system includes a location index (evaluating the accessibility of the site, surrounding traffic conditions, etc.), a cost index (evaluating the construction cost, operation cost, etc. of the site), and a capacity index (evaluating the number of fast charging piles and the total charging capacity that the site can provide). The above-mentioned various indexes can be represented by formulas, models, etc., so as to use these evaluation indexes to screen candidate sites in various scenarios and obtain evaluation values corresponding to each candidate site for characterizing various evaluation results.

[0103] Identify the points with the conditions for building a charging station in various scenarios and use them as candidate sites for that scenario. The conditions for having the conditions for building a charging station should meet the following several: having a public ground parking lot, and the number of its 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. For example, 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, evaluate each candidate site in various scenarios to obtain the evaluation results of the location index, cost index, and capacity index of each candidate site, and comprehensively evaluate the results of these three indexes to compare them with the preset location, cost, and capacity thresholds, and then screen out the preliminary candidate sites in various scenarios according to the comparison results.

[0104] Then, based on the geographical location data of various scenarios, use the K-means clustering algorithm to perform clustering analysis on the preliminary candidate sites in various scenarios: determine the value of K (i.e., the number of clusters), randomly select K preliminary candidate sites as the initial cluster centers, calculate the distance from each preliminary candidate site in various scenarios to each cluster center, 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 then various ones can be obtained. By clustering these sites, they can be relatively concentrated geographically, which is convenient for subsequent optimization and planning.

[0105] According to the fast charging station demand data and fast charging pile capacity data, construct constraint conditions, including the charging demand that each charging station needs to meet, the number and capacity limits of fast charging piles, etc., and use the construction cost data to construct an objective function, aiming to minimize the fixed cost, equipment cost, grid access cost and operation cost of the candidate sites, which is represented by the following formula:

[0106]

[0107]

[0108] In the formula, is the total cost of the site combination in the j-th scenario; is the candidate site number; X is the fast charging station demand data in the j-th scenario; is the candidate site in the site combination in the j-th scenario land cost; is the candidate site in the site combination in the j-th scenario procurement cost of fast charging equipment; is the candidate site in the site combination in the j-th scenario retrofit cost for grid connection; is the candidate site in the site combination in the j-th scenario operation and maintenance cost.

[0109] Finally, the candidate sites of each cluster in various scenarios are used as the input of the particle swarm optimization algorithm, and iterative search is carried out through the particle swarm algorithm. In each iteration, the objective function and constraint conditions are used to evaluate the advantages and disadvantages of each cluster candidate site to adjust the speed and position of the particles, so that they continuously approach the optimal solution. Finally, the optimal cluster candidate site combination in each scenario to be planned is selected as the final candidate site set for output.

[0110] By constructing an evaluation index system, the present invention can quickly screen out eligible candidate sites, reducing blindness and uncertainty in the planning process; using the K-means clustering algorithm to perform clustering analysis on candidate sites makes the sites more concentrated and reasonable geographically, improving the coverage rate and utilization efficiency of charging facilities; searching for the optimal site combination through the particle swarm optimization algorithm can minimize the total construction cost of the charging station on the premise of meeting the charging demand, improving the economy of the planning; the optimized site layout and reasonable configuration of fast charging pile capacity can shorten the charging waiting time of users, improving the convenience and satisfaction of charging services.

[0111] S4. Select several candidate sites from each of the candidate site sets to form multiple site combinations, and construct an optimization model for the site combination in each of the scenarios to be planned based on each of the site combinations;

[0112] In one embodiment, step S4 includes:

[0113] Respectively use the index evaluation results, cost index evaluation results, and capacity index evaluation results of each scenario to be planned as the first attribute, second attribute, and third attribute of the corresponding candidate site set, and select several candidate sites from each of the candidate site sets in each scenario to be planned to construct each site combination in each scenario to be planned;

[0114] Quantify the weighted average distance from all candidate sites in each of the site combinations to the charging demand points in the to-be-planned scenario to which they belong, the total cost of each of the site combinations, and the revenue of each of the site combinations per unit time, and weight them respectively through the first attribute, the second attribute, and the third attribute of the corresponding candidate site set, to obtain the site combination optimization model for each of the to-be-planned scenarios.

[0115] Specifically, the present invention uses the evaluation values characterizing the index evaluation results, the cost index evaluation results, and the capacity index evaluation results in various scenarios as the first attribute, the second attribute, and the third attribute of the candidate site set in this scenario respectively, and selects several candidate sites from the candidate site sets in various scenarios according to a preset selection rule (such as the optimal solution, the greedy algorithm, etc.) to construct the site combinations for each of the to-be-planned scenarios. When selecting, consider the mutual influence between the site combinations (such as overlapping coverage range, cost increase, etc.), optimize the site combinations, and ensure that the constructed site combinations meet the requirements while achieving the lowest cost and the greatest revenue.

[0116] Then, for the site combinations in various scenarios, quantify the weighted average distance from all candidate sites to the charging demand points in the to-be-planned scenario to which they belong, the total cost, and the revenue per unit time: Since the charging demand of each vehicle is completely met by the nearby fast charging stations, then regard the positions with a large distribution of electric vehicles 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 of the candidate sites in the site combination that meets the charging demand points can be determined (which needs to meet the constraint that the sum of all proportions is 1). Then, the weighted average distance from all candidate sites in the site combinations in various scenarios to the charging demand points in this scenario can be expressed by the following formula:

[0117]

[0118] In the formula, is the charging demand point number; Y is the number of charging demand points in the j-th to-be-planned scenario; is the candidate site in the site combination in the corresponding scenario meeting the charging demand of the charging demand point charging demand ratio.

[0119] The total cost of the site combinations in various scenarios is the same as the cost described in the above objective function, which will not be elaborated here. And the revenue generated by the candidate sites in the site combinations in various scenarios per unit time can be expressed by the following formula:

[0120]

[0121] In the formula, is the unit time; is the utilization rate of the fast charging pile; is the capacity of the fast charging pile under the j-th scenario to be planned; is the charging price.

[0122] Finally, according to the first attribute, second attribute, and third attribute of the candidate site set under various scenarios, the distance quantization result, cost quantization result, and revenue quantization result under this scenario are weighted respectively, and the site combination optimization model under this scenario can be obtained, which is represented by the following formula:

[0123]

[0124] In the formula, is the site combination optimization model for the j-th scenario to be planned; are respectively the first attribute, second attribute, and third attribute of the candidate site set under the j-th scenario to be planned.

[0125] Among them, 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 quantization result, cost quantization result, and revenue quantization result under various scenarios with three different weight coefficients respectively. The weight coefficients here are used to balance the convenience, economy, and profitability of various scenarios, and their specific values are adjusted according to the site selection requirements.

[0126] Through quantitative evaluation and weighting processing, the present invention transforms the complex site planning problem into a specific mathematical model, which is convenient for rapid and accurate calculation and analysis, thereby improving the planning efficiency; according to the distribution of charging demand points, cost constraints, and capacity requirements under each scenario to be planned, candidate sites are reasonably selected and a site combination is constructed to achieve the optimal allocation of resources; by optimizing the site combination, the coverage rate and quality of charging services are improved, thereby increasing the revenue per unit time; this solution can be flexibly adjusted and optimized according to actual needs, such as considering the dynamic changes of charging demand points and the elastic adjustment of site capacity, etc., enhancing the adaptability and flexibility of the planning.

[0127] S5. Solve each of the site combination optimization models through 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;

[0128] In one embodiment, the solving each of the site combination optimization models through an optimization algorithm to obtain the best site combination under each of the scenarios to be planned includes:

[0129] Solve each of the site combination optimization models through a genetic algorithm to obtain the initial site combinations and initial combination optimization values for each of the to-be-planned scenarios, and use each of the initial site combinations as the current combination for each of the to-be-planned scenarios to perform simulated annealing operations;

[0130] Randomly select a candidate site from each of the candidate site sets to replace one candidate site in each of the current combinations, generate neighborhood combinations for each of the to-be-planned scenarios, and quantify the neighborhood combinations based on each of the site combination optimization models to obtain the neighborhood combination optimization values for each of the to-be-planned scenarios;

[0131] Compare each of the neighborhood combination optimization values with each of the initial combination optimization values once, update the current combination for each of the to-be-planned scenarios based on the result of the first comparison, perform a second comparison on the optimal combination formed by the updated current combination and the initial site combinations for each of the to-be-planned scenarios, update the optimal combination based on the result of the second comparison, and iteratively execute the neighborhood combination generation step and the optimal combination update step according to the preset annealing conditions to obtain the best site combinations for each of the to-be-planned scenarios.

[0132] The present invention optimizes the site combinations in various scenarios through a genetic algorithm and a simulated annealing algorithm, specifically as follows:

[0133] First, solve the site combination optimization models for various scenarios through a genetic algorithm to obtain the initial site combinations and initial combination optimization values for various scenarios: select W site combinations from the candidate data sets for various scenarios, each site combination includes Q candidate sites, and the value of Q is equal to the fast charging station demand quantity for that scenario; solve the fitness values of the initial site combinations for various scenarios according to the site combination optimization model, select P site combinations with high fitness to enter the next generation, then randomly generate W site combinations, perform iterative operations, and stop the iteration after meeting the set conditions, use the combination with high fitness as the primary selected site combination for the jth scenario, and use the fitness value of this combination 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:

[0134] Initialize the population, randomly generate W site combinations in the candidate site sets for various scenarios as the first-generation data, and the number of candidate sites in each site combination is the fast charging station number Q;

[0135] Calculate the fitness value of each site combination according to the site combination optimization model, that is, the candidate value Z of Q candidate sites, and calculate W candidate values for W site combinations;

[0136] Select the site combinations with high fitness, that is, select P site combinations with smaller candidate values Z among the W candidate values to enter the next-generation data;

[0137] Generate a new generation of data through crossover and mutation to obtain a new site combination, so as to increase the diversity of solutions;

[0138] Repeat the steps of selection, crossover, and mutation until the preset number of iterations is reached or the fitness value converges;

[0139] Output the optimal solution, which is the primary selected site combination under the corresponding scenario , and use the fitness value of this combination as the initial combination optimization value.

[0140] Then, use the initial site combinations under various scenarios as the current combinations for simulated annealing operations in this scenario, and the optimal site combinations under various scenarios can be obtained:

[0141] Take the primary selected site combination obtained through the genetic algorithm as the current combination in the simulated annealing algorithm , use the primary combination optimization value as the current combination optimization value , in the initial stage, use the current combination as the optimal combination , that is , the corresponding current combination optimization value is the optimal combination optimization value , that is , and set the initial temperature , the termination temperature and the cooling rate α;

[0142] Randomly select a candidate site from the candidate site sets under various scenarios to replace one candidate site in the current combination in this scenario to generate a neighborhood combination , and calculate the neighborhood combination optimization value of the neighborhood combination based on the site combination optimization model in this scenario , to obtain the neighborhood combination optimization values under various scenarios;

[0143] Calculate the difference between the neighborhood combination optimization values under various scenarios and the current combination optimization value in this scenario , if this difference is less than 0, it means that the neighborhood combination in this scenario is better, and use the neighborhood combination as the current combination, and then assign a value to the current combination , ; if this difference is greater than 0, it means that the neighborhood combination in this scenario is worse, and it can be a sub-optimal solution, and calculate the probability at the current temperature of this scenario to accept the sub-optimal solution, and the probability at the current temperature is as shown in the following formula:

[0144]

[0145] wherein, is the probability of the current temperature.

[0146] Set a random number , if , then accept the sub-optimal solution, update the current combination with the neighborhood combination in this scenario, and use the optimized value of the neighborhood combination as the optimized value of the current combination, that is, assign the current combination , ; if , then keep the current combination unchanged, that is, do not update the current combination. And accepting the sub-optimal solution is to explore more solution spaces in the initial stage, avoiding getting stuck in a local optimal solution and unable to get out.

[0147] Compare the updated current combination with the optimal combination. When the optimized value of the updated current combination is less than the optimized value of the optimal combination, replace the optimal combination with the updated current combination as the updated optimal combination, that is, when , update , . And according to the cooling rate α, reduce the temperature , iteratively execute the neighborhood construction, current combination update, and optimal combination update steps until the preset annealing condition is reached, that is, the temperature , return the refined and optimized optimal combination as the best site combination in this scenario; according to the above method, calculate the best site combinations in all scenarios within the area to be planned respectively for combination, and then 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 best site combination can be optimized not only by the above scheme, but also by machine learning algorithms or other optimization algorithms, such as greedy algorithms and local search, heuristic algorithms and meta-heuristic algorithms, etc.

[0148] Through the selection, crossover, and mutation operations of the genetic algorithm, the present invention can effectively perform global search in the solution space and avoid falling into local optimal solutions; 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, further improving the quality of the solution; this scheme combines the advantages of the genetic algorithm and the simulated annealing algorithm, has certain robustness to parameters such as the selection of the initial population, crossover and mutation operations, and annealing temperature setting, can maintain good performance in different scenarios, and then make adaptive adjustments according to different site combination optimization models, and is applicable to various complex site planning problems; through the global search of the genetic algorithm and the local optimization of the simulated annealing algorithm, this scheme can find high-quality site combination solutions to meet actual needs, and while ensuring the quality of the solution, maintain high computational efficiency.

[0149] In the embodiments of the present application, in view of the problem of how to plan fast charging stations in multiple scenarios, a fast charging station planning method applicable to multiple scenarios is designed. It realizes scene analysis of the area to be planned, obtains multiple scenarios to be planned, and quantifies the charging demand data of various types of electric vehicles in each of the scenarios to be planned; according to each of the charging demand data, it quantifies the number of fast charging piles in each of the scenarios to be planned, and combines 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 each of the fast charging station demand data, it takes the points with the conditions for building a charging station in each of the scenarios to be planned as candidate sites to construct a candidate site set in each of the scenarios to be planned; selects several candidate sites from each of the candidate site sets to form multiple site combinations, and constructs a site combination optimization model in each of the scenarios to be planned based on each of the site combinations; solves each of the site combination optimization models through an optimization algorithm to obtain the best site combination in each of the scenarios to be planned for combination, generates a fast charging station planning scheme for the area to be planned and executes the technical solution; through the analysis of different scenarios, calculates the number of fast charging stations in different scenarios, ensures the reasonable number of fast charging stations in different scenarios, through setting a candidate site set with the conditions for building a charging station, analyzes 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, realizes the comprehensive consideration of the candidate site, through establishing a site combination optimization model, using genetic algorithm and simulated annealing algorithm, obtains the best site combination in different scenarios from the candidate site set, improves the rationality and scientificity of the fast charging station layout, and through distinguishing the charging demand in different scenarios and the points with the conditions for building a fast charging station, can reasonably and scientifically realize the fast charging station layout.

[0150] It should be noted that although the steps in the above flowchart are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders.

[0151] In another embodiment, as Figure 2 shown, the second aspect of the present invention provides a fast charging station planning system applicable to multiple scenarios, including:

[0152] The first demand quantification module 10 is used to perform scene analysis on the area to be planned, obtain multiple scenarios to be planned, and quantify the charging demand data of various types of electric vehicles in each of the scenarios to be planned;

[0153] 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;

[0154] The site set construction module 30 is configured to use the fast charging station demand data of each site to use the points where the charging station construction conditions are met in each of the to-be-planned scenarios as candidate sites, so as to construct a candidate site set for each of the to-be-planned scenarios;

[0155] The optimization model construction module 40 is configured to select several candidate sites from each of the candidate site sets to form multiple site combinations, and construct a site combination optimization model for each of the to-be-planned scenarios based on each of the site combinations;

[0156] The optimization model solving module 50 is configured to solve each of the site combination optimization models through an optimization algorithm, obtain the best site combination for each of the to-be-planned scenarios for combination, generate a fast charging station planning scheme for the to-be-planned area, and execute it.

[0157] It should be noted that each module in the above fast charging station planning system applicable to multiple scenarios can be implemented in whole or in part by software, hardware, and their combinations. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules. For the specific limitations of a fast charging station planning system applicable to multiple scenarios, refer to the limitations of a fast charging station planning method applicable to multiple scenarios in the above text. The two have the same functions and effects, and will not be elaborated here.

[0158] The third aspect of the present invention provides an electronic device, which includes:

[0159] A processor, a memory, and a bus;

[0160] The bus is used to connect the processor and the memory;

[0161] The memory is used to store operation instructions;

[0162] The processor is configured to execute an operation corresponding to a fast charging station planning method applicable to multiple scenarios as shown in the first aspect of the present application by calling the operation instructions.

[0163] In an alternative embodiment, an electronic device is provided, as Figure 3 shown Figure 3The electronic device 5000 shown includes: a processor 5001 and a memory 5003. Among them, the processor 5001 and the memory 5003 are connected, such as connected through a bus 5002. Optionally, the electronic device 5000 may further 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 to the embodiments of the present application.

[0164] The processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the disclosure of the present application. The processor 5001 may also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0165] The bus 5002 may include a path for transmitting 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 a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0166] The memory 5003 may be a ROM or other types of static storage devices that can store static information and instructions, a RAM, or other types of dynamic storage devices that can store information and instructions. It may also be an EEPROM, a CD-ROM, or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, 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 thereto.

[0167] The memory 5003 is used to store the application program code for executing the solution of the present application and is controlled by the processor 5001 to execute. The processor 5001 is used to execute the application program code stored in the memory 5003 to implement the content shown in any of the foregoing method embodiments.

[0168] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0169] In a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which when executed by a processor implements a fast charging station planning method applicable to multiple scenarios as shown in the first aspect of the present application.

[0170] Another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which when run on a computer enables the computer to execute the corresponding content in the foregoing method embodiments.

[0171] In addition, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor implements the steps of the above method.

[0172] In summary, the present invention relates to the technical field of fast charging station layout, and discloses a fast charging station planning method, system, device and medium applicable to multiple scenarios. The area to be planned is divided into multiple to-be-planned scenarios, and the number of fast charging piles in each scenario is quantified according to the charging demand data of various types of electric vehicles in each to-be-planned scenario, and the fast charging station demand data in each scenario is quantified in combination with the capacity of the fast charging piles in various scenarios; the points that meet the conditions for building a charging station in various scenarios are used as candidate sites to construct a set of candidate sites in various scenarios, and several candidate sites are selected from them to form multiple site combinations, so as to construct an optimization model of the site combination in various scenarios based on these site combinations; the optimization algorithm is used to solve these site combination optimization models to obtain the best site combination and combine them to generate a fast charging station planning scheme for the area to be planned for execution. By distinguishing the charging demands in different scenarios and the points that meet the conditions for building a fast charging station, the layout of the fast charging station can be realized reasonably and scientifically.

[0173] Each embodiment in this specification is described in a progressive manner. For the parts that are the same or similar in each embodiment, they can be referred to each other. 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 embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0174] The above-described embodiments merely represent several preferred embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the technical principles of the present invention, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the protection scope of the claims described above.

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; Solving the site combination optimization model by an optimization algorithm to obtain the best site combination in each of the scenarios to be planned for combination, generating and executing a fast charging station planning scheme for the area to be planned; 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; Using each of the cluster candidate sites as an input of the particle swarm optimization algorithm for iterative search, and in each iteration, evaluating the pros and cons of each of the cluster candidate sites according to the objective function and the constraint conditions, so as to select the optimal cluster candidate site combination under each of the scenarios to be planned as the final candidate site set; 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; Quantify 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, and weight them 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 in each scenario to be planned; 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.

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: 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.

5. 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; An optimization model solving module, 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 scheme for the area to be planned, and execute it; 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; Using each of the cluster candidate sites as an input of the particle swarm optimization algorithm for iterative search, and in each iteration, evaluating the pros and cons of each of the cluster candidate sites according to the objective function and the constraint conditions, so as to select the optimal cluster candidate site combination under each of the scenarios to be planned as the final candidate site set; 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; Quantify 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, and weight them 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 in each scenario to be planned; 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.

6. 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 4.

7. 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 4 is implemented.

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