CN115271550A - Electric vehicle public charging station site selection and volume fixing method, computing equipment and storage medium - Google Patents
Electric vehicle public charging station site selection and volume fixing method, computing equipment and storage medium Download PDFInfo
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Abstract
The invention provides a method for locating and sizing an electric vehicle public charging station, computing equipment and a storage medium, wherein the method comprises the following steps: acquiring population data, private car inventory data and GDP data of an area to be planned; determining private car inventory prediction data in a preset time period according to population data, private car inventory data and GDP data; determining the electric automobile inventory prediction data according to the private automobile inventory prediction data; determining the minimum demand forecasting data of the charging pile according to the reserve forecasting data of the electric vehicle and the preset public pile configuration proportion corresponding to the area to be planned; dividing a region to be planned into a plurality of sub-regions according to a specified distance; and determining the station building and site selecting positions of the charging stations in the area to be planned and the quantity of the charging piles according to the minimum demand prediction data of the charging piles and the preset site selecting and volume planning model corresponding to each sub-area. The invention has the beneficial effects that: charging station position and the charging stake quantity of filling in the charging station that will build in can the reasonable definite city.
Description
Technical Field
The invention relates to the technical field of electric vehicle charging, in particular to a location and volume determining method, computing equipment and a storage medium for an electric vehicle public charging station.
Background
Electric automobile charging station planning management problem becomes the focus and the key problem that industry and academic focus were paid close attention to, and scientific and reasonable's charging station addressing constant volume planning management can guide the construction and the use of charging station effectively, improves the operation efficiency and the quality of service of charging station itself, avoids public infrastructure repeated coverage, can reduce the blindness of construction electric automobile charging facility in-process, increases the stability of electric wire netting.
In recent years, the number of electric vehicles is increasing day by day, some public charging stations are far from meeting the charging requirements of electric vehicle users no matter from the number or from service objects, charging infrastructures in many areas are not coordinated with the development of the electric vehicles, the overall planning layout of the charging facilities is unreasonable, the phenomena of no pile in the presence of vehicles and no pile in the presence of vehicles coexist, and the charging difficulty problem is increasingly prominent in many areas.
Disclosure of Invention
The invention aims to solve the technical problem of how to realize reasonable planning of charging stations and charging piles in urban areas at least to a certain extent.
Specifically, the invention provides a location and volume selecting method for an electric vehicle public charging station, which comprises the following steps:
dividing a region to be planned into a plurality of sub-regions according to a specified distance;
acquiring population data, private car inventory data and GDP data of the area to be planned;
determining private car keeping quantity prediction data in a preset time period according to the population data, the private car keeping quantity data and the GDP data;
determining the reserved quantity prediction data of the electric automobile according to the reserved quantity prediction data of the private automobile;
determining the minimum demand prediction data of the charging pile according to the electric vehicle inventory prediction data and the preset public pile configuration proportion corresponding to the area to be planned;
and determining the station building and site selecting positions of the charging stations and the quantity of the charging piles in the area to be planned according to the charging pile minimum demand forecast data and the preset site selecting and sizing planning model corresponding to each sub-area.
The electric vehicle public charging station site selection and volume fixing method comprises the steps of determining the population number of an area to be planned, private car inventory data and GDP data, and further utilizing the data to estimate electric vehicle inventory prediction data in a future time period, wherein the electric vehicle inventory prediction data can determine corresponding charging pile minimum demand prediction data so as to achieve long-term planning, meanwhile, for the area to be planned, dividing a plurality of sub-areas, and obtaining construction schemes of charging piles and charging stations in the area by planning based on the combination of the electric vehicle inventory prediction data and preset site selection and volume fixing planning models corresponding to the sub-areas, so that site selection positions and the charging pile number (volume fixing) of the charging stations are obtained. From this, realize more reasonable effectual planning, be adapted to the long-term operation of charging station, improve city user's use and experience.
Further, the preset siting volume planning model includes:
wherein,
represents the lowest overall cost of the charging station plan,
the cost of building the charging station is shown,
indicating that the charging station user has access to the total cost,
the comprehensive benefits of the charging station are shown,
、
representing the weight coefficients.
Further, the formula for determining the station building cost of the charging station comprises:
wherein,
is a number of 0 or 1, and,
equal to 0 is indicated injThe sub-areas are not built with charging stations,
equal to 1 is indicated injThe sub-areas are used for building the charging stations,
is shown injThe charging stations of the sub-area build up the infrastructure costs,cthe cost of each charging post is shown,
to representjEstablishing the number of the charging piles in the sub-area,kindicating the number of sub-regions.
Further, the formula for determining the total cost of access of the charging station user comprises:
wherein,
which represents two different sub-regions of the image,isub-region tojThe distance of the sub-areas is such that,
representiThe electric vehicles in the sub-region hold the quantity prediction data,
to representiThe number of the effective peripheral areas of the sub-area,
representing the cost of access per kilometer of users.
Further, the formula for determining the comprehensive benefit of the charging station comprises:
wherein,
is a number of 0 or 1, and,
equal to 0 is indicated iniThe sub-areas are not built up with the charging stations,
equal to 1 is indicated iniThe sub-areas are used for building the charging stations,
representiAnd (4) presetting a comprehensive benefit score of the subarea.
Further, the constraint conditions of the preset siting volume planning model include:
wherein,
representiThe number of charging piles of the charging stations in the sub-area;
to representjThe number of charging piles at a charging station in a sub-area,
representing the preset minimum number of charging piles;
representing the number of the preset maximum charging piles;
to representjNumber of electric vehicles in sub-region
The required quantity of each electric automobile to the charging pile is represented;
the minimum required quantity of the local charging piles is represented;
representiCharging pile loads of charging stations in the sub-area;
representiThe charging pile maximum load is filled in the presetting of charging station in the subregion.
Further, the private car keeping quantity prediction data comprises a first type of prediction data and a second type of prediction data, and the step of determining the private car keeping quantity prediction data in a preset time period according to the population data, the private car keeping quantity data and the GDP data comprises the steps of;
determining first-class prediction data according to the private car inventory data and a preset inventory prediction time sequence model;
and determining second type prediction data according to the population data, the GDP data and a preset multiple regression prediction model.
Further, the step of determining the station building and locating positions of the charging stations and the number of the charging piles in the area to be planned according to the charging pile minimum demand forecast data and the preset locating and locating planning model corresponding to each sub-area comprises the following steps:
calculating and optimizing the preset locating and sizing planning model based on a neural network algorithm of a genetic algorithm;
and determining the station building and site selecting positions of the charging stations and the number of the charging piles, which accord with preset conditions in the optimizing process.
The invention also provides a computing device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the electric automobile public charging station locating and sizing method when executing the computer program.
The beneficial effect of the computing equipment in the invention is similar to that of the electric vehicle public charging station location and volume determination method, and is not described herein again.
The invention also proposes a computer-readable storage medium, on which a computer program is stored, which, when running, executes the steps of the electric vehicle public charging station siting and sizing method as described above.
The beneficial effect of the computer readable storage medium in the invention is similar to the beneficial effect of the electric vehicle public charging station location and volume determination method, and the description is omitted here.
Drawings
Fig. 1 is a first schematic flow chart of a location and volume selecting method for an electric vehicle public charging station according to an embodiment of the present invention.
Fig. 2 is a second schematic flow chart of the electric vehicle public charging station location and sizing method in the embodiment of the invention.
Fig. 3 is a schematic flow chart of the calculation and optimization of the preset siting and sizing planning model by the neural network algorithm based on the genetic algorithm in the embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein.
In the description herein, references to "an embodiment," "one embodiment," and "one implementation," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or implementation is included in at least one embodiment or implementation of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or implementation. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or implementations.
Referring to fig. 1 and fig. 2, an embodiment of the present invention provides a method for locating and sizing a public charging station for an electric vehicle, including the steps of:
acquiring population data, private car inventory data and GDP data of the area to be planned;
determining private car keeping quantity prediction data in a preset time period according to the population data, the private car keeping quantity data and the GDP data;
in the embodiment of the invention, population data, private car inventory data and GDP data of the area are obtained through precise investigation, so that a database is constructed, and on the basis, the existing data analysis data mining technology is utilized to predict the future private car inventory so as to obtain prediction data.
In a specific embodiment, related data such as traffic and administrative districts in the area to be planned can be obtained, so that more accurate private car reserve prediction data can be determined by combining the population data, the private car reserve data and the GDP data.
The preset time period can be determined according to actual planning conditions, for example, the short time period can be 2022-2030, the long time period can be 2022-2050, and the like.
Determining the electric automobile inventory prediction data according to the private automobile inventory prediction data;
the private cars usually comprise gasoline cars, electric cars and the like, wherein the number of the electric cars accounts for a certain proportion of the number of the private cars, and the proportion coefficient can be obtained through investigation, so that the estimated electric car remaining amount prediction data can be obtained. In addition, for the acquisition of the reserved quantity prediction data of the electric automobile in a relatively long period, the occupation condition of the electric automobile relative to a private automobile has certain fluctuation, the floating trend in related research can be called, and the reserved quantity prediction data of the electric automobile in the area to be planned is further determined.
Determining the minimum demand forecasting data of the charging pile according to the electric vehicle holding quantity forecasting data and the preset public pile configuration proportion corresponding to the area to be planned;
in this embodiment, the minimum demand prediction data of the charging pile is a product of the electric vehicle holding capacity prediction data and a preset public pile configuration ratio corresponding to the area to be planned, where the preset public pile configuration ratio corresponding to the area to be planned is set according to an actual situation.
In a specific example, the ratio of the electric vehicles to the public charging piles is not lower than 7, and as the times develop, the planning determines that the requirement is higher, about 85% of the electric vehicles are charged by using the private self-use piles, so the allocation ratio of the public piles is about 15%, and the allocation ratio of the public charging piles is roughly between 4.
Dividing a region to be planned into a plurality of sub-regions according to a specified distance;
in this embodiment, for the division of the region to be planned, gridding division may be adopted, first, the whole region is uniformly divided into a plurality of grids at a specified uniform distance, each grid is a sub-region, the center of each sub-region is a representative of the region, and the distance between the sub-regions is the distance between the centers of the sub-regions.
In an optional embodiment, each sub-region may be classified according to a preset classification rule, and each sub-region is divided into a first-stage region and a second-stage region according to the magnitude of the traffic flow, the sub-region having the traffic flow above a specified value is classified as the first-stage region, and the sub-region having the traffic flow smaller than the specified value is classified as the second-stage region. Of course, the area can be divided into more levels, and the higher the number is, the lower the level is, the smaller the traffic flow in the area is, so that the division of the sub-areas is more reasonable.
And determining the station building and site selecting positions of the charging stations and the number of the charging piles in the area to be planned according to the minimum demand prediction data of the charging piles and a preset site selecting constant volume planning model corresponding to each sub-area.
In summary, in the electric vehicle public charging station site selection and volume fixing method in the embodiment of the invention, the population quantity of the area to be planned, the private car reserve data and the GDP data are determined, and the electric vehicle reserve prediction data in the future time period are predicted by using the data, so that the corresponding charging pile minimum demand prediction data can be determined by the electric vehicle reserve prediction data, long-term planning is achieved, meanwhile, for the area to be planned, a plurality of sub-areas are divided, and based on the electric vehicle reserve prediction data and the preset site selection and volume fixing planning model corresponding to each sub-area, the construction scheme of the charging piles and the charging stations in the area is obtained through planning, and the site selection position of the charging station and the number (volume fixing) of the charging piles are obtained. From this, realize more reasonable effectual planning, be adapted to the long-term operation of charging station, improve city user's use and experience.
In an optional embodiment of the present invention, the preset siting volume planning model includes:
wherein,
represents the lowest overall cost of the charging station plan,
indicating the establishment of a charging stationThe cost of the station(s) is,
indicating the total cost of access by the charging station user,
the comprehensive benefits of the charging station are shown,
、
representing the weight coefficients.
In this embodiment, the preset siting and sizing planning model is used to represent the lowest comprehensive cost of the charging station planning in the sub-area, and three targets are mainly considered in the determination process of the station building and siting positions of the charging stations and the number of charging piles based on the preset siting and sizing planning model: the charging station building cost, the charging station user access total cost and the comprehensive cost obtained by the comprehensive benefits of the charging station are the lowest. In a specific example, the station building cost of the charging station, the total access cost of a user of the charging station and the comprehensive benefit of the charging station are all pursued to be minimum.
The station building cost of the charging station is specifically the station building base cost and the charging pile building cost, the total access cost of a user of the charging station is the cost spent by a traveler to the charging station, and the comprehensive benefit of the charging station is the comprehensive benefit evaluation of factors such as economic factors, environmental factors, traffic factors and power grid factors which need to be considered for station building.
Referring to fig. 3, in an optional embodiment of the present invention, the determining, according to the charging pile minimum demand prediction data and the preset siting and sizing planning model corresponding to each sub-area, a station building and siting position of a charging station and a number of charging piles in the area to be planned includes:
calculating and optimizing the preset siting and sizing planning model based on a neural network algorithm of a genetic algorithm;
and determining the station building and site selecting positions of the charging stations and the number of the charging piles, which accord with preset conditions in the optimizing process.
In the embodiment of the invention, a neural network method of a conventional genetic algorithm can be adopted, such as the method shown in fig. 3, the calculation optimization of the preset siting and sizing planning model is carried out, the lowest comprehensive cost of the charging station planning in the optimization process is determined, and when the model is optimized to the preset regulation, if the number exceeds the threshold value or the lowest comprehensive cost of the charging station planning meets the requirement, the charging station siting position of each sub-area corresponding to the lowest comprehensive cost of the current charging station planning and the corresponding number of charging piles are determined, so that the final planning scheme is obtained.
Therefore, in the embodiment of the invention, the genetic algorithm and the neural network method are comprehensively utilized, the genetic algorithm is used as a combination strategy of the ensemble learning, the diversity of the individual learners is fully absorbed while the classification accuracy of the individual learners is ensured, iterative evolution is carried out on the neural network ensemble learner by utilizing the modes of genetic operation and species invasion, the neural network ensemble learner with global optimization is obtained, the ensemble learner trained by the neural network individual learner integration method has good global performance, the overfitting phenomenon of the network can be effectively avoided, and finally, a planning scheme can be obtained quickly and accurately.
In an optional embodiment of the present invention, the formula for determining the station building cost of the charging station includes:
wherein,
is a number of 0 or 1, and,
equal to 0 is indicated injThe sub-areas are not built with charging stations,
equal to 1 is indicated injSub-area construction stationThe charging station is provided with a charging hole,
is shown injThe charging stations of the sub-area build up the infrastructure costs,cthe cost per charging pile is expressed and,
to representjEstablishing the number of the charging piles in the sub-area,kindicating the number of sub-regions.
The determination formula of the total access cost of the charging station user comprises the following steps:
wherein,
which represents the two different sub-regions of the picture,isub-region tojThe distance of the sub-areas is such that,
representing the holding capacity prediction data of the electric automobile in the i sub-area,
to representiThe number of the effective peripheral areas of the sub-area,
representing the cost of access to the user per kilometer.
The determination formula of the comprehensive benefit of the charging station comprises the following steps:
wherein,
is a number of 0 or 1, and,
equal to 0 is indicated iniThe sub-areas are not built up with the charging stations,
equal to 1 is indicated iniThe sub-areas are used for building the charging stations,
representiAnd (4) presetting a comprehensive benefit score of the subarea.
It should be noted that, in the preset siting and sizing planning model obtained by combining the above formula for determining the station building cost of the charging station, the formula for determining the total access cost of the user of the charging station and the formula for determining the comprehensive benefit of the charging station,iandjthe counting symbols are respectively the counting symbols representing the sub-areas, wherein the counting symbols in the determination formula of the comprehensive benefit of the charging station and the determination formula of the station building cost of the charging station are different, mainly for distinguishing the two formulas, but for the determination formula of the total cost of the access of the user of the charging station, the cost data for the access of different areas are reflected, so that the counting symbols simultaneously appeariAndjfor distinguishing two different sub-areas.
In addition, in one embodiment, in order to improve the rationality of the number planning in the site selection and charging of the charging station, constraint conditions are set for the preset site selection constant volume planning model, so that a more reasonable result can be obtained. The constraint conditions of the preset siting constant volume planning model comprise:
wherein,
to representiThe number of charging piles of the charging stations in the sub-area is an integer; k is the number of the sub-regions divided in the region to be planned;
to representjThe number of charging piles at a charging station in a sub-area,
representing the preset minimum number of charging piles;
representing the number of the preset maximum charging piles;
to representjNumber of electric vehicles in sub-region
The required quantity of each electric automobile to the charging pile is represented;
the minimum required quantity of the local charging piles is represented;
representiCharging pile loads of charging stations in the sub-area;
to representiThe maximum load of the preset charging pile of the charging station in the sub-area is achieved.
For the above constraint, formula (1) specifically indicates that at least one sub-area is to be charged; the formula (2) specifically represents the minimum and maximum quantity requirements of the construction charging piles existing in each charging station; the formula (3) represents the subarea of each charging station, and the requirement of the electric vehicle in the subarea must be met; the formula (4) shows that the total number of the charging piles has minimum requirements and needs to meet the minimum local requirements; equation (5) indicates that the load of the charging post in the sub-area cannot exceed a maximum load.
Therefore, by setting the constraint conditions, when the lowest comprehensive cost optimization of the charging station planning is carried out by combining the preset site selection constant volume planning model by constructing the site selection positions of the charging stations in the area to be planned and the quantity of the charging piles, the finally determined site selection positions of the charging stations and the quantity of the charging piles are more reasonable.
In summary, in the invention, under consideration of influence factors such as construction cost, traveler satisfaction, policy response, regional characteristics, power grid requirements and the like, the obtained preset site selection constant volume planning model can be used for accurately and quickly determining the site selection positions of the charging stations in each sub-region and the quantity of the charging piles, and further determining the site selection positions of the charging stations in the region to be planned and the quantity of the charging piles.
Referring to fig. 2, in an alternative embodiment of the present invention, the private car occupancy prediction data includes a first type of prediction data and a second type of prediction data, and the determining private car occupancy prediction data in a preset time period according to the population data, the private car occupancy data and the GDP data includes:
determining first-class prediction data according to the private car inventory data and a preset inventory prediction time sequence model;
and determining second type prediction data according to the population data, the GDP data and a preset multiple regression prediction model.
In the embodiment, the reserved quantity of the private car in a future period of time, namely the first-type prediction data, can be predicted by combining historical reserved data of the private car package and a preset reserved quantity prediction time sequence model; the private car reserve in a future period of time, namely second-class prediction data, can be predicted through population data, GDP data and a preset multiple regression prediction model; therefore, the determined prediction data can be respectively used for determining planning schemes by subsequently combining with a preset site selection constant volume planning model, and finally, one scheme is selected, so that site selection and the determination of the number of charging piles are more reasonable.
In a specific example, for a city, the preset inventory prediction time series model is M1= -19050487.433+9487.983 × Y, where M1 is the first type of prediction data and Y is the year, and the value of the first type of prediction data is obtained by substituting the value of the year to be predicted.
Similarly, for the city, a preset multiple regression prediction model is a binary regression prediction model, and specifically comprises the following steps: m2= -61237.634+0.102 × N +139.748 × G, wherein M2 is second-class prediction data, N is human mouth data and is in the unit of ten thousand persons, and G is GDP data and is in the unit of hundred million yuan.
For the areas to be planned corresponding to other cities, the preset inventory prediction time series model and the preset multiple regression prediction model can be determined according to actual historical data, and are not limited herein.
Taking city a as an example, the flow of station building and site selection of the charging station and the determination of the number of charging piles by adopting the site selection and sizing method for the electric vehicle public charging station in the embodiment of the invention is as follows:
gridding the city A, uniformly dividing grids with the side length of three hundred meters as a unit, dividing the city A into 1924 grids after removing invalid regions, numbering the grids, taking each grid as a subarea of the city A, taking the center of each grid as a representative of the subarea, and calculating the distance between the centers of different subareas to obtain the distance between the subareas.
In the embodiment, the charging station location and volume fixing planning of the embodiment needs to achieve full coverage of the charging stations in the city A, the charging suitable distance acceptable to users in the first-level area is set to be 0.9 kilometer, the charging station in the second-level area is set to be 2 kilometers, namely, the charging stations can be found in the range of 0.9 kilometer in any place in the first-level area, and the charging stations can be found in the range of 2 kilometers in any place in the second-level area.
And calculating the distance between each grid and other grids in the selected area, and calculating to obtain the serial numbers and the number of the primary area and the secondary area. In 1924 grids of city A, 766 primary areas and 1158 secondary areas are obtained in total.
According to the practical situation of the A city, the parameters in the preset siting constant volume planning model in each area are determined by methods such as investigation, calculation and the like, and a foundation is laid for the subsequent model solution. And determining the number, the site and the capacity of the charging stations required to be established in the city A and the service range of each charging station by applying a preset site selection and volume determination planning model and a neural network algorithm based on a genetic algorithm. Model solving results are carried out on the charging station location and volume problems of the city A in the next 3 years, the next 5 years and the next 10 years to obtain the following table 1.
Table 1: charging facility planning table in city A
| Scheme(s) | 2022 year old | 2024 year old | 2029 year old |
| Number of charging stations | 66 | 66 | 66 |
| Number of charging piles | 295 | 559 | 1458 |
Specifically, taking the planning of the next 10 years as an example, the planning of the charging station positions and the pile numbers of 2029 years in city a is as follows:
table 2: charging station layout planning table in 2029 year a city
| Address numbering | Number of charging piles | Address numbering | Number of charging piles | Address numbering | Number of charging piles |
| 28 | 30 | 952 | 6 | 1480 | 6 |
| 57 | 11 | 962 | 6 | 1491 | 30 |
| 66 | 30 | 969 | 30 | 1504 | 6 |
| 139 | 30 | 980 | 30 | 1530 | 30 |
| 177 | 30 | 1014 | 30 | 1563 | 30 |
| 193 | 30 | 1021 | 30 | 1595 | 30 |
| 237 | 30 | 1061 | 6 | 1614 | 30 |
| 243 | 30 | 1093 | 30 | 1687 | 30 |
| 319 | 30 | 1100 | 30 | 1690 | 30 |
| 366 | 30 | 1150 | 30 | 1703 | 30 |
| 424 | 30 | 1236 | 30 | 1759 | 30 |
| 427 | 30 | 1241 | 6 | 1762 | 30 |
| 482 | 7 | 1244 | 6 | 1767 | 6 |
| 495 | 9 | 1260 | 14 | 1808 | 6 |
| 594 | 4 | 1269 | 30 | 1820 | 30 |
| 723 | 7 | 1317 | 14 | 1825 | 30 |
| 766 | 30 | 1329 | 6 | 1853 | 30 |
| 792 | 6 | 1341 | 30 | 1884 | 30 |
| 813 | 30 | 1368 | 6 | 1885 | 8 |
| 878 | 30 | 1385 | 30 | 1890 | 5 |
| 884 | 30 | 1472 | 9 | 1893 | 30 |
| 922 | 30 | 1475 | 30 | 1913 | 8 |
The invention also provides a computing device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the electric automobile public charging station locating and sizing method when executing the computer program.
The beneficial effect of the computing equipment in the invention is similar to that of the electric vehicle public charging station location and volume determination method, and is not described herein again.
The invention also proposes a computer-readable storage medium, on which a computer program is stored, which, when running, executes the steps of the electric vehicle public charging station siting and sizing method as described above.
The beneficial effect of the computer readable storage medium in the invention is similar to the beneficial effect of the electric vehicle public charging station location and volume determination method, and the description is omitted here.
In general, computer instructions for carrying out the methods of the present invention may be carried on any combination of one or more computer-readable storage media. Non-transitory computer readable storage media may include any computer readable medium except for the signal itself, which is temporarily propagating.
A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code for carrying out operations for aspects of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, or a combination thereof, as well as conventional procedural programming languages, such as the "C" language or similar programming languages, and in particular, python languages suitable for neural network computing and TensorFlow, pyTorch-based platform frameworks may be used. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Those of ordinary skill in the art will understand that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for locating and sizing a public charging station of an electric automobile is characterized by comprising the following steps:
acquiring population data, private car inventory data and GDP data of an area to be planned;
determining private car keeping quantity prediction data in a preset time period according to the population data, the private car keeping quantity data and the GDP data;
determining the reserved quantity prediction data of the electric automobile according to the reserved quantity prediction data of the private automobile;
determining the minimum demand prediction data of the charging pile according to the electric vehicle inventory prediction data and the preset public pile configuration proportion corresponding to the area to be planned;
dividing the region to be planned into a plurality of sub-regions according to a specified distance;
and determining the station building and site selecting positions of the charging stations and the number of the charging piles in the area to be planned according to the minimum demand prediction data of the charging piles and a preset site selecting constant volume planning model corresponding to each sub-area.
2. The electric vehicle public charging station siting and sizing method according to claim 1, wherein the preset siting and sizing planning model comprises:
wherein,
represents the lowest overall cost of the charging station plan,
the cost of building the charging station is shown,
indicating the total cost of access by the charging station user,
the comprehensive benefits of the charging station are shown,
、
representing the weight coefficients.
3. The electric vehicle public charging station site selection and sizing method as claimed in claim 2, wherein the formula for determining the station building cost of the charging station comprises:
wherein,
is a number of 0 or 1, and,
equal to 0 is indicated injThe sub-areas are not built with charging stations,
equal to 1 is indicated injThe sub-areas are used for building the charging stations,
is shown injThe charging stations of the sub-area build up the infrastructure costs,cthe cost per charging pile is expressed and,
to representjEstablishing the number of the charging piles in the sub-area,kindicating the number of sub-regions.
4. The electric vehicle bus charging station siting and sizing method according to claim 3, wherein said determination of the charging station user's access total cost comprises:
wherein,
which represents two different sub-regions of the image,isub-region tojThe distance of the sub-areas is such that,
to representiThe electric vehicles in the sub-region hold the quantity prediction data,
representiThe number of effective peripheral areas of the sub-area,
representing the cost of access per kilometer of users.
5. The electric vehicle public charging station siting and sizing method according to claim 4, wherein the formula for determining the comprehensive benefit of the charging station comprises:
wherein,
is a number of 0 or 1, and,
equal to 0 is indicated iniThe sub-areas do not build the charging stations,
equal to 1 is indicated iniThe sub-area is built up with the charging stations,
to representiAnd (4) presetting a comprehensive benefit score of the subarea.
6. The electric vehicle public charging station siting and sizing method according to claim 5, wherein the constraint conditions of the preset siting and sizing planning model comprise:
wherein,
representiThe number of charging piles of charging stations in the sub-area;
representjThe number of charging piles at a charging station in a sub-area,
representing the preset minimum number of charging piles;
representing the number of the preset maximum charging piles;
to representjNumber of electric vehicles in sub-region
The required quantity of each electric automobile to the charging pile is represented;
the minimum required quantity of the local charging piles is represented;
representiCharging in sub-areaCharging pile load of the power station;
representiThe charging pile maximum load is filled in the presetting of charging station in the subregion.
7. The electric vehicle public charging station siting and sizing method according to any one of claims 1 to 5, wherein the private car occupancy prediction data comprises a first type of prediction data and a second type of prediction data, and the determination of private car occupancy prediction data in a preset time period according to the population data, the private car occupancy data and the GDP data comprises;
determining the first type of prediction data according to the private car inventory data and a preset inventory prediction time sequence model;
and determining the second type of prediction data according to the population data, the GDP data and a preset multiple regression prediction model.
8. The electric vehicle public charging station site selection and sizing method according to claim 1, wherein the step of determining the station building and site selection positions of the charging stations and the number of the charging piles in the area to be planned according to the charging pile minimum demand prediction data and a preset site selection and sizing planning model corresponding to each sub-area comprises the steps of:
calculating and optimizing the preset locating and sizing planning model based on a neural network algorithm of a genetic algorithm;
and determining the station building and site selecting positions of the charging stations and the number of the charging piles, which accord with preset conditions in the optimizing process.
9. A computing device comprising a memory storing a computer program and a processor implementing the steps of the electric vehicle public charging station location determination method of any of claims 1-8 when the processor executes the computer program.
10. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program when executed performs the steps of the electric vehicle public charging station location determination method of any of claims 1-8.
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