CN113011652A - Site selection optimization method and system for electric vehicle charging station - Google Patents
Site selection optimization method and system for electric vehicle charging station Download PDFInfo
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Abstract
The utility model provides an electric vehicle charging station site selection optimization method and system, comprising: according to the construction requirements of a charging station in a certain area, acquiring a plurality of charging demand stations in the area, randomly selecting a preset number of stations from the charging demand stations, and constructing a candidate address set of a charging service station; constructing a charging station site selection model considering construction cost and distance condition constraints; carrying out optimization solution on the charging station site selection model by using an improved immune algorithm to obtain a primary charging service station address; dividing the area charging station service area based on the Voronoi diagram, and performing rationality analysis on the obtained charging station address and the distribution of demand stations; and determining the optimal charging service station address according to the analysis result.
Description
Technical Field
The disclosure belongs to the technical field of site selection optimization of charging stations, and particularly relates to a site selection optimization method and system for an electric vehicle charging station.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the continuous development of social economy in China, people's thinking is brought about by the problems of environmental protection and conversion of new and old kinetic energy under the background that non-renewable fuels such as coal, petroleum and the like are exploited in large quantities and the problem of environmental pollution is aggravated. Aiming at the current situation that the current non-renewable resources are over-exploited and face exhaustion, new energy is developed, and the exploration of green, energy-saving and environment-friendly travel modes becomes the current research focus. The electric automobile is serving as a green, environment-friendly and feasible travel tool and is facing the rapid development stage. While the advantages and the characteristics of the electric automobile are concerned, the development of the electric automobile is restrained to a certain extent by some factors. In the scientific and technological wave of rapid development of electric vehicles, a charging station is an important component of a charging facility, and the site selection of the charging station becomes an important ring of the strategic layout of urban scientific development, and is a key step of converting new and old kinetic energy of cities. The site selection planning and the layout of the charging pile are closely related to the development of the electric automobile, and as the electric automobile at the present stage is in short endurance mileage, the battery problem can not make breakthrough progress in a short time, and the reasonable planning and the layout of the charging station are very important. Under the condition that the quantity of charging piles can meet the charging requirement of a charging service point, the reasonable and convenient site selection can stimulate the desire of people to select to drive the electric automobile for going out.
The inventor finds that the establishment of the objective function of the existing charging station address optimization method does not effectively consider the construction cost of the charging station, and does not effectively consider the close correlation between the distance from the charging station to the charging station and the selection of the charging station, and meanwhile, the existing method cannot effectively balance the relationship between the construction cost and the distance, so that the optimized charging station address cannot meet the actual requirement.
Disclosure of Invention
The method comprises the steps of setting an objective function, considering the problems of distance of a charging station and construction cost, optimizing and selecting the address of the charging station by using an improved immune algorithm, keeping the diversity of a group based on a diversity generation and maintenance mechanism of the immune system, overcoming the problem of difficult processing 'precocity' in the general optimization process, finally dividing an area by using a Voronoi diagram, further analyzing and checking an optimization result, and ensuring the optimal address selection result of the charging station through multiple times of simulation comparative analysis.
According to a first aspect of the embodiments of the present disclosure, there is provided an electric vehicle charging station site selection optimization method, including:
according to the construction requirements of a charging station in a certain area, acquiring a plurality of charging demand stations in the area, randomly selecting a preset number of stations from the charging demand stations, and constructing a candidate address set of a charging service station;
constructing a charging station site selection model considering construction cost and distance condition constraints;
carrying out optimization solution on the charging station site selection model by using an improved immune algorithm to obtain a primary charging service station address;
dividing the area charging station service area based on the Voronoi diagram, and performing rationality analysis on the obtained charging station address and the distribution of demand stations;
and determining the optimal charging service station address according to the analysis result.
Furthermore, the charging station site selection model is constructed based on the ideas of a P-median model and a maximum coverage model, the product of the distance value from the charging demand station to the charging service station and the charging pile number of the charging service station is considered in the charging station site selection model, the charging service station construction cost is considered, and a distance punishment item is set.
Further, the distance penalty item is used for playing a role in inhibition to promote selection of the optimal individual when the distance from the demand station to the service station does not meet the set threshold.
According to a second aspect of the embodiments of the present disclosure, there is provided an electric vehicle charging station site selection optimization system, including:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring a plurality of charging demand stations in a certain area according to the charging station construction requirements of the area, randomly selecting a preset number of stations from the charging demand stations and constructing a candidate address set of charging service stations;
a model construction unit for constructing a charging station site selection model considering construction cost and distance condition constraints;
the optimization solving unit is used for carrying out optimization solving on the charging station address selection model by utilizing an improved immune algorithm to obtain a primary charging service station address;
the reasonability analysis unit is used for dividing the region charging station service region based on the Voronoi diagram and carrying out reasonability analysis on the obtained charging station address and the distribution of the demand station;
and the optimal result output unit is used for determining the optimal charging service station address according to the analysis result.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory for execution, where the processor implements the method for optimizing location selection of an electric vehicle charging station when executing the program.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an electric vehicle charging station location optimization as described.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) the invention provides an optimization method for site selection of an electric vehicle charging station, which is characterized in that the optimized site selection of the charging station is carried out by utilizing an improved immune algorithm, the diversity of a group is kept based on a diversity generation and maintenance mechanism of an immune system, and the problem of intractable early maturity in the general optimization process is solved; meanwhile, in the setting of the objective function, on one hand, the problem of distance is considered from the perspective of users, and on the other hand, the construction cost is considered from the perspective of governments or enterprises; finally, the Voronoi diagram is used for dividing the region, the selection result of the algorithm is further analyzed and checked, and the current selection result is effectively ensured to be optimal through multiple times of simulation comparative analysis.
(2) The scheme of the disclosure makes certain improvement on the immune algorithm: firstly, the setting of the fitness function is adjusted, and the addition of the weight value enables the reaction of a user to be more convenient when the construction cost and the distance are distributed according to different proportions; and secondly, introducing a detection link in the mutation operation, wherein after the mutation operation is finished, individual individuals possibly do not meet the distance constraint condition, and the detection link is introduced to ensure that the site selection finally meets the requirement of all site coverage.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a general block diagram of an electric vehicle charging station location optimization method according to a first embodiment of the disclosure;
FIG. 2 is a flowchart of an immunization algorithm according to a first embodiment of the present disclosure;
FIG. 3 is a flow chart of a specific embodiment described in the first example of the present disclosure;
fig. 4 is a detailed schematic diagram of the number of charging piles of each charging demand station and the unit construction cost in the first embodiment of the disclosure;
FIG. 5(a) is a diagram showing the optimized addressing result of the improved immunization algorithm according to the first embodiment of the disclosure;
FIG. 5(b) is a graph of the improved immune algorithm convergence curve described in the first embodiment of the present disclosure;
fig. 5(c) is a schematic diagram of a Voronoi diagram region division result in the first embodiment of the disclosure;
fig. 5(d) is a schematic diagram of the number of charging piles to be constructed at each charging service station and the unit construction cost in the first embodiment of the disclosure;
fig. 5(e) is a schematic diagram of an optimized address selection result and a Voronoi region partition result of the immune algorithm according to the first embodiment of the disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The first embodiment is as follows:
the embodiment aims at providing an electric vehicle charging station site selection optimization method.
An electric vehicle charging station site selection optimization method comprises the following steps:
according to the construction requirements of a charging station in a certain area, acquiring a plurality of charging demand stations in the area, randomly selecting a preset number of stations from the charging demand stations, and constructing a candidate address set of a charging service station;
constructing a charging station site selection model considering construction cost and distance condition constraints;
carrying out optimization solution on the charging station site selection model by using an improved immune algorithm to obtain a primary charging service station address;
dividing the area charging station service area based on the Voronoi diagram, and performing rationality analysis on the obtained charging station address and the distribution of demand stations;
and determining the optimal charging service station address according to the analysis result.
In particular, for ease of understanding, the embodiments of the present disclosure are described in detail below with reference to the attached drawings:
the site selection optimization method for the electric vehicle charging station comprises the following steps: the method comprises the steps of establishing a mathematical model by using ideas of a P-median model and a maximum coverage model, normalizing processing data, an immune algorithm optimization process, simulation result analysis and Voronoi Diagram (Voronoi Diagram) analysis and implementation.
In the establishment process of the mathematical model, experience is extracted from a traditional P-median model and a maximum coverage model, and besides the product of the distance value from a charging demand station to a charging service station and the number of charging piles of the charging service station, on one hand, the consideration of the construction cost of the charging service station is added; and on the other hand, a distance penalty item is set, namely when the distance from the demand station to the service station does not meet the set distance threshold value, the distance penalty item is restrained so as to promote the selection of the optimal individual.
By using the mathematical model built after the traditional model for reference:
Zij≤hj，i∈N，j∈Mi 
Zij，hj∈｛0，1｝，i∈M，j∈Mi
dij≤s
wone,wtwo∈{0,1},wone+wtwo＝1
wherein N ═ {1,2,3, …, N } is allA sequence number set of demand points; miFor a set of charging service stations having a distance to the charging demand station i less than s,
wone,wtwothe weight value reflects the influence of the construction cost and the distance of the charging station on the user; cjThe construction cost of the charging service station; cfFor the set distance punishment item, the station which does not meet the distance requirement is restrained; w is aiThe number of the charging piles of the charging demand station is represented; dijRepresents the distance from the charging demand station i to the charging service station j closest thereto; zijThe variable is 0-1, the variable represents the demand distribution relationship between the charging demand station and the charging service station, when the variable is 1, the variable represents that the demand of the charging demand station i is provided by the charging service station j, otherwise, the variable is 0; h isjIs a 0-1 variable, which when 1, indicates that point j is selected as the charging service station; s is the upper limit of the distance of the charging service station from the charging demand station served by it.
2) And (5) normalizing the coordinates of the station. The longitude and latitude coordinates are usually selected directly during coordinate selection, but when each station is relatively close to each other in a certain small range, the respective coordinates are particularly close to each other and are concentrated in the certain small range, so that subsequent data processing and operation are not facilitated, and the final result is adversely affected.
Normalization:
3) as shown in fig. 2, the immune algorithm optimization process is demonstrated. Compared with genetic algorithms, immune algorithms are characterized mainly by different evaluation, selection and generation modes of individuals. The immune algorithm is obtained by calculating the affinity degree in the evaluation of the individual, the individual selection is based on the affinity degree, the authenticity and diversity of a system can be better reflected, the evaluation of the individual is more objective, and the appropriate individual can be better selected. The algorithm can not only enable people to know the characteristics of the artificial immune system more deeply, but also can be better fused with other intelligent strategies.
Wherein:
affinity between antibody and antigen:
affinity between antibody and antibody:
antibody concentration:
expected propagation probability:
wherein, FvIs an objective function; k is a radical ofv，sIs the same number of bits in the antibody as in the antibody; l is the antibody length; n is the total number of antibodies; alpha is a constant.
The immunization operation comprises the following steps:
(1) selecting: selecting individuals by a selection function according to the individual fitness value by adopting a roulette method;
(2) and (3) crossing: the crossing operation adopts a real number crossing method to carry out crossing;
(3) mutation: and performing mutation by using a real number mutation method.
Furthermore, the improved immune algorithm disclosed by the disclosure is characterized in that on the basis of the traditional immune algorithm, a relative detection link is added after a change of a fitness function, a penalty term is added and a variation link is added; wherein, (1) the change of fitness function (namely the addition of weighted value), it can change the proportion that construction cost and distance account for, reflect better when the proportion value changes, the change of the result of site selection, explore the construction cost and sensitivity to user of the distance value. (2) And adding a punishment item, and finally setting a punishment value in the objective function, wherein in the solving process, when a station which does not meet the constraint requirement appears, the punishment value can play a role in inhibiting the station so that the site selection result conforms to the condition as much as possible. (3) A detection link step: 1) after selection, crossing and mutation operations, the selected charging service station set is sent to a detection link; 2) selecting two stations with the farthest distance among the stations in all the charging demand station sets, and taking the distance value as the distance value of the maximum coverage range; 3) calculating the distances between stations in the charging service station set by an Euclidean distance formula, and selecting the maximum value by using a maximum function; 4) and judging whether the calculated value is not larger than the maximum coverage range, if so, meeting the requirement, and otherwise, repeating the operation.
Specifically, the adjustment of the weight value represents a change in the selection of the user site when the specific gravity of the distance to the construction cost is changed. The initial weighted values are all 0.5, and the sensitivity of the user to the construction cost and the distance can be considered to be the same at the moment. In practice, however, each individual acts as an independent individual with a different response to the same change. Observing the result of the final charging station selection by changing the weight value; meanwhile, a detection link is arranged after the variation link, and the purpose of the detection link is to judge whether the currently selected station meets the requirement of covering all stations in the maximum coverage model, so that all stations can be effectively ensured to be within the coverage range, and each charging demand point has a corresponding charging service station to provide service for the charging demand point.
4) Voronoi Diagram (Voronoi Diagram) analysis and implementation. The Veno diagram, also called Thiessen polygon or Dirichlet diagram, is composed of a set of continuous polygons composed of perpendicular bisectors connecting two adjacent point straight lines.
Features of Voronoi diagrams
(1) Each V polygon is provided with a generator;
(2) the distance from each inner point of the V polygon to the generator is shorter than the distances from the inner points to other generators;
(3) the distances from the points on the polygon boundary to the generator generating the boundary are equal;
(4) the Voronoi polygon boundaries of the adjoining figure are a subset of the original adjoining boundaries.
The Voronoi diagram has the general characteristic of dividing the adjacent areas according to the distance, and the application range is wide. After the charging service stations are obtained through the immune algorithm, the Voronoi diagram is used for dividing the regions and observing the distribution situation of other charging demand stations, and due to the characteristic that the Voronoi diagram is the closest in distance from the region divided according to the point set to the point, the distribution situation of each station can be seen clearly, and whether the station selection and the demand station distribution are reasonable or not can be judged.
The specific Vornoi diagram is mainly used for judging whether a site set obtained through the model meets constraint requirements or not. The main process is as follows:
(1) and solving the site selection model through an immune algorithm, and selecting a proper charging service station to form a station set. In this example, the set of sites is: a coach bus stop, a flood square, a Henglong square, Shandong provincial Hospital, Olympic center, and Qianfshan.
(2) And taking each charging service station in the station set as a generating element, and dividing the service range by utilizing a Voronoi diagram.
(3) Judging whether the current addressing result is appropriate or not by combining the model solution result
When the Voronoi diagram is used for dividing the service range, a plurality of V polygons are generated. The distance from each point within the V-polygon to the generator is shorter than the distances to other generators. Points on the V-polygon boundary are equidistant from the generator that generated the boundary.
If the station a is found to be a charging service station and the station a provides charging service for the station B through model solution, in the Vornoi diagram, the station a is a generator and the station B is inside a V polygon generated by the station a, and the result is considered to be appropriate. However, if site B is located within the V-polygon generated by other sites, it is not as expected and the selection is not appropriate.
As will be described in brief with reference to fig. 5(e), the flood square is a charging service station, and the service area is divided using the charging service station as a generator. And solving a result obtained by the model to provide charging service for the Hongkou square station position daming lake station and the Shandong university station (central school district). The Vornoi diagram results in a situation where both the damming lake station and the Shandong university (central school zone) station are within the V-polygon generated by the floodgate square station, and the selection is deemed appropriate. For daming lake stations, which are located on the V polygon boundary, points on the V polygon boundary are equidistant from the generator that generated the boundary. The unit construction cost of the Jinan long-distance bus stop is 5700 Yuan, and the flood square is 5500 Yuan. Therefore, the reason for selecting the flood building square for the great lake station is that the construction cost of the flood building square is cheaper than that of the Jinan long-distance bus station. And other sites are found to meet the requirements through analysis.
The scheme of the present disclosure is illustrated below by specific examples:
fig. 3 is a block diagram of an overall embodiment of an immune algorithm and Voronoi diagram addressing optimization for an electric vehicle charging station. Wherein:
1) the objective function consists of three parts: the charging service station construction cost, the product of the distance from the charging demand station to the charging service station and the charging pile number of the charging demand station, and the distance punishment item. The weight values are all set to 0.5, and the distance threshold is 2 km.
2) And selecting the charging demand stations by comprehensively considering population distribution, regional functions, geographic positions and economic development conditions. Taking an area in the city of denna as an example, site selection planning of a charging station is performed, 15 charging demand stations are selected in total, and the stations are specifically as follows, and 6 stations are selected as charging service stations.
TABLE 1 list of charging demand stations in certain region of Jinan City
3) And (5) processing the point data of the demand. For the coordinates, the longitude and latitude coordinates are normalized and then mapped to the interval [10,100 ]. The processed coordinate values are proper in distribution range and no longer have dimensions, and numerical calculation is facilitated. For the unit construction cost and the number of the charging piles of each charging demand station, in reality, due to the fact that the construction of the charging stations is incomplete, the related data are less, and therefore, the unit construction cost and the number of the charging piles when the charging stations are constructed at each station are assumed, and the specific figure shows.
4) The specific implementation steps and key parameter setting of the immune algorithm. The method comprises the following specific steps:
(1) and (5) analyzing the problem. Analyzing the characteristics of the problem and the solution thereof, and designing a proper expression form of the solution.
(2) An initial population of antibodies is generated. The population size sizepop is set to be 50, the memory storage capacity overbest is 10, the number of charging service stations is 6, the cross probability is 0.5, the variation probability is 0.4, and the iteration number is 300.
(3) Each antibody in the above population was evaluated. The evaluation of the individuals here is based on the expected probability of reproduction P of the individual. Through analyzing an expected propagation probability formula, the higher the individual fitness is, the higher the expected propagation probability is; the greater the individual concentration, the smaller the probability of propagation is expected. Therefore, individuals with high fitness are encouraged, and individuals with high concentration are restrained, so that the diversity of the population is ensured.
(4) Forming a parent population.
(5) Judging whether an ending condition is met, if so, ending; otherwise, the next operation is continued.
(6) Generation of New populations
(7) Go to execute step (3)
5) And (5) realizing algorithm simulation by using a Matlab tool. The convergence curve of the fitness function is shown in the figure with the selection result of the charging service station.
6) The Voronoi diagram performs region division. After the selection result of the charging service station is obtained through Matlab by using an immune algorithm, the position of the charging service station is set as the center of the area where the charging service station is located, the area division is carried out, and the distribution condition of the remaining 9 charging demand stations is observed. As shown in the figure. It is observed from the figure that the charging demand stations obtained by the immune algorithm and the corresponding charging service stations are all in the area divided by the charging service stations, for the Jinan station and the Daling lake station which are positioned on the boundary line of the area, the Jinan provincial hospital and the Hongkou square are selected to be charged but the Jinan long-distance bus station is not selected, because of the construction cost, the unit construction cost of the Jinan long-distance bus station is 5700 yuan, and the Jinan provincial hospital and the Hongkou square are 5400 yuan and 5500 yuan respectively, so that the Shandong provincial hospital and the Hongkou square station with less unit construction cost are selected for reducing the expenditure of the construction cost.
7) And (5) processing and analyzing data, and determining an optimized address selection scheme. Through optimization, the Jinan coach station, Shandong provincial Hospital, Hongtou square, Qianfshan, Henglong square and Olympic center are selected as the charging service stations.
Example two:
the purpose of this embodiment is an electric automobile charging station site selection optimizing system.
An electric vehicle charging station site selection optimization system, comprising:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring a plurality of charging demand stations in a certain area according to the charging station construction requirements of the area, randomly selecting a preset number of stations from the charging demand stations and constructing a candidate address set of charging service stations;
a model construction unit for constructing a charging station site selection model considering construction cost and distance condition constraints;
the optimization solving unit is used for carrying out optimization solving on the charging station address selection model by utilizing an improved immune algorithm to obtain a primary charging service station address;
the reasonability analysis unit is used for dividing the region charging station service region based on the Voronoi diagram and carrying out reasonability analysis on the obtained charging station address and the distribution of the demand station;
and the optimal result output unit is used for determining the optimal charging service station address according to the analysis result. In further embodiments, there is also provided:
an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment one. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The site selection optimization method and system for the electric vehicle charging station can be realized, and have wide application prospects.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. An electric vehicle charging station site selection optimization method is characterized by comprising the following steps:
according to the construction requirements of a charging station in a certain area, acquiring a plurality of charging demand stations in the area, randomly selecting a preset number of stations from the charging demand stations, and constructing a candidate address set of a charging service station;
constructing a charging station site selection model considering construction cost and distance condition constraints;
carrying out optimization solution on the charging station site selection model by using an improved immune algorithm to obtain a primary charging service station address;
dividing the area charging station service area based on the Voronoi diagram, and performing rationality analysis on the obtained charging station address and the distribution of demand stations;
and determining the optimal charging service station address according to the analysis result.
2. The electric vehicle charging station site selection optimization method according to claim 1, wherein the charging station site selection model is constructed based on ideas of a P-median model and a maximum coverage model, and in the charging station site selection model, in addition to consideration of a product of a distance value from a charging demand station to a charging service station and the number of charging piles of the charging service station, consideration of construction cost of the charging service station is added, and a distance penalty item is set.
3. The electric vehicle charging station site selection optimization method as claimed in claim 2, wherein the distance penalty term is used for inhibiting to promote selection of an optimal individual when the distance from the demand station to the service station does not meet a set threshold.
4. The method as claimed in claim 1, wherein the improved immune algorithm is based on a traditional immune algorithm, and changes of fitness function, adding penalty term and adding related detection link after variation link are added.
5. The electric vehicle charging station site selection optimization method according to claim 4, wherein the fitness function is changed by introducing a weight value to change the proportion of the construction cost and the distance, the penalty term is set to a penalty value at the end of the objective function, and in the solving process, when a station which does not meet the constraint requirement appears, the station is restrained by the penalty value, so that the site selection result meets the condition as much as possible.
6. The method of claim 1, wherein the preliminary determination of all candidate charging station addresses for a region of charging stations based on charging station construction requirements for the region comprises: and preliminarily determining the candidate charging station addresses of the charging demand stations according to the population distribution, the regional function and the geographic position of the region.
7. The method as claimed in claim 1, wherein the longitude and latitude coordinates of the station of the charging demand station are normalized and mapped to a preset interval.
8. An electric vehicle charging station site selection optimization system, comprising:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring a plurality of charging demand stations in a certain area according to the charging station construction requirements of the area, randomly selecting a preset number of stations from the charging demand stations and constructing a candidate address set of charging service stations;
a model construction unit for constructing a charging station site selection model considering construction cost and distance condition constraints;
the optimization solving unit is used for carrying out optimization solving on the charging station address selection model by utilizing an improved immune algorithm to obtain a primary charging service station address;
the reasonability analysis unit is used for dividing the region charging station service region based on the Voronoi diagram and carrying out reasonability analysis on the obtained charging station address and the distribution of the demand station;
and the optimal result output unit is used for determining the optimal charging service station address according to the analysis result.
9. An electronic device comprising a memory, a processor and a computer program stored and executed on the memory, wherein the processor when executing the program implements an electric vehicle charging station location optimization method as claimed in any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements an electric vehicle charging station location optimization method as claimed in any one of claims 1 to 7.
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