Public charging station planning method and device

Abstract

The present disclosure provides a public charging station planning method and device, the method comprising: estimating daily charging demand of each type of vehicle according to driving electricity consumption of each type of vehicle in a planning area obtained by querying a database; determining daily charging capacity of charging piles of different powers according to resource input of charging piles of different powers in a preset period, wherein the public charging station comprises a plurality of charging piles; determining the number of charging piles of different powers according to the daily charging demand and the daily charging capacity; determining a plurality of target site locations of the public charging station according to traffic density information in the planning area, charging station site selection constraint conditions and a road network map, wherein the charging station site selection constraint conditions at least include power grid capacity safety conditions; obtaining a plurality of initial site selection and capacity determination schemes according to the number of charging piles of different powers and the plurality of target site locations; and outputting a target site selection and capacity determination scheme for recommendation from the plurality of initial site selection and capacity determination schemes.

Description

Technical Field

[0001] This disclosure relates to the field of electric vehicle charging station planning technology, and in particular to a method and apparatus for planning public charging stations. Background Technology

[0002] The construction of charging infrastructure is an essential requirement to ensure the high-quality development of new energy vehicles. The number of charging piles and the location of charging stations are the core of charging infrastructure construction and necessary measures to improve the user charging experience and maintain the normalized development of the charging market.

[0003] With the increase in the number of new energy vehicles, the number and scale of charging piles and charging stations are increasing rapidly. How to rationally select the site and determine the capacity of charging stations has become one of the important issues in the development of new energy vehicles.

[0004] In the process of implementing this disclosure, it was found that the traditional "vehicle-to-charging-pile ratio" method for planning the number of charging piles did not fully consider the charging power requirements of vehicles in different fields, making it difficult for the construction of charging piles to be highly compatible with the development of new energy vehicles; and the unreasonable layout of charging stations has led to a common problem in the development of the charging industry: vehicles without charging piles, charging piles without vehicles, and difficulty in charging. Summary of the Invention

[0005] In view of the above problems, this disclosure provides a method, apparatus, equipment, medium and program product for planning public charging stations.

[0006] According to a first aspect of this disclosure, a method for planning public charging stations is provided, comprising:

[0007] Based on the electricity consumption of various types of vehicles in the planning area obtained from the database, the average daily charging demand of each type of vehicle is estimated.

[0008] Based on the resource investment of charging piles with different power levels within a preset period, the average daily charging volume of charging piles with different power levels is determined. Among them, public charging stations include multiple charging piles.

[0009] The number of charging piles with different power ratings is determined based on the average daily charging demand and the average daily charging volume.

[0010] Based on the traffic flow density information, charging station site selection constraints, and road network map within the planning area, multiple target site locations for public charging stations are determined. Among these, the charging station site selection constraints include at least the grid capacity safety conditions.

[0011] Based on the number of charging piles with different power ratings and the locations of multiple target sites, several initial site selection and capacity determination schemes were obtained; and

[0012] Output the target location and capacity scheme from multiple initial location and capacity schemes for recommendation.

[0013] According to embodiments of this disclosure, the preset period includes a construction period and an operation period;

[0014] Based on the resource investment of charging piles with different power ratings within a preset period, the average daily charging volume of charging piles with different power ratings is determined, including:

[0015] The expenditure value is determined based on the construction resource input during the construction period and the operation resource input during the operation period of charging piles with different power levels;

[0016] Based on the expenditure value and the preset electricity consumption assessment value, determine the charging amount for different power levels; and

[0017] The charging volume is used as the average daily charging volume of charging piles with different power levels.

[0018] According to embodiments of this disclosure, multiple target site locations for public charging stations are determined based on traffic flow density information within the planning area, charging station site selection constraints, and road network maps, including:

[0019] Based on traffic flow density information, charging station site selection constraints, and road network map, the site location of each public charging station is configured to obtain the initial site location;

[0020] A weighted Thiessen polygon map is generated based on the preset weighting coefficients for each public charging station and the location of each initial station site. The preset weighting coefficients are determined according to the level and service area of ​​each public charging station.

[0021] Given that the weighted Thiessen polygon map meets the preset charging station planning constraints, multiple target station locations are output.

[0022] According to embodiments of this disclosure, a target location and capacity scheme for recommendation is output from a plurality of initial location and capacity schemes, including:

[0023] For each initial site selection and capacity determination scheme:

[0024] The resource input value is determined based on the average annual resource input of the initial site selection and capacity determination plan;

[0025] Based on the distance between each type of vehicle and the public charging station, and the number of each type of vehicle, determine the driving input value for each type of vehicle to reach the public charging station; and

[0026] Based on the resource input value and the driving input value, the total input value is determined so that the initial site selection and sizing scheme with the lowest total input value can be used as the target site selection and sizing scheme for recommendation.

[0027] According to embodiments of this disclosure, the average annual resource input includes: average annual construction resource input and average annual operation resource input;

[0028] Based on the average annual resource input of the initial site selection and capacity determination plan, the resource input value is determined, including:

[0029] Based on the average annual construction resource input of the initial site selection and capacity determination plan, construct an average annual construction resource function;

[0030] Based on the average annual operating resource input of the initial site selection and capacity determination plan, construct an average annual operating resource function;

[0031] The total resource function is determined based on the average annual construction resource function and the average annual operation resource function; and

[0032] The total resource function outputs the resource input value.

[0033] According to embodiments of this disclosure, the average annual investment in construction resources includes: the average annual purchase and installation of charging piles and supporting equipment in public charging stations, and the average annual land use of public charging stations.

[0034] The annual average investment in operating resources includes at least one of the following: the annual average management of public charging stations, the annual average maintenance of charging piles and supporting equipment, the annual average electricity consumption of public charging stations, and the annual average tax payment of public charging stations.

[0035] According to embodiments of this disclosure, the driving input value for each type of vehicle to reach the public charging station is determined based on the distance between each type of vehicle and the public charging station and the number of each type of vehicle, including:

[0036] Based on the particle swarm optimization algorithm, the target public charging stations for each type of vehicle are determined.

[0037] Based on the non-linear distance between each type of vehicle and the target public charging station, and the total number of each type of vehicle selecting the same target public charging station, a target travel function for each type of vehicle to reach the public charging station is constructed; and

[0038] The target driving function outputs the driving input value.

[0039] According to embodiments of this disclosure, determining the number of charging piles with different power ratings based on average daily charging demand and average daily charging volume includes:

[0040] Based on the average daily charging demand of each type of vehicle, determine the average daily charging demand of each type of vehicle at charging piles with different power ratings.

[0041] Based on the average daily charging demand, determine the total average daily charging volume required for charging piles of various power ratings; and

[0042] Based on the total daily average charging volume required for each charging pile with different power ratings and the corresponding daily average charging volume for each charging pile with different power ratings, determine the number of charging piles corresponding to each power rating.

[0043] According to embodiments of this disclosure, the power consumption during driving includes: vehicle mileage, vehicle power consumption per 100 kilometers, and vehicle charging using a charging station.

[0044] A second aspect of this disclosure provides a public charging station planning device, comprising:

[0045] The daily average charging demand estimation module is used to estimate the daily average charging demand of each type of vehicle based on the driving electricity consumption of each type of vehicle in the planning area obtained from the database.

[0046] The daily average charging volume determination module is used to determine the daily average charging volume of charging piles with different power based on the resource investment of charging piles with different power within a preset period. The public charging station includes multiple charging piles.

[0047] The charging pile quantity determination module is used to determine the number of charging piles with different power based on the average daily charging demand and the average daily charging volume.

[0048] The target site location determination module is used to determine multiple target site locations for public charging stations based on traffic flow density information, charging station site selection constraints, and road network map within the planning area. The charging station site selection constraints include at least the power grid capacity safety condition.

[0049] The initial site selection and capacity determination scheme acquisition module is used to obtain multiple initial site selection and capacity determination schemes based on the number of charging piles with different power ratings and multiple target site locations; and

[0050] The target location and capacity scheme output module is used to output a recommended target location and capacity scheme from multiple initial location and capacity schemes.

[0051] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the above-described public charging station planning method.

[0052] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the above-described public charging station planning method.

[0053] The fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described public charging station planning method.

[0054] According to embodiments of this disclosure, the number of charging piles with different power ratings is obtained by estimating the average daily charging demand of various types of vehicles and combining the average daily charging volume of charging piles with different power ratings. Based on traffic flow density information within the planning area, charging station site selection constraints, and road network maps, multiple target site locations for public charging stations are determined. Based on the number of charging piles with different power ratings and the multiple target site locations, multiple initial site selection and capacity allocation schemes are obtained, and finally, a recommended target site selection and capacity allocation scheme is output. Starting from the average daily charging demand of various types of vehicles and the resource investment of charging piles with different power ratings within a preset period, the number of charging piles with different power ratings is determined, breaking away from the traditional "vehicle-to-pile ratio" method for planning the number of charging piles, and overcoming the difficulty of charging station capacity allocation to a certain extent. Site selection based on traffic flow density information within the planning area, charging station site selection constraints, and road network maps, and finally outputting a recommended target site selection and capacity allocation scheme, improves the reliability of site selection and capacity allocation, and at least partially solves the problems of vehicles without charging piles, charging piles without vehicles, and charging difficulties caused by unreasonable charging station layouts. Attached Figure Description

[0055] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0056] Figure 1 The illustration schematically depicts an application scenario of a public charging station planning method, apparatus, equipment, medium, and program product according to embodiments of the present disclosure.

[0057] Figure 2 A flowchart illustrating a public charging station planning method according to an embodiment of the present disclosure is shown schematically.

[0058] Figure 3 This diagram illustrates the operation of a 60kW charging pile according to an embodiment of the present invention.

[0059] Figure 4 The diagram illustrates the average daily charging volume statistics of charging piles with various power ratings according to embodiments of the present invention.

[0060] Figure 5 The illustration shows a schematic diagram of the layout of public charging stations on a road network map of a planning area according to an embodiment of the present invention;

[0061] Figure 6(a) schematically illustrates a comparison of the optimal fitness curves of two algorithms according to embodiments of the present disclosure;

[0062] Figure 6(b) schematically illustrates the trend of public charging station driving input variation under different quantity schemes according to embodiments of the present disclosure;

[0063] Figure 6(c) schematically illustrates the trend of total investment under different quantity schemes of public charging station layout according to embodiments of the present disclosure;

[0064] Figure 6(d) schematically illustrates a public charging station planning result according to an embodiment of the present disclosure;

[0065] Figure 7 A flowchart illustrating a public charging station planning method according to another embodiment of this disclosure is shown schematically.

[0066] Figure 8 A schematic diagram illustrating a public charging station planning device according to an embodiment of the present disclosure is shown; and

[0067] Figure 9 A block diagram of an electronic device suitable for implementing a public charging station planning method according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0068] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0069] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0070] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0071] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0072] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision, disclosure, and application of data (including but not limited to user personal information) comply with the provisions of relevant laws and regulations, necessary confidentiality measures have been taken, and they do not violate public order and good morals.

[0073] In the technical solutions of this disclosure, user authorization or consent is obtained before acquiring or collecting user personal information.

[0074] In the process of implementing this disclosure, it was found that the traditional "vehicle-to-charging-pile ratio" method for planning the number of charging piles did not fully consider the charging power requirements of vehicles in different fields, making it difficult for the construction of charging piles to be highly compatible with the development of new energy vehicles; and the unreasonable layout of charging stations has led to a common problem in the development of the charging industry: vehicles without charging piles, charging piles without vehicles, and difficulty in charging.

[0075] This disclosure provides a method for planning public charging stations, comprising: estimating the average daily charging demand of each type of vehicle based on the electricity consumption of various types of vehicles within a planning area obtained from a database; determining the average daily charging volume of charging piles with different power ratings based on the resource investment of charging piles with different power ratings within a preset period, wherein the public charging station includes multiple charging piles; determining the number of charging piles with different power ratings based on the average daily charging demand and the average daily charging volume; determining multiple target site locations for the public charging station based on traffic flow density information, charging station site selection constraints, and a road network map within the planning area, wherein the charging station site selection constraints include at least grid capacity safety conditions; obtaining multiple initial site selection and capacity determination schemes based on the number of charging piles with different power ratings and the multiple target site locations; and outputting a target site selection and capacity determination scheme for recommendation from the multiple initial site selection and capacity determination schemes.

[0076] Figure 1 The illustration schematically depicts an application scenario of a public charging station planning method, apparatus, device, medium, and program product according to embodiments of the present disclosure.

[0077] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0078] Users can interact with server 105 via network 104 using at least one of the first terminal device 101, second terminal device 102, and third terminal device 103 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, second terminal device 102, and third terminal device 103, such as applications with location functions, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0079] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0080] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0081] It should be noted that the public charging station planning method provided in this embodiment can generally be executed by server 105. Correspondingly, the public charging station planning device provided in this embodiment can generally be located in server 105. The public charging station planning method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the public charging station planning device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0082] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0083] The following will be based on Figure 1 The described scene, through Figures 2-7 The public charging station planning method of the disclosed embodiments is described in detail.

[0084] Figure 2 A flowchart illustrating a public charging station planning method according to an embodiment of the present disclosure is shown schematically.

[0085] like Figure 2 As shown, the public charging station planning method 200 of this embodiment includes operations S201 to S206.

[0086] In operation S201, based on the electricity consumption of various types of vehicles in the planning area obtained from the database, the average daily charging demand of each type of vehicle is estimated.

[0087] According to embodiments of this disclosure, the database can store multiple data tables, with each type of vehicle corresponding to one or more data tables. The electricity consumption of each type of vehicle can be pre-recorded in these data tables. Before estimating the average daily charging demand of each type of vehicle based on its electricity consumption, the user can input planning area text information via an electronic device. The electronic device then queries the data tables in the database to retrieve the electricity consumption information of each type of vehicle within that planning area.

[0088] According to embodiments of this disclosure, the vehicle's electricity usage is queried from a database using an electronic device, and the vehicle's average daily charging demand is estimated, thereby improving data processing efficiency and achieving automated estimation.

[0089] According to embodiments of this disclosure, vehicles may include new energy vehicles. Various types of vehicles may include: private vehicles, taxis, ride-hailing taxis, sanitation vehicles, buses, intercity and tourist buses, and various types of freight trucks, etc.

[0090] According to embodiments of this disclosure, the power consumption during driving may include: vehicle mileage, vehicle power consumption per 100 kilometers, and vehicle charging at a charging station.

[0091] According to embodiments of this disclosure, a daily average charging demand model for each type of vehicle can be constructed based on the electricity consumption of each type of vehicle during operation. By inputting data obtained from a database, such as vehicle mileage, electricity consumption per 100 kilometers, and charging usage at charging stations within the planning area, into the daily average charging demand model for each type of vehicle, the daily average charging demand for each type of vehicle is output.

[0092] For example, the daily charging demand model for various types of vehicles can be implemented using an algorithm model, which is shown in equation (1) below:

[0093]

[0094] Where Q(t) represents the average daily charging demand of type t new energy vehicles; D(t) represents the average annual mileage of type t new energy vehicles; P(t) represents the energy consumption per 100 kilometers of type t new energy vehicles; R(t) represents the average percentage of type t new energy vehicles that use charging piles for charging; and η is the charging efficiency.

[0095] In operation S202, the average daily charging volume of charging piles with different power is determined based on the resource investment of charging piles with different power within a preset period. Among them, the public charging station includes multiple charging piles.

[0096] According to embodiments of this disclosure, charging piles may include charging piles with different power ratings. A resource investment algorithm model can be constructed based on the resource investment information of charging piles with different power ratings within a preset period, obtained from a database, to estimate the resource investment value within the preset period. The average daily charging volume of charging piles with different power ratings can be obtained based on the resource investment value within the preset period and a preset electricity consumption assessment value. The preset electricity consumption assessment value can be determined based on the electricity consumption standards within the planning area. The preset period may include a construction period and an operation period.

[0097] For example, the resource input algorithm model within a preset period can be shown in equations (2) to (3) below:

[0098]

[0099] Among them, C f This indicates the resource input within a preset period; j represents the power type of the charging pile; n represents the year (n = 1, 2, 3, ..., N); C op (n,j) represents the operating resource investment of charging piles with power of type j in year n; r represents the discount rate, which can be 8%; C ep (j) represents the resource investment in the configuration of charging piles and supporting equipment for power class j; C in (j) represents the resource investment required for the installation of charging piles and supporting equipment of power class j; C ot (j) represents other resource inputs for charging piles with power type j. It should be noted that the power type of charging piles can be classified according to different power levels.

[0100] C op (n,j) can be represented by the following equation (3):

[0101] C op (n,j)=C so (n,j)+C ma (n,j)+C eq (n,j)+C lo (n,j)+C tax (n,j)+C el(n,j) (3)

[0102] Among them, C so (n,j) represents the land resources invested in charging piles; C ma (n,j) represents the management resource investment for charging piles of power type j in year n; C eq (n,j) represents the equipment maintenance resource investment for charging piles of power type j in year n; C lo (n,j) represents the loss of electrical resources invested by a charging pile of power type j in year n; C tax (n,j) represents the taxable resource investment of charging piles of power type j in year n; C el (n,j) represents the other daily operating resource input for charging piles of power type j in year n.

[0103] In operation S203, the number of charging piles with different power is determined based on the average daily charging demand and the average daily charging volume.

[0104] According to embodiments of this disclosure, an algorithm model can be constructed based on the average daily charging demand and average daily charging volume to predict the number of charging piles with different power ratings, and used to output the number of charging piles with different power ratings.

[0105] For example, the algorithm model for predicting the number of charging piles with different power ratings can be shown in equation (4) below:

[0106]

[0107] Where A(i) represents the number of charging piles with power of i; Q m (i) represents the total average daily charging volume of a charging pile with power i; Q a (i) represents the average daily charging amount of a charging pile with power of i.

[0108] Q m (i) can be represented by the following equation (5):

[0109]

[0110] Q m (t,i) represents the average daily charging demand required for a type t new energy vehicle using a charging pile with power i; Q m (t,i) can be represented by the following equation (6):

[0111] Q m (t,i)=Q(t)·B(t)·G(t,i) (6)

[0112] Where Q(t) represents the average daily charging demand of type t new energy vehicles; B(t) represents the number of type t new energy vehicles in the planning area; and G(t,i) represents the proportion of type t new energy vehicles using charging piles with power of i.

[0113] In operating S204, based on the traffic flow density information in the planning area, the site selection constraints of charging stations, and the road network map, multiple target site locations for public charging stations are determined. Among them, the site selection constraints of charging stations include at least the grid capacity safety conditions.

[0114] According to embodiments of this disclosure, vehicle flow density information within the planning area can be obtained by accessing a heat map of pedestrian flow within the planning area stored in a database. The road network map can be retrieved from a database storing road network maps.

[0115] According to embodiments of this disclosure, the site selection constraints for charging stations may further include: geographical location constraints. These constraints are used to constrain traffic flow and population flow. Power grid capacity safety constraints are used to constrain the power grid redundancy within the planning area.

[0116] It should be noted that charging stations should be located in areas with sufficient existing power grid capacity, or in areas close to distribution centers where it is easy to expand the charging grid capacity. This reduces the resource investment required for charging station construction and facilitates grid connection. Charging stations should also be located in peripheral areas with high traffic and population density to attract vehicles for charging, while avoiding the problems of traffic congestion and safety hazards caused by building charging stations in densely populated areas.

[0117] According to embodiments of this disclosure, the initial site locations of each public charging station can be configured through a configuration center based on traffic flow density information, charging station site selection constraints, and a road network map. A weighting coefficient for each public charging station is determined according to its level and service range. A weighted Thiessen polygon map is generated based on the preset weighting coefficients of each public charging station and each initial site location. If the weighted Thiessen polygon map satisfies preset charging station planning constraints, multiple target site locations are output. These preset charging station planning constraints are determined based on factors such as the service capacity and maximum service radius of charging stations within the planning area.

[0118] In operation S205, multiple initial site selection and capacity determination schemes are obtained based on the number of charging piles with different power and the locations of multiple target sites.

[0119] According to embodiments of this disclosure, multiple initial site selection and capacity determination schemes can be generated based on the number of charging piles with different power ratings and multiple target site locations.

[0120] In operation S206, a target location and capacity scheme is output from multiple initial location and capacity schemes for recommendation.

[0121] According to embodiments of this disclosure, a prediction function model can be constructed to obtain predictions of resource input for the initial site selection and capacity allocation scheme, as well as the estimated travel time of various types of vehicles arriving at public charging stations. From these predictions, target site selection and capacity allocation schemes that meet preset conditions are selected for recommendation. The prediction function model can be determined based on data retrieved from a database regarding the resource input required to plan public charging stations within a preset period in the planning area, and the travel time of various types of vehicles arriving at public charging stations.

[0122] According to embodiments of this disclosure, the number of charging piles with different power ratings is obtained by estimating the average daily charging demand of various types of vehicles and combining the average daily charging volume of charging piles with different power ratings. Based on traffic flow density information within the planning area, charging station site selection constraints, and road network maps, multiple target site locations for public charging stations are determined. Based on the number of charging piles with different power ratings and the multiple target site locations, multiple initial site selection and capacity allocation schemes are obtained, and finally, a recommended target site selection and capacity allocation scheme is output. Starting from the average daily charging demand of various types of vehicles and the resource investment of charging piles with different power ratings within a preset period, the number of charging piles with different power ratings is determined, breaking away from the traditional "vehicle-to-pile ratio" method for planning the number of charging piles, and overcoming the difficulty of charging station capacity allocation to a certain extent. Site selection based on traffic flow density information within the planning area, charging station site selection constraints, and road network maps, and finally outputting a recommended target site selection and capacity allocation scheme, improves the reliability of site selection and capacity allocation, and at least partially solves the problems of vehicles without charging piles, charging piles without vehicles, and charging difficulties caused by unreasonable charging station layouts.

[0123] Figure 3 This diagram illustrates the operation of a 60kW charging pile according to an embodiment of the present invention. Figure 4 The diagram illustrates the average daily charging volume statistics of charging piles with various power ratings according to an embodiment of the present invention.

[0124] According to embodiments of this disclosure, the preset period may include a construction period and an operation period.

[0125] The method of determining the average daily charging volume of charging piles with different power levels based on the resource investment of charging piles with different power levels within a preset period may include: determining the expenditure value based on the construction resource investment of charging piles with different power levels during the construction period and the operation resource investment during the operation period; determining the charging volume of charging piles with different power levels based on the expenditure value and the preset electricity consumption assessment value; and using the charging volume as the average daily charging volume of charging piles with different power levels.

[0126] According to embodiments of this disclosure, the investment in construction resources and the investment in operational resources can be summarized to obtain the total resource investment. Based on the total resource investment, a total resource investment algorithm model is constructed to estimate expenditures. Based on the estimated expenditures and preset electricity consumption assessment values, the charging volume for different power levels can be obtained as the average daily charging volume for charging piles of different power levels. The preset electricity consumption assessment values ​​can be determined based on the electricity consumption standards within the planning area.

[0127] For example, taking a 60kW charging pile as an example, a database query can be used to find the number of 60kW charging piles within the planning area, the resource investment in construction and operation of these 60kW charging piles, the average daily charging volume per charging pile (i.e., a single pile), the preset electricity consumption assessment value, and the operating resource revenue over the next 6 years. Based on the resource investment in construction and operation of the 60kW charging piles, the total resource investment can be obtained, and the estimated expenditure value can be calculated. Based on the estimated expenditure value, the operating resource revenue over the next 6 years, and the preset electricity consumption assessment value, the following can be obtained: Figure 3 The diagram shows the operational status of a 60kW charging pile. Similarly, diagrams illustrating the operational status of charging piles with different power ratings (30kW, 120kW, 180kW, 240kW, etc.) can be obtained. Based on the minimum average daily charging volume per pile when the revenue is 0, Fourier fitting yields the following results: Figure 4 The average daily charging volume of charging piles with different power ratings is shown.

[0128] The operating resource revenue over 6 years can be obtained through the operating resource revenue function over n years as shown in equation (7).

[0129]

[0130] Among them, P f Indicates revenue from operating resources; P se,n This represents the pre-set electricity consumption assessment resource revenue situation in year n; P su,n Indicates details of various subsidies; P re,n This indicates the scrapping and recycling of charging piles and supporting equipment after n years of use, which can be calculated as 5% of the resource investment in charging piles and supporting equipment.

[0131] According to the embodiments of this disclosure, the average daily charging volume of charging piles with different power is determined based on the resource investment of charging piles with different power within a preset period. This is beneficial for determining the number of charging piles with different power, thereby breaking the traditional "vehicle-to-pile ratio" method for planning the number of charging piles and overcoming the problem of charging station capacity to a certain extent.

[0132] According to embodiments of this disclosure, determining the number of charging piles with different power ratings based on the average daily charging demand and the average daily charging amount may include: determining the average daily charging demand of each type of vehicle at charging piles with different power ratings based on the average daily charging demand of each type of vehicle; determining the total average daily charging amount required for each power charging pile based on the average daily charging demand; and determining the number of charging piles corresponding to each power rating based on the total average daily charging amount required for each power charging pile and the average daily charging amount corresponding to each power charging pile.

[0133] For example, taking a planned area in the core urban area of ​​a city with an east-west span of 12km and a north-south span of 9km as an example, by analyzing the city's car ownership, the development trend of new energy vehicles, and the current vehicle activity in the planned area, the average daily activity of vehicles in various sectors within the planned area in 2025 can be predicted. For instance, it could be approximately 10,500 new energy vehicles in the private sector, approximately 1,050 in the taxi and ride-hailing sector, approximately 100 in the sanitation vehicle sector, approximately 240 in the public transport and intercity bus sector, and approximately 100 in the truck sector. Considering that truck charging is mainly concentrated in logistics dispatch centers outside the main urban area, the demand for truck charging within the planned area is not considered.

[0134] Based on the electricity consumption data of various types of vehicles within a planned area spanning 12km east-west and 9km north-south in the core urban area of ​​a certain city, obtained from a database query, the average daily charging demand of each type of vehicle can be estimated. Based on this average daily charging demand, the average daily charging capacity required for each type of vehicle at different charging pile power levels can be determined. Based on the average daily charging capacity required for each charging pile power level, the total average daily charging capacity required for each charging pile power level can be determined. Finally, based on the total average daily charging capacity required for each charging pile power level and the corresponding average daily charging capacity, the number of charging piles corresponding to each power level can be determined. The number of charging piles at different power levels is shown in Table 1 below.

[0135] Table 1

[0136]

[0137] According to the embodiments of this disclosure, starting from the average daily charging demand of various types of vehicles, the average daily charging demand of charging piles with different power is determined, and then the total average daily charging amount required for each power charging pile is determined. Finally, the number of charging piles corresponding to each power is determined, breaking the traditional "vehicle-to-pile ratio" method for planning the number of charging piles, and overcoming the problem of charging station capacity to a certain extent.

[0138] According to embodiments of this disclosure, determining multiple target site locations for public charging stations based on traffic flow density information within the planning area, charging station site selection constraints, and road network maps may include:

[0139] Based on traffic flow density information, charging station site selection constraints, and road network map, the site location of each public charging station is configured to obtain the initial site location; a weighted Thiessen polygon map is generated based on the preset weight coefficient of each public charging station and each initial site location, wherein the preset weight coefficient is determined according to the level and service range of each public charging station; and multiple target site locations are output when the weighted Thiessen polygon map meets the preset charging station planning constraints.

[0140] According to embodiments of this disclosure, the range of the number of public charging stations [N] can be pre-determined based on the number of charging piles with different power ratings and the maximum service range of public charging stations of different levels, according to preset charging station planning constraints and charging station scale classification standards. s min N s max This information can serve as a candidate database for the number of public charging stations.

[0141] According to embodiments of this disclosure, the initial site location can be obtained by configuring the site location of each public charging station in the public charging station candidate database through a configuration center, based on traffic flow density information, charging station site selection constraints, and road network maps. The level of a public charging station can be classified according to the number of charging piles. The service range can be determined based on the service radius of different levels of public charging stations obtained in advance from the database. It should be noted that the more charging piles and the higher their power, the greater the preset weighting coefficient of the public charging station.

[0142] For example, a public charging station with 10 charging piles can be classified as small; one with 20 charging piles as medium; one with 30 charging piles as medium-large; and one with 50 charging piles as large. The various parameters for different levels of public charging stations, as shown in Table 2 below, can be obtained in advance from the database.

[0143] Table 2

[0144]

[0145] According to embodiments of this disclosure, a Thiessen polygon graph function can be constructed based on the preset weighting coefficients of each public charging station and each initial station location to generate a weighted Thiessen polygon graph.

[0146] For example, the Thiessen polygon graph function can be expressed as shown in equation (8):

[0147]

[0148] Where V(q) i) represents the region bounded by the Voronoi diagram; d(x,q) i ) and d(x,q j () represent x and public charging station q respectively. i and public charging stations q j The Euclidean distance between them; ω i The vertex Thiessen polygon diffusion weight represents the pre-defined weight coefficient; 3≤n≤∞.

[0149] According to embodiments of this disclosure, the preset charging station planning constraints may include constraints on the charging demand of new energy vehicles and constraints on the service radius of new energy vehicles.

[0150] The charging demand constraint for new energy vehicles can be expressed as shown in equation (9):

[0151]

[0152] Where, N k nev Indicates the number of new energy vehicles within the service area of ​​a public charging station; γ max This indicates the simultaneous charging rate of new energy vehicles within the service area of ​​a public charging station; N k m This indicates the number of charging stations and charging piles, ensuring that the number of vehicles charging simultaneously during peak hours within the service area does not exceed the number of charging piles, thereby improving the user charging experience.

[0153] The service radius constraint for new energy vehicles can be expressed as shown in equations (10) to (11):

[0154] λ kj d kj ≤d max (10)

[0155] D min ≤λ kj D kj (11)

[0156] Where, λ kj Indicates the road tortuosity coefficient; d kj d represents the straight-line distance between the vehicle's location point j and the public charging station k; max D represents the maximum distance from vehicle location j to public charging station k; kj D represents the Euclidean distance between the vehicle's location j and the public charging station k; min This represents the minimum distance specified between the vehicle's location point j and the public charging station k.

[0157] If the weighted Thiessen polygon map meets the preset charging station planning constraints, multiple target station locations can be output. Public charging stations can then be laid out on the road network map of the planning area based on these target locations.

[0158] Figure 5 The illustration shows a schematic diagram of the layout of public charging stations on a road network map of a planning area according to an embodiment of the present invention.

[0159] For example, if the weighted Thiessen polygon graph satisfies the preset charging station planning constraints, the output can be as follows: Figure 5 The map shows 21 public charging stations laid out on the road network of the planning area. It should be noted that... Figure 5 The center dot indicates a public charging station.

[0160] According to embodiments of this disclosure, multiple target site locations for public charging stations are determined based on traffic flow density information within the planning area, charging station site selection constraints, and a road network map. Site selection is performed based on traffic flow density information within the planning area, charging station site selection constraints, and a road network map, ultimately outputting a recommended target site selection and capacity allocation scheme. This improves the reliability of site selection and capacity allocation, and at least partially solves the problems of vehicles without charging stations, charging stations without vehicles, and charging difficulties caused by unreasonable charging station layouts.

[0161] According to embodiments of this disclosure, outputting a target location and capacity scheme for recommendation from a plurality of initial location and capacity schemes may include: for each initial location and capacity scheme:

[0162] Based on the average annual resource input of the initial site selection and capacity allocation scheme, the resource input value is determined; based on the distance between each type of vehicle and the public charging station and the number of each type of vehicle, the driving input value for each type of vehicle to reach the public charging station is determined; and based on the resource input value and the driving input value, the total input value is determined so that the initial site selection and capacity allocation scheme with the lowest total input value can be used as the target site selection and capacity allocation scheme for recommendation.

[0163] According to embodiments of this disclosure, a total investment value is determined based on resource investment value and driving investment value, so that the initial site selection and capacity allocation scheme with the lowest total investment value can be used as the target site selection and capacity allocation scheme for recommendation, thereby improving the reliability of site selection and capacity allocation.

[0164] According to embodiments of this disclosure, the average annual resource input may include: average annual construction resource input and average annual operation resource input.

[0165] The process of determining the resource input value based on the average annual resource input of the initial site selection and capacity determination plan may include: constructing an average annual construction resource function based on the average annual construction resource input of the initial site selection and capacity determination plan; constructing an average annual operation resource function based on the average annual operation resource input of the initial site selection and capacity determination plan; determining the total resource function based on the average annual construction resource function and the average annual operation resource function; and outputting the resource input value through the total resource function.

[0166] According to embodiments of this disclosure, the average annual investment in construction resources may include: the average annual purchase and installation of charging piles and supporting equipment in public charging stations, and the average annual land use of public charging stations.

[0167] According to embodiments of this disclosure, the annual average investment in operating resources may include at least one of the following: the annual average management of public charging stations, the annual average maintenance of charging piles and supporting equipment, the annual average power consumption of public charging stations, and the annual average tax payment of public charging stations.

[0168] For example, the annual average construction resource function can be represented by the following equation (12):

[0169]

[0170] Among them, C bu This indicates the average annual resource investment in the construction of public charging stations; r represents the discount rate; C k ep This indicates the average annual purchase and installation of charging piles and supporting equipment within public charging station k; S k and C k L These represent the average annual land area used by public charging stations and the average annual land investment for public charging stations, respectively.

[0171] The annual average operating resource function can be expressed as follows (13):

[0172]

[0173] Among them, C en This indicates the average annual investment in operating resources for public charging stations; C k ma C k eq C k lo C k tax C k el These figures represent the annual average management status of public charging stations, the annual average maintenance status of charging piles and supporting equipment, the annual average electricity consumption of public charging stations, the annual average tax payment status of public charging stations, and other operational statuses.

[0174] According to embodiments of this disclosure, a target travel function for each type of vehicle to reach the public charging station can be constructed based on the distance between each type of vehicle and the public charging station and the number of each type of vehicle. The travel input value for each type of vehicle to reach the public charging station is then determined based on the target travel function. By summing the resource input value and the travel input value, a total input value is obtained, so that the initial location and capacity allocation scheme with the lowest total input value is output as the recommended target location and capacity allocation scheme.

[0175] According to the embodiments of this disclosure, the resource input value is determined based on the average annual construction resource input and the average annual operation resource input, which fully considers the resource input situation and is conducive to accurately obtaining the resource input value. Then, the total input value is determined by combining the driving input value, and the target site selection and capacity determination scheme is output for recommendation, thereby improving the reliability of site selection and capacity determination.

[0176] According to embodiments of this disclosure, determining the driving input value for each type of vehicle to reach the public charging station based on the distance between each type of vehicle and the public charging station and the number of each type of vehicle may include:

[0177] Based on the particle swarm optimization algorithm, the target public charging stations selected by each type of vehicle are determined; based on the non-linear distance between each type of vehicle and the target public charging station and the total number of each type of vehicle selecting the same target public charging station, the target driving function for each type of vehicle to reach the public charging station is constructed; and the driving input value is output through the target driving function.

[0178] According to embodiments of this disclosure, the particle swarm optimization algorithm may include the Catfish Particle Swarm Optimization (CPSO) algorithm. Various parameters of the CPSO algorithm can be initialized, and initial particle positions, i.e., the initial locations of the target public charging stations selected by each type of vehicle, can be generated randomly. The particle positions and velocities are then updated using the CPSO algorithm until the maximum number of iterations is reached, outputting the optimal particle positions and fitness values. Based on the optimal particle positions, the target public charging stations selected by each type of vehicle are obtained.

[0179] It should be noted that charging station site selection is a nonlinear optimization problem. Traditional particle swarm optimization algorithms are prone to getting stuck in local optima. Therefore, this disclosure introduces the catfish particle swarm optimization algorithm (CPSO) to improve the speed and effectiveness of the optimization problem.

[0180] For example, this process can be shown in equation (14):

[0181] v id (t+1)=ω1·v id (t)+c1rand()[c3rand()·p best -x id (t)]

[0182] +c2rand()[c4rand()·g best -x id (t)] (14)

[0183] Where ω1 represents the self-inertia weight; v id (t) represents the velocity of the particle at time t; v id (t+1) represents the particle's update rate at time t+1; x id (t) represents the position of the particle at time t; p best Represents the best historical position of an individual particle; g best c1 and c2 represent the global historical optimal position; c3 and c4 represent the learning factors; c3 and c4 represent the interference intensity of the catfish on the individual optimal extreme value and the overall optimal extreme value; c3rand() and c4rand() represent the catfish operator, and their values ​​are shown in equations (15) and (16) respectively:

[0184]

[0185]

[0186] Among them, e 0p This represents the threshold deviation between the current value and the current individual extreme value; e p This indicates the deviation between the current value and the current individual extreme value; e 0g This represents the threshold value representing the deviation between the current value and the current global extremum; e g This represents the deviation between the current value and the current global optimum. If the deviation of the current value is greater than the deviation threshold, the catfish operator c3rand() takes the value of 1, and the catfish particle swarm algorithm is the standard particle swarm algorithm; otherwise, it is assumed that the particles are converging, so the catfish operator is used to perturb the local optimum and the global optimum, and the particles are encouraged to continue moving by impact, thus tending towards the global optimum.

[0187] According to embodiments of this disclosure, considering the limitations of the road network map, the distance a vehicle travels to the target public charging station is not Euclidean distance, but rather a broken line distance based on the road network map. Therefore, the path for each type of vehicle to reach the public charging station needs to be determined by incorporating a tortuosity coefficient, taking into account the actual road network conditions. A target travel function for each type of vehicle to reach the public charging station can be constructed based on the non-linear distance between each type of vehicle and the target public charging station, and the total number of vehicles of each type choosing the same target public charging station.

[0188] For example, the target driving function can be represented by the following equation (17):

[0189] MinC hu =365σ∑ k ∑ j λ kj dkj q j (k = 1, 2, ..., N) c ;j∈J i (17)

[0190] Among them, C hu This indicates the driving input; σ represents the electric vehicle's single-kilometer driving and time conversion, determined by the vehicle model and user profile; d kj q represents the distance between vehicle location j and public charging station k; j Let be the number of electric vehicles that need to be served daily at vehicle location j. Considering the limitations of the road network map, the relationship between vehicle location j and public charging station k is not a straight line, therefore a tortuosity coefficient λ is introduced. kj Its value ranges from 1 to 1.41, and the specific value is determined by the actual road conditions. The more complex the road network structure, the higher the tortuosity coefficient.

[0191] The driving input value can be output based on the target driving function described above.

[0192] Figure 6(a) schematically illustrates a comparison of the optimal fitness curves of two algorithms according to embodiments of the present disclosure; Figure 6(b) schematically illustrates the trend of public charging station driving input changes under different quantity schemes according to embodiments of the present disclosure.

[0193] Figure 6(a) shows a comparison of the convergence curves of the Catfish Particle Swarm Optimization (CPSO) algorithm and the traditional Particle Swarm Optimization (PSO) algorithm for selecting public charging station locations, for example, by selecting 21 public charging station options. Figure 6(a) demonstrates that the Catfish Particle Swarm Optimization (CPSO) algorithm outperforms the traditional PSO algorithm in both convergence speed and optimization performance, achieving better results.

[0194] Through continuous iterative optimization, the trend of driving input for different numbers of public charging stations can be obtained, as shown in Figure 6(b). Figure 6(b) shows that the more public charging stations there are, the more convenient it is for vehicles to reach them, and the lower the driving input value. However, when the number of public charging stations reaches 20 or more, the rate of decrease in driving input value for vehicles reaching public charging stations drops significantly with the continued increase in the number of public charging stations.

[0195] According to embodiments of this disclosure, starting from the convenience of users driving their vehicles to the target public charging station, the catfish particle swarm optimization algorithm is used for optimization. This avoids the problem of local optima that particle swarm optimization is prone to, enhances the algorithm's global search capability, and determines the target public charging station for each type of vehicle. The non-linear distance between each type of vehicle and the target public charging station is considered, ultimately obtaining the driving input value, which to some extent enhances the rationality of public charging station planning.

[0196] Figure 6(c) schematically illustrates the trend of total investment under different quantity schemes of public charging station layout according to embodiments of the present disclosure; Figure 6(d) schematically illustrates the planning results of public charging stations according to embodiments of the present disclosure.

[0197] According to embodiments of this disclosure, a total investment value is determined based on resource input value and driving input value, so that the initial site selection and capacity allocation scheme with the lowest total investment value is output as the recommended target site selection and capacity allocation scheme. Specifically, an algorithm model for outputting the recommended target site selection and capacity allocation scheme can be constructed based on the average annual construction resource input, average annual operating resource input, and driving input.

[0198] For example, the algorithm model for outputting the recommended target location and sizing scheme can be shown in equation (18):

[0199] MinC=C hu +C bu +C en (18)

[0200] Here, MinC represents the case with the lowest total investment. Based on the algorithm model used to output the recommended target location and capacity scheme, the initial location and capacity scheme corresponding to the case with the lowest total investment can be used as the recommended target location and capacity scheme.

[0201] For example, after multiple iterative calculations, the total investment trend under different public charging station layout schemes with varying numbers of stations can be obtained, as shown in Figure 6(c). Figure 6(c) shows that the more public charging stations there are, the shorter the distance vehicles travel to reach them, resulting in lower investment. However, a higher number of public charging stations also leads to higher investment in construction and operation resources, significantly increasing the overall investment required for both. In summary, a total investment of 21 public charging stations in the planning area represents the lowest possible investment, making it the optimal number of charging stations for construction.

[0202] For example, the recommended target site selection and capacity scheme can be 9 small charging stations, 8 medium charging stations, 2 medium-large charging stations, and 2 medium-large charging stations, and the site coordinates can be shown in Table 3 below.

[0203] Table 3

[0204]

[0205]

[0206] Furthermore, the service area of ​​each charging station can be divided on the road network map using a weighted Voronoi diagram, resulting in the planning outcome of public charging stations, as shown in Figure 6(d).

[0207] It should be noted that the introduction of the tortuosity coefficient ensures that the service boundary of public charging stations is basically aligned with the adjacent road network, making the service area division more consistent with actual vehicle driving conditions. Furthermore, the service area varies considerably between different public charging stations, primarily depending on the service capacity of their respective levels; the higher the charging station level, the larger its service area.

[0208] According to embodiments of this disclosure, by incorporating resource input value and driving input value, the initial site selection and sizing scheme with the lowest total input value is used as the target site selection and sizing scheme for recommendation, thereby improving the reliability of site selection and sizing.

[0209] According to embodiments of this disclosure, the power consumption during driving may include: vehicle mileage, vehicle power consumption per 100 kilometers, and vehicle charging at a charging station.

[0210] Figure 7 A flowchart illustrating a public charging station planning method according to another embodiment of the present disclosure is shown.

[0211] like Figure 7 As shown, the public charging station planning method 700 of this embodiment may include operations S701 to S711.

[0212] In operation S701, determine the number of charging stations with different power ratings.

[0213] According to embodiments of this disclosure, the daily average charging demand model for each type of vehicle can be generated by inputting data obtained from a database, including vehicle mileage, energy consumption per 100 kilometers, and charging usage at charging piles within the planning area. The output model then calculates the daily average charging demand for each type of vehicle. A resource allocation algorithm model can be constructed based on the resource allocation data of charging piles with different power ratings obtained from the database within a preset period. This model is used to estimate the resource allocation value within the preset period. The daily average charging volume of charging piles with different power ratings can be obtained based on the resource allocation value within the preset period and a preset electricity consumption assessment value. Finally, an algorithm model can be constructed based on the daily average charging demand and daily average charging volume to predict the number of charging piles with different power ratings, and this model is used to output the number of charging piles with different power ratings.

[0214] During the operation of S702, the range of the number of public charging stations of different levels in the planning area was initially determined.

[0215] According to embodiments of this disclosure, based on the number of charging piles with different power ratings and the maximum service range of public charging stations of different levels, and according to preset charging station planning constraints and charging station scale classification standards, the range [N] of the number of public charging stations of different levels in the planning area can be initially determined. s min N s max ].

[0216] By operating S703, combining traffic flow density information, charging station site selection constraints, and road network map, the initial locations of public charging stations with different numbers of options are determined to obtain the initial site locations.

[0217] According to embodiments of this disclosure, the initial site location can be obtained by configuring the site location of each public charging station in the public charging station quantity candidate pool through a configuration center based on traffic flow density information, charging station site selection constraints, and road network map.

[0218] In operation S704, a weighted Thiessen polygon map is generated based on the preset weight coefficient of each public charging station and the location of each initial station site.

[0219] According to embodiments of this disclosure, a Thiessen polygon graph function can be constructed based on the preset weighting coefficients of each public charging station and each initial station location to generate a weighted Thiessen polygon graph.

[0220] When operating S705, does the weighted Thiessen polygon graph meet the preset charging station planning constraints?

[0221] According to embodiments of this disclosure, the preset charging station planning constraints may include constraints on the charging demand of new energy vehicles and constraints on the service radius of new energy vehicles. If satisfied, operation S706 is executed. If not satisfied, operation S703 is executed again.

[0222] In operation S706, initialize the catfish particle swarm algorithm parameters and generate initial particle positions on the road network map.

[0223] According to embodiments of this disclosure, various parameters of the catfish particle swarm algorithm can be initialized, and initial particle positions can be generated on the road network map in a random manner, i.e., the initial locations of target public charging stations selected by each type of vehicle.

[0224] When operating S707, based on the structure of the road network map, a tortuosity coefficient is introduced to construct the target driving function.

[0225] According to embodiments of this disclosure, a target driving function for each type of vehicle to reach a public charging station can be constructed based on the non-linear distance between each type of vehicle and the target public charging station, and the total number of each type of vehicle selecting the same target public charging station.

[0226] In operating S708, the catfish operator is introduced to update the particle state.

[0227] When operating S709, has the convergence count been reached?

[0228] According to embodiments of this disclosure, the particle position and velocity are updated using a catfish operator until a preset number of convergences is reached. If the preset number of convergences is reached, operation S710 is executed to output the particle position and fitness value. If the preset number of convergences is not reached, operation S706 is repeated.

[0229] In operation S711, the total input value is determined so that the initial site selection and sizing scheme with the lowest total input value can be output as the target site selection and sizing scheme for recommendation.

[0230] According to embodiments of this disclosure, a total input value is determined based on resource input value and driving input value. The initial site selection and sizing scheme with the lowest total input value is output as the target site selection and sizing scheme for recommendation.

[0231] Based on the above-mentioned public charging station planning method, this disclosure also provides a public charging station planning device. The following will be combined with... Figure 8 The device is described in detail.

[0232] Figure 8 A schematic block diagram of a public charging station planning device according to an embodiment of the present disclosure is shown.

[0233] like Figure 8 As shown, the public charging station planning device 800 of this embodiment includes a daily average charging demand estimation module 810, a daily average charging volume determination module 820, a charging pile quantity determination module 830, a target site location determination module 840, an initial site selection and capacity determination scheme acquisition module 850, and a target site selection and capacity determination scheme output module 860.

[0234] The daily average charging demand estimation module 810 is used to estimate the daily average charging demand of each type of vehicle based on the electricity consumption of each type of vehicle in the planning area obtained from the database.

[0235] The daily average charging volume determination module 820 is used to determine the daily average charging volume of charging piles with different power based on the resource investment of charging piles with different power within a preset period. The public charging station includes multiple charging piles.

[0236] The charging pile quantity determination module 830 is used to determine the number of charging piles with different power based on the average daily charging demand and the average daily charging volume.

[0237] The target site location determination module 840 is used to determine multiple target site locations for public charging stations based on traffic flow density information within the planning area, charging station site selection constraints, and road network map. The charging station site selection constraints include at least the power grid capacity safety conditions.

[0238] The initial site selection and capacity determination scheme acquisition module 850 is used to obtain multiple initial site selection and capacity determination schemes based on the number of charging piles with different power and multiple target site locations.

[0239] The target location and capacity scheme output module 860 is used to output a recommended target location and capacity scheme from multiple initial location and capacity schemes.

[0240] According to embodiments of this disclosure, any and multiple modules among the daily average charging demand estimation module 810, daily average charging volume determination module 820, charging pile quantity determination module 830, target site location determination module 840, initial site selection and capacity allocation scheme acquisition module 850, and target site selection and capacity allocation scheme output module 860 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the daily average charging demand estimation module 810, daily average charging volume determination module 820, charging pile quantity determination module 830, target site location determination module 840, initial site selection and capacity allocation scheme acquisition module 850, and target site selection and capacity allocation scheme output module 860 can be at least partially implemented as hardware circuits, such as field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits (ASICs), or can be implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuit, or implemented in software, hardware, and firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the following modules can be implemented as a computer program module: the daily average charging demand estimation module 810, the daily average charging volume determination module 820, the number of charging piles determination module 830, the target site location determination module 840, the initial site selection and capacity determination scheme acquisition module 850, and the target site selection and capacity determination scheme output module 860. When the computer program module is run, it can perform the corresponding function.

[0241] Figure 9 A block diagram of an electronic device suitable for implementing a public charging station planning method according to an embodiment of the present disclosure is shown schematically.

[0242] like Figure 9As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0243] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0244] According to embodiments of this disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0245] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0246] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0247] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this disclosure.

[0248] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0249] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0250] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0251] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0252] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0253] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0254] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A public charging station planning method, comprising: estimating daily charging demand of each type of vehicle according to driving electricity consumption of the each type of vehicle in a planning area obtained by querying a database; for charging piles of different powers: determining an expenditure value according to construction resource input of the charging pile in a construction period and operation resource input of the charging pile in an operation period; drawing a charging pile operation condition diagram according to the expenditure value, a preset electricity consumption evaluation value, and operation resource income of the charging pile in the operation period; and determining a daily minimum charging amount when a benefit value is zero based on the charging pile operation condition diagram; obtaining daily charging amounts of the charging piles of different powers by Fourier fitting the daily minimum charging amounts of the charging piles of different powers respectively, wherein the preset electricity consumption evaluation value is determined according to electricity consumption standards in the planning area, and the public charging station comprises a plurality of the charging piles; determining quantities of the charging piles of different powers according to the daily charging demand and the daily charging amounts; configuring station site positions of each of the public charging stations according to traffic density information in the planning area, charging station site selection constraint conditions, and a road network diagram to obtain initial station site positions; determining preset weight coefficients of each of the public charging stations according to grades and service ranges of the public charging stations, wherein the grades of the public charging stations are obtained according to charging pile quantities, and the service ranges of the public charging stations are determined according to service radii of public charging stations of different grades obtained in advance from the database; generating a weighted Thiessen polygon diagram according to the preset weight coefficients of each of the public charging stations and each of the initial station site positions; obtaining a plurality of target station site positions of the public charging stations in a case where the weighted Thiessen polygon diagram meets preset charging station planning constraint conditions, wherein the charging station site selection constraint conditions at least comprise a power grid capacity safety condition, and the preset charging station planning constraint conditions are determined according to service capabilities and maximum service radii of the public charging stations in the planning area; obtaining a plurality of initial site selection and capacity determination schemes according to the quantities of the charging piles of different powers and the plurality of target station site positions; outputting a target site selection and capacity determination scheme for recommendation from the plurality of initial site selection and capacity determination schemes.

2. The method of claim 1, wherein, The outputting of the target site selection and capacity determination scheme for recommendation from the plurality of initial site selection and capacity determination schemes comprises: for each of the initial site selection and capacity determination schemes: determining a resource input value according to annual resource input of the initial site selection and capacity determination scheme; determining driving input values of each type of vehicle to the public charging station according to distances of the each type of vehicle to the public charging station and quantities of the each type of vehicle; and determining a total input value according to the resource input value and the driving input values, so as to output the initial site selection and capacity determination scheme with the lowest total input value as the target site selection and capacity determination scheme for recommendation.

3. The method of claim 2, wherein, The annual resource input comprises annual construction resource input and annual operation resource input. The determining of the resource input value according to the annual resource input of the initial site selection and capacity determination scheme comprises: constructing a yearly construction resource function according to the yearly average construction resource input condition of the initial site selection and capacity determination scheme; constructing a yearly operation resource function according to the yearly average operation resource input condition of the initial site selection and capacity determination scheme; determining a total resource function according to the yearly construction resource function and the yearly operation resource function; and outputting the resource input value through the total resource function.

4. The method of claim 3, wherein, The yearly average construction resource input condition includes yearly average purchase condition, yearly average installation condition of charging piles and supporting equipment in the public charging station, and yearly average land use condition of the public charging station. The yearly average operation resource input condition includes at least one of the following: yearly average management condition of the public charging station, yearly average maintenance condition of the charging piles and supporting equipment, yearly average power consumption condition of the public charging station, and yearly average tax payment condition of the public charging station.

5. The method of claim 2, wherein, The method for determining the driving input value of the vehicles of different types to the public charging station according to the distance between the vehicles of different types and the public charging station and the number of the vehicles of different types comprises: determining a target public charging station selected by the vehicles of different types according to a particle swarm algorithm; constructing a target driving function of the vehicles of different types to the public charging station according to the non-straight-line distance between the vehicles of different types and the target public charging station, and the total number of the vehicles of different types selecting the same target public charging station; and outputting the driving input value through the target driving function.

6. The method of claim 1, wherein, The method for determining the number of charging piles of different powers according to the daily average charging demand and the daily average charging amount comprises: determining daily average charging electronic demand of the vehicles of different types at charging piles of different powers according to the daily average charging demand of the vehicles of different types; determining total daily average charging amount required by charging piles of each power according to the daily average charging electronic demand; and determining the number of charging piles corresponding to each power according to the total daily average charging amount required by charging piles of each power and the daily average charging amount corresponding to the charging piles of each power.

7. The method of claim 1, wherein, The driving electricity consumption condition includes vehicle driving mileage condition, vehicle 100-kilometer electricity consumption condition, and vehicle charging condition using the charging piles.

8. A public charging station planning device, comprising: a daily average charging demand estimation module configured to estimate daily average charging demand of vehicles of different types according to driving electricity consumption condition of the vehicles of different types in a planning area obtained by querying a database. The daily average charging volume determination module is used for charging piles of different power levels to: determine the expenditure value based on the construction resource investment during the construction period and the operation resource investment during the operation period; draw a schematic diagram of the charging pile operation based on the expenditure value, the preset electricity consumption assessment value, and the operation resource revenue during the operation period; determine the minimum daily average charging volume when the revenue value is zero based on the schematic diagram of the charging pile operation; and obtain the average daily charging volume of the charging piles of different power levels by Fourier fitting the minimum daily average charging volume of each charging pile. The preset electricity consumption assessment value is determined according to the electricity consumption standard in the planning area, and the public charging station includes multiple charging piles. The charging pile quantity determination module is used to determine the quantity of charging piles with different power based on the average daily charging demand and the average daily charging volume. The target site location determination module is used to configure the site location of each public charging station based on traffic flow density information, charging station site selection constraints, and road network map within the planning area to obtain an initial site location; determine a preset weight coefficient for each public charging station based on its level and service range, wherein the level of the public charging station is determined according to the number of charging piles, and the service range of the public charging station is determined based on the service radius of different levels of public charging stations obtained in advance from the database; generate a weighted Thiessen polygon map based on the preset weight coefficient of each public charging station and each initial site location; and obtain multiple target site locations for the public charging stations if the weighted Thiessen polygon map satisfies the preset charging station planning constraints, wherein the charging station site selection constraints include at least the power grid capacity safety condition, and the preset charging station planning constraints are determined based on the service capacity and maximum service radius of the public charging stations within the planning area. The initial site selection and capacity determination scheme acquisition module is used to obtain multiple initial site selection and capacity determination schemes based on the number of charging piles with different power and the multiple target site locations; The target location and capacity setting scheme output module is used to output a recommended target location and capacity setting scheme from multiple initial location and capacity setting schemes.

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