CN118071109A - Electric vehicle charging station site selection and volume determination method, system, equipment and medium - Google Patents
Electric vehicle charging station site selection and volume determination method, system, equipment and medium Download PDFInfo
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Abstract
The invention relates to an electric vehicle charging station site selection and volume determination method, a system, equipment and a medium, and relates to the technical field of electric vehicle charging station position and capacity planning, wherein the method comprises the following steps: step S1: taking the minimum total social annual cost as an objective function, and constructing an electric vehicle charging station planning model under preset constraint conditions; step S2: and setting n selected charging stations in the required station building area in advance, determining the number of charging piles corresponding to the n selected charging stations by solving the electric vehicle charging station planning model, and simultaneously selecting a plurality of charging stations from the n selected charging stations as the optimal charging station address to finish the electric vehicle charging station site selection and volume fixation. The invention provides a new idea for the site selection and the volume setting of the electric automobile charging station.
Description
Technical Field
The invention relates to the technical field of electric vehicle charging station position and capacity planning, in particular to an electric vehicle charging station location and volume-determining method, system, equipment and medium.
Background
The electric automobile is not only a novel transportation means, but also a random electric load. Therefore, when the charging facility is planned, not only the traffic characteristics of the electric vehicle but also the load characteristics of the electric vehicle need to be considered.
The charging station planning problem can be divided into a charging station address selection problem and a charging station constant volume problem. Charging station location modes can be divided into two types, namely alternative station addresses and non-alternative station addresses. The alternative site mode is to select alternative sites suitable for constructing the charging station according to the actual geographic information factors of the planning area, and then determine the position of the charging station by considering the factors of traffic conditions, charging requirements, service ranges, the actual conditions of power distribution network construction such as line outlet intervals, line lengths and the like. The site selection mode without the alternative sites needs to estimate the number of the electric vehicles according to the regional electric vehicle storage quantity, randomly generate the charging station positions, and finally determine the optimal site according to the regional electric vehicle flow, the charging station service range and the cost indexes. The existing site selection mode related to the non-alternative site can ensure that the selected position is the optimal target in the area, but the condition that whether traffic is convenient and the site selection accords with the actual condition cannot be met at the same time when the charging site is planned.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to solve the problem that the mode without the alternative station address in the prior art can not simultaneously meet whether traffic is convenient or not and whether the address selection accords with the reality or not.
In order to solve the technical problems, the invention provides an electric vehicle charging station location and volume-fixing method, which comprises the following steps:
Step S1: taking the minimum total social annual cost as an objective function, and constructing an electric vehicle charging station planning model under preset constraint conditions;
Step S2: and setting n selected charging stations in the required station building area in advance, determining the number of charging piles corresponding to the n selected charging stations by solving the electric vehicle charging station planning model, and simultaneously selecting a plurality of charging stations from the n selected charging stations as the optimal charging station address to finish the electric vehicle charging station site selection and volume fixation.
Preferably, the formula of the minimum social annual total cost is:
；
wherein, Is the total cost of the society year,/>For the construction cost of charging station,/>For annual operating costs of charging stations,/>And the charging cost is the charging cost for the electric automobile user in the driving process.
Preferably, the construction cost of the charging stationThe formula is:
；
wherein, Is the discount rate; /(I)Is the depreciated year; /(I)A set of alternative charging stations; /(I)Is a step function; for charging station/> The number of the internal charging piles; /(I)The fixed cost generated for each newly built charging station; /(I)Price for a single charging pile; /(I)Is an equivalent investment coefficient related to the number of the charging piles;
Annual operating costs of the charging station The formula is:
；
wherein, The labor cost for each charging station; /(I)Annual maintenance cost for a single charging pile;
Charging cost of electric automobile user during driving The formula is:
；
wherein, Is a driving cost coefficient in the charging process; /(I)The trip deadline cost coefficient of all people in one city; /(I)For charging station/>A set of all charging demand points within the service range; /(I)For charging demand points/>The corresponding number of electric vehicles; /(I)Is the speed of an electric automobile; /(I)For charging demand points/>To charging station/>Is a distance of (3).
Preferably, the preset constraint condition includes a limitation constraint of queuing transit time of the charging equipment, and the formula is:
；
wherein, Maximum queuing time acceptable for electric automobile user,/>For charging station/>Vehicle queuing waiting time expectations,/>The size is determined by the M/M/s model of queuing theory, and the formula is:
；
wherein, For/>Service intensity of each charging pile,/>For/>Probability of idle of each charging pile,/>For the number of electric vehicles arriving at a charging station per unit time following poisson distribution,/>For the average service rate of charging piles,/>For charging station/>Number of internal charging piles,/>To iterate from zero to the number of charging piles minus a value of 1,/>In the form of a factorial operation symbol,For charging station/>Number of electric vehicles in service range,/>Charging period for electric automobile,/>And charging the charging pile bicycle for a charging time.
Preferably, the preset constraint condition includes a limit constraint of the number of use of the charging piles, and the formula is:
；
wherein, For the upper limit of the number of charging piles in each charging station,/>For charging station/>Number of internally charged piles.
Preferably, the preset constraint condition includes a power constraint, and the formula is:
；
wherein, Charging power for charging pile single machine,/>Minimum charging power of electric automobile,/>For charging station/>Number of internal charging piles,/>Is a set of alternative charging stations.
Preferably, in step S2, n selected charging stations are set in advance in the required station building area, the number of charging piles corresponding to the n selected charging stations is determined by solving the electric vehicle charging station planning model, and meanwhile, a plurality of charging stations are selected from the n selected charging stations as the best charging station address, so that the electric vehicle charging station site selection and volume determination are completed, and the method comprises the following steps:
S21: dividing a required station building area into a plurality of subareas, taking a geometric center point of each subarea as a charging demand point, concentrating the number of electric vehicles in each subarea at the charging demand point, and setting n selected charging stations in the required station building area according to the position of the charging demand point of each subarea;
S22: selecting the number of charging stations to be generated from n selected charging stations; at the same time, the iteration times are set through a genetic algorithm Generating an initial population of the number of charging piles in n alternative charging stations，/>For charging station/>The number of the internal charging piles;
S23: confirming whether each selected charging station is effective according to the number of charging piles corresponding to each alternative charging station, if so Indicating that the charging peg is installed at the selected charging station, the selected charging station is valid; if/>Indicating that the charging post is not installed at the selected charging station, and the selected charging station is not effective;
S24: drawing a Voronoi diagram according to the coordinates of each effective selected charging station, forming each Voronoi diagram subarea by each effective selected charging station, and determining the number of electric vehicles in the Voronoi diagram subarea corresponding to each effective selected charging station Simultaneously determining the distance/>, of the charging demand point to the selected charging station；
S25: according to the number of the electric automobilesSum distance/>Calculating the social annual total cost/>, of all valid selected charging stationsWill social annual total cost/>As the fitness value of the genetic algorithm, the number/>, of the current charging piles is recordedAnd the current fitness value/>；
S26: judging the stop condition if the genetic algorithm reaches the maximum iteration number or the current fitness valueThe change rate is smaller than the set value, and the number/>, of the current charging piles is outputAnd an optimal charging station address for each charging station to be generated; otherwise, set the iteration number/>And pair/>Performing selection, crossover, and mutation operations to generate a new population, comprising:
If it is Middle/>The number of (2) is greater than a preset number and the iteration number/>When judging the current fitness value/>And last fitness value/>If the ratio of (2) is greater than 0.3, if the ratio is greater than 0.3, then pair/>In/>Ordering from big to small while simultaneously ordering/>All/>Averaging to obtain an average value/>And will/>Least 8/>Are all replaced by average values/>Then, selecting, crossing and mutating operations are carried out to generate a new group; if the ratio is not greater than 0.3, directly executing the operations of selection, crossing and mutation to generate a new group; returning to the step S23 until the stopping condition is met;
If it is Middle/>The number of (2) is not greater than a preset number and the iteration number/>When judging the current fitness value/>And last fitness value/>If the ratio of (2) is less than 0.15, if the ratio is less than 0.15, then pair/>In (a) and (b)Ordering from big to small, and simultaneously ordering the smallest 50% number/>, of the ordering resultsAveraging to obtain an average valueAnd will/>Maximum 3/>Are all replaced by average values/>Then, selecting, crossing and mutating operations are carried out to generate a new group; if the ratio is not less than 0.15, directly executing the operations of selection, crossing and mutation to generate a new group; returning to the step S23 until the stopping condition is met;
And finally, obtaining the optimal charging station address and the number of charging piles of each charging station to be generated, and finishing the site and volume selection of the electric vehicle charging stations.
In order to solve the technical problems, the invention provides an electric vehicle charging station location and volume-fixing system, comprising:
the construction module comprises: the method comprises the steps of using the minimum social annual total cost as an objective function, and constructing an electric vehicle charging station planning model under preset constraint conditions;
And (3) an address and volume selection module: the electric vehicle charging station planning model is used for setting n selected charging stations in a required station building area in advance, determining the number of charging piles corresponding to the n selected charging stations by solving the electric vehicle charging station planning model, and simultaneously selecting a plurality of charging stations from the n selected charging stations as optimal charging station addresses to finish the site selection and volume fixation of the electric vehicle charging stations.
In order to solve the technical problems, the invention provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the steps of the electric vehicle charging station location and volume-determining method are realized when the processor executes the computer program.
To solve the above technical problem, the present invention provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for locating and sizing an electric vehicle charging station as described above.
Compared with the prior art, the technical scheme of the invention has the following advantages:
according to the invention, n selected charging stations are arranged in a station building area in advance, the number of charging piles in each selected charging station is determined through a genetic algorithm, a plurality of charging stations are selected from the n selected charging stations as optimal charging station addresses, and the electric vehicle charging station site selection and volume fixation are completed;
According to the invention, when the electric vehicle charging station planning model is solved to determine the optimal station addresses and the number of charging piles of the selected charging stations, the genetic algorithm is optimized, and when the number of the charging stations is large, the iteration speed of the genetic algorithm can be increased, and the calculated amount is reduced; when the number of the charging stations is small, the iteration speed of the genetic algorithm can be reduced, so that the calculation result is more in accordance with the actual requirement, and the number of charging piles in each selected charging station and the optimal station address of each selected charging station can be more in accordance with the actual condition requirement;
The charging station has more reasonable site selection position, can be distributed at the road junction, and is convenient for the electric automobile to travel to the site for charging;
the method is simple, reliable and easy to popularize, and the cost is relatively low.
Drawings
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of dividing sub-areas in a desired building area in an embodiment of the present invention;
FIG. 3 is a graph comparing social annual total cost of different numbers of charging stations in an embodiment of the invention;
FIG. 4 is a diagram of a preset charging station planning result in an embodiment of the present invention;
fig. 5 is a diagram of a result of planning a charging station without presetting in an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.
Example 1
Referring to fig. 1, the invention relates to an electric vehicle charging station location and volume-determining method, which comprises the following steps:
Step S1: taking the minimum total social annual cost as an objective function, and constructing an electric vehicle charging station planning model under preset constraint conditions;
Step S2: and setting n selected charging stations in the required station building area in advance, determining the number of charging piles corresponding to the n selected charging stations by solving the electric vehicle charging station planning model, and simultaneously selecting a plurality of charging stations from the n selected charging stations as the optimal charging station address to finish the electric vehicle charging station site selection and volume fixation.
The present embodiment is described in detail below:
The electric vehicle charging station planning model decision variable of the embodiment is the number of charging piles of each electric vehicle charging station, the number of charging piles of the charging station is set to be an integer greater than or equal to zero, the charging stations are constructed at the station when the number of the charging piles is greater than zero, and the charging stations are not constructed at the station when the number of the charging piles is equal to zero. The electric vehicle charging station planning model takes the minimum investment operation cost of a planning operator and the charging cost of a user in the driving process as an objective function, and comprises the investment construction cost of a charging station, the annual operation cost of the charging station and the charging cost of the electric vehicle user in the driving process. The operator invests in operating costs including construction costs for charging stations Annual operating fee with charging station/>The user aspect is the charging cost/>, in the driving process, of the electric automobile userConstitutes the total cost of the society years/>. The specific objective function is as follows:
；
In the method, in the process of the invention, Is the total cost of the society year,/>For the construction cost of charging station,/>For annual operating costs of charging stations,/>And the charging cost is the charging cost for the electric automobile user in the driving process.
(1) Annual construction costs of charging station
；
In the method, in the process of the invention,Is the discount rate; /(I)Is the depreciated year; /(I)A set of alternative charging stations; /(I)Is a step function, and when the parameters in brackets are negative, the function value is-1; when the parameter in brackets is 0, the function value is 0; when the parameters in brackets are positive numbers, the function value is 1; /(I)For charging station/>The number of the internal charging piles; /(I)The fixed cost generated for each newly built charging station; /(I)Price for a single charging pile; /(I)Is an equivalent investment coefficient related to the number of the charging piles, and comprises the capacity of a distribution transformer, the occupied area, cables and the like.
(2) Annual operating costs of charging stations
；
In the method, in the process of the invention,The labor cost for each charging station; /(I)Annual maintenance costs for a single charging pile.
(3) Charging cost of electric automobile user during running
；
In the method, in the process of the invention,Is a driving cost coefficient in the charging process; /(I)The trip deadline cost coefficient of all people in one city; /(I)For charging station/>A set of all charging demand points within a service range (the area divided by the Voronoi diagram is the service range of the charging station); /(I)For charging demand points/>The corresponding number of electric vehicles; /(I)Is the speed of an electric automobile; /(I)For charging demand points/>To charging station/>Is a distance of (3).
Constraint conditions
The constraint conditions are reasonably selected to enable the planning result to be more in line with the actual situation, and the constraint conditions provided by the embodiment mainly comprise: limiting constraint on queuing pass time of the charging equipment, limiting constraint on the number of use of the charging equipment piles and power constraint. The specific constraint conditions are as follows:
(1) Queuing time constraints for each charging station
；
In the method, in the process of the invention,Maximum queuing time acceptable for electric automobile user,/>For charging station/>Vehicle queuing waiting time expectations,/>The size is determined by the queuing theory model. According to the M/M/s model of queuing theory, the electric automobile queuing time expects/>The method comprises the following steps:
；
In the method, in the process of the invention, For/>Service intensity of each charging pile,/>For/>Probability of idle of each charging pile,/>For the number of electric vehicles arriving at a charging station per unit time following poisson distribution,/>For the average service rate of charging piles,/>To iterate from zero to the number of charging piles minus a value of 1,/>Is a factorial operation symbol,/>For charging station/>Number of electric vehicles in service range,/>Charging period for electric automobile,/>And charging the charging pile bicycle for a charging time.
(2) And (3) constraining the number of charging piles:
；
In the method, in the process of the invention, An upper limit is charged for the number of piles in each charging station.
(3) Power constraint
；
In the method, in the process of the invention,For the single charging power of the charging pile, the total charging pile power meets the charging load requirement,/>The minimum charging power of the electric vehicle (specifically, the minimum charging power of the electric vehicle in a sub-region divided as described below).
Model solving
1 Genetic algorithm
The genetic algorithm utilizes a computer to simulate operation in a mathematical mode, and converts the electric vehicle charging station planning solving process into a chromosome gene-like crossing and mutation process. The genetic algorithm only needs to evaluate individuals through the adaptation value, the adaptation function is not subject to continuous micro constraint, the definition domain of the adaptation function can be set at will, and the adaptation function is very suitable for the electric vehicle charging station planning model established by the embodiment.
2Voronoi diagram and determination of initial site of charging station
The Voronoi diagram has no obvious physical distinction of n three-dimensional points on the same plane, and the n three-dimensional points are respectively divided into a plane according to a principle of closest approach, and each point has close physical association with a nearest area. After the location of the service plan is preliminarily determined in this embodiment, the corresponding service requirement range locations of the respective charging stations may be divided in detail through its Voronoi diagram. The present embodiment utilizes Voronoi diagrams to determine the most preferred address of the charging station.
3 Solving procedure
S21: dividing a required station building area into a plurality of subareas, taking a geometric center point of each subarea as a charging demand point, concentrating the number of electric vehicles in each subarea at the charging demand point, and setting n selected charging stations in the required station building area according to the position of the charging demand point of each subarea;
In this embodiment, the required station building area is divided into a plurality of sub-areas (for example, fig. 2 is a block diagram of 31 sub-areas) firstly, and the purpose of dividing the sub-areas is to avoid places such as rivers, mountains and the like which are not suitable for building charging stations, so that the address selection of the selected charging stations is more reasonable. Taking the geometric center point of each sub-area as a point for centralized charging starting from the electric automobile, and in short, taking the geometric center point of each sub-area as a charging demand point.
The n selected charging stations in this embodiment are set according to the distance between the charging demand points, and are generally set at the center positions close to the charging demand points, that is, the charging demand points where the selected charging stations reach the surroundings are guaranteed to be nearest.
S22: selecting the number of charging stations to be generated from n selected charging stations; at the same time, the iteration times are set through a genetic algorithmGenerating an initial population of the number of charging piles in n alternative charging stations；
S23: confirming whether each selected charging station is effective according to the number of charging piles corresponding to each alternative charging station, if soIndicating that the charging peg is installed at the selected charging station, the selected charging station is valid; if/>Indicating that the charging post is not installed at the selected charging station, and the selected charging station is not effective; it should be noted that the effective number of charging stations is the number of charging stations to be generated;
s24: drawing a Voronoi diagram according to the coordinates of each effective selected charging station, forming each effective selected charging station into a respective Voronoi diagram subarea (namely a charging station service range), and determining the number of electric vehicles in the Voronoi diagram subarea corresponding to each effective selected charging station Simultaneously determining the distance/>, of the charging demand point to the selected charging station；
S25: according to the number of the electric automobilesSum distance/>Calculating the social annual total cost/>, of all valid selected charging stationsWill social annual total cost/>As the fitness value of the genetic algorithm, the number/>, of the current charging piles is recordedAnd the current fitness value/>；
It should be noted that the above-mentioned electric automobile user charges cost during drivingIs the running cost of a vehicle, and calculates the total social annual cost/>In this case, it is necessary to divide the number of electric vehicles in each Voronoi diagram region/>Multiplied byObtaining the charge cost in the driving process of all vehicles in each Voronoi map subarea, and finally adding the charge cost in the driving process of all vehicles in each Voronoi map subarea to obtain the total/>。
S26: judging the stop condition if the genetic algorithm reaches the maximum iteration number or the current fitness valueThe change rate is smaller than the set value, and the number/>, of the current charging piles is outputAnd an optimal charging station address for each charging station to be generated; otherwise, set the iteration number/>And pair/>Performing selection, crossover, and mutation operations to generate a new population, comprising:
If it is Middle/>The number of (a) is greater than a preset number (e.g. 100) and the number of iterations/>When judging the current fitness value/>And last fitness value/>If the ratio of (2) is greater than 0.3, if the ratio is greater than 0.3, then pair/>In/>Ordering from big to small while simultaneously ordering/>All/>Averaging to obtain an average value/>And will/>Least 8/>Are all replaced by average values/>Then, selecting, crossing and mutating operations are carried out to generate a new group; if the ratio is not greater than 0.3, directly executing the operations of selection, crossing and mutation to generate a new group; returning to the step S23 until the stopping condition is met;
If it is Middle/>The number of (a) is not more than a preset number (e.g. 100) and the number of iterations/>When judging the current fitness value/>And last fitness value/>If the ratio of (2) is less than 0.15, if the ratio is less than 0.15, thenIn/>Ordering from big to small, and simultaneously ordering the smallest 50% number/>, of the ordering resultsAveraging to obtain an average value/>And will/>Maximum 3/>Are all replaced by average values/>Then, selecting, crossing and mutating operations are carried out to generate a new group; if the ratio is not less than 0.15, directly executing the operations of selection, crossing and mutation to generate a new group; returning to the step S23 until the stopping condition is met;
In the iterative process of the genetic algorithm, in addition to the number of charging piles corresponding to each charging station, how to select the optimal charging station address of the charging station to be generated from n selected charging stations needs to consider the charging demand point To charging station/>Distance/>Based on/>Consider the charge cost/>, in the driving process of the electric automobile userBased on/>Consider fitness value/>According to fitness value/>Finding the optimal charging station address of each charging station to be generated;
And finally, obtaining the optimal charging station address and the number of charging piles of each charging station to be generated through iteration of a genetic algorithm, and finishing the site and volume selection of the electric vehicle charging stations. Of course, the current fitness value can be finally output To make a cost comparison.
Calculation case analysis
In order to verify the effectiveness and the universality of the charging station planning model established in the embodiment, simulation analysis is carried out on a specific area of a certain city and a certain area, and the optimal position of charging station construction and the optimal number of charging piles configured in the charging station are determined.
Description of the basic situation of the planning area
The simulation calculation of a specific area of a certain city is shown in fig. 2, the specific area is intercepted from map software according to a certain proportion, and the map labeling information shows that the specific area comprises a residential area, a commercial area, an industrial area and a scenic area, wherein the residential area and the commercial area are mainly, and the type of charging station facing private vehicles is considered when the charging station is planned and built. The area is 3.8km long and 3.3km wide, and the area is 12.54558 square kilometers. In this embodiment, the specific area is artificially divided into 31 sub-areas, the positions and serial numbers of charging demand points of the sub-areas are marked in fig. 2, the geometric center of each sub-area is represented by a dot, and for convenience, the dot in each sub-area is regarded as a collecting point of all electric vehicles in the sub-area, and the dot is regarded as a starting point for the electric vehicles to be charged in a concentrated manner.
2 Basic parameters
And the charging power of the charging pile is constantly 60kW, and the charging mode is used for charging the electric automobile by the user for saving time. The battery capacity of the electric automobile is different according to different factors such as brands, places of production and the like, and is generally between 15 kWh and 60kWh, and the embodiment assumes that the battery capacity of the electric automobile to be charged is 40kWh, and the electric automobile user reaches a charging station according to the poisson distribution principle.
According to the statistical data, the daily electricity consumption of a certain region is 82990MWh, the total storage quantity of electric vehicles in the certain region is 8457, the coordinates of each charging demand point and the power load are shown in a table 1, and the coordinates of each alternative station are shown in a table 2. Discount rate0.08, Depreciation years/>For 20 years, the fixed cost of newly building a charging station in each period is 100 ten thousand yuan, the charging pile is 10 ten thousand yuan/station, and the equivalent investment coefficient/>For 3 ten thousand yuan/station, the labor cost of each charging station/>The average running cost coefficient in the charging process is 25 yuan for 4 ten thousand yuan/year, the average running speed of the electric automobile is 60km/h respectively, the charging time of the maximum queuing is generally 0.25h, the charging period of the electric automobile is generally 48h, the upper limit of the number of charging piles of each charging station is generally 20, the charging power of a single charging pile on a small automobile is generally 60kW, and the minimum charging power/>Is 1200kW.
Table 1 demand point coordinates and Power load Meter
Table 2 alternative site coordinate tables
3 Example result analysis
(1) Cost comparison for different numbers of charging stations
The number of charging station construction directly affects the cost of charging station construction, thereby indirectly affecting the total cost of social years, and in order to verify the influence of the number of charging station construction on the model, the embodiment respectively simulates 4 to 8 charging stations of the specific area planning construction.
And respectively calculating construction costs of corresponding charging stations when 4 to 8 charging stations are planned in the specific area by a genetic algorithm and a Voronoi diagram joint solving method, wherein social year assembly cost pairs of charging stations with different numbers are shown in figure 3.
As can be seen from fig. 3, the total social annual total cost is minimum, 1854.3 ten thousand yuan, when 6 charging stations are built in the specific area. The total social annual cost is affected by three parts of the construction cost of the charging station, the annual running cost of the charging station and the charging cost of the electric automobile user in the driving process. When less than 6 charging stations are built, the charging cost of the electric vehicle user during running can be reduced along with the increase of the number of the charging stations, so that the total cost of the society year is reduced along with the increase of the number of the charging stations. However, when the number of charging stations is greater than 6, the charging cost does not change much while the user is traveling, but the charging station construction cost increases greatly, resulting in an increase in the total cost of the social year with an increase in the number of charging stations. In summary, the construction of 6 charging stations in this specific area is optimal.
(2) Comparing the results of the preset site with the results of the non-preset site
The charging station planning model without the preset station directly optimizes the regional construction station, and does not pre-screen the charging station construction land. In order to compare the influence of the charging station selected from a plurality of alternative sites on the total cost of the social year in the model provided by the embodiment, the preset site model and the non-preset site model are respectively compared and analyzed. Both models are designed to construct 6 charging stations, and the preset charging station planning results are shown in table 3. The preset charging station planning result is shown in fig. 4, the rectangle in fig. 4 represents the selected charging stations, 10 selected charging stations in total, and finally 6 charging stations are screened, the numbers 1 to 6 in fig. 4 represent the number 1 to 6 charging stations respectively, and the numbers in each subarea represent the charging stations with corresponding numbers.
Table 3 preset charging station plan results
The results of the charging station planning model without presetting are shown in table 4. The result of not presetting a charging station planning model is shown in fig. 5, triangles in fig. 5 represent charging station positions obtained by an algorithm, 6 charging stations are taken as a total, numbers 1 to 6 in fig. 5 represent charging stations 1 to 6 respectively, and numbers in each subarea represent charging stations with corresponding numbers.
Table 4 does not preset the charging station planning results
As can be seen from a comparison of tables 3 and 4, the total cost of not preset charging station programming is lower than the total cost of preset charging station programming. The number of charging stations which are not preset is 3 less than the number of the total charging piles of the preset charging stations, the annual construction cost is 215 ten thousand yuan less, the annual operation cost is 5 ten thousand yuan less, the charging cost during the running process of a user is 1.9 ten thousand yuan more, and the average cost of a general society is 218.5 ten thousand yuan less. Because when the charging stations are not preset, the demand stations in the planning area can be optimally divided, and the minimum number of the charging piles to be built is determined, so that the construction cost is reduced. After the charging stations are preset, the limitation of the charging stations is that the division of the charging demand stations is affected, so that more charging piles are generated, and the higher social average cost is increased.
As can be seen by comparing fig. 4 and fig. 5, after the charging station is preset, the planned charging station can be strictly put on the preset charging station, and the construction land is not restricted in the construction process of the charging station. The charging stations are not preset, the charging stations are randomly allocated by the planning model, and the planned charging stations can be located in a business center or in the center of roads and rivers, so that the actual charging station construction is not facilitated.
It is not difficult to find that although the cost of not presetting the charging station may be slightly lower than the method of presetting the charging station of the present embodiment because not presetting the charging station is to find global optimum, the location of the charging station found by the method of not presetting the charging station is often not reasonable (e.g., the location may be located in the center of roads and rivers). The method of the embodiment is locally optimal, is comprehensively superior to a method without presetting a charging station, has more reasonable site selection positions, can be distributed at a road junction (convenient for the electric vehicle to travel to the site for charging), and can effectively control the cost.
Example two
The embodiment provides an electric automobile charging station site selection constant volume system, includes:
the construction module comprises: the method comprises the steps of using the minimum social annual total cost as an objective function, and constructing an electric vehicle charging station planning model under preset constraint conditions;
And (3) an address and volume selection module: the electric vehicle charging station planning model is used for setting n selected charging stations in a required station building area in advance, determining the number of charging piles corresponding to the n selected charging stations by solving the electric vehicle charging station planning model, and simultaneously selecting a plurality of charging stations from the n selected charging stations as optimal charging station addresses to finish the site selection and volume fixation of the electric vehicle charging stations.
Example III
The embodiment provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the electric vehicle charging station location and volume-fixing method in the embodiment when executing the computer program.
Example IV
The present embodiment provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps of the electric vehicle charging station location and sizing method of embodiment one.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (4)
1. An electric vehicle charging station locating and sizing method is characterized in that: comprising the following steps:
Step S1: taking the minimum total social annual cost as an objective function, and constructing an electric vehicle charging station planning model under preset constraint conditions;
Step S2: setting n selected charging stations in a station building area in advance, determining the number of charging piles corresponding to the n selected charging stations by solving the electric vehicle charging station planning model, and simultaneously selecting a plurality of charging stations from the n selected charging stations as optimal charging station addresses to finish the site selection and volume fixation of the electric vehicle charging stations;
The formula of the minimum social annual total cost is as follows:
；
wherein, Is the total cost of the society year,/>For the construction cost of charging station,/>For the annual operating costs of the charging station,Charging cost for the electric automobile user in the driving process;
Construction cost of the charging station The formula is:
；
wherein, Is the discount rate; /(I)Is the depreciated year; /(I)A set of alternative charging stations; /(I)Is a step function; /(I)For charging station/>The number of the internal charging piles; /(I)The fixed cost generated for each newly built charging station; /(I)Price for a single charging pile; /(I)Is an equivalent investment coefficient related to the number of the charging piles;
Annual operating costs of the charging station The formula is:
；
wherein, The labor cost for each charging station; /(I)Annual maintenance cost for a single charging pile;
Charging cost of electric automobile user during driving The formula is:
；
wherein, Is a driving cost coefficient in the charging process; /(I)The trip deadline cost coefficient of all people in one city; /(I)For charging station/>A set of all charging demand points within the service range; /(I)For charging demand points/>The corresponding number of electric vehicles; /(I)Is the speed of an electric automobile; /(I)For charging demand points/>To charging station/>Is a distance of (2);
The preset constraint condition comprises a limitation constraint of queuing passing time of the charging equipment, and the formula is as follows:
；
wherein, Maximum queuing time acceptable for electric automobile user,/>For charging station/>Vehicle queuing waiting time expectations,/>The size is determined by the M/M/s model of queuing theory, and the formula is:
；
wherein, For/>Service intensity of each charging pile,/>For/>Probability of idle of each charging pile,/>For the number of electric vehicles arriving at a charging station per unit time following poisson distribution,/>For the average service rate of charging piles,/>For charging station/>Number of internal charging piles,/>To iterate from zero to the number of charging piles minus a value of 1,/>Is a factorial operation symbol,/>For charging station/>Number of electric vehicles in service range,/>Charging period for electric automobile,/>Charging time for the charging pile bicycle;
The preset constraint conditions comprise the limit constraint of the number of the charging piles, and the formula is as follows:
；
wherein, For the upper limit of the number of charging piles in each charging station,/>For charging station/>The number of the internal charging piles;
The preset constraint conditions comprise power constraint, and the formula is as follows:
；
wherein, Charging power for charging pile single machine,/>Minimum charging power of electric automobile,/>For charging station/>Number of internal charging piles,/>A set of alternative charging stations;
In step S2, n selected charging stations are set in advance in the required station building area, the number of charging piles corresponding to the n selected charging stations is determined by solving the electric vehicle charging station planning model, and meanwhile, a plurality of charging stations are selected from the n selected charging stations as the best charging station address, so that the electric vehicle charging station site selection and volume determination are completed, and the method comprises the following steps:
S21: dividing a required station building area into a plurality of subareas, taking a geometric center point of each subarea as a charging demand point, concentrating the number of electric vehicles in each subarea at the charging demand point, and setting n selected charging stations in the required station building area according to the position of the charging demand point of each subarea;
S22: selecting the number of charging stations to be generated from n selected charging stations; at the same time, the iteration times are set through a genetic algorithm Generating an initial population of the number of charging piles in n alternative charging stations，/>For charging station/>The number of the internal charging piles;
S23: confirming whether each selected charging station is effective according to the number of charging piles corresponding to each alternative charging station, if so Indicating that the charging peg is installed at the selected charging station, the selected charging station is valid; if/>Indicating that the charging post is not installed at the selected charging station, and the selected charging station is not effective;
S24: drawing a Voronoi diagram according to the coordinates of each effective selected charging station, forming each Voronoi diagram subarea by each effective selected charging station, and determining the number of electric vehicles in the Voronoi diagram subarea corresponding to each effective selected charging station Simultaneously determining the distance/>, of the charging demand point to the selected charging station；
S25: according to the number of the electric automobilesSum distance/>Calculating the social annual total cost/>, of all valid selected charging stationsWill social annual total cost/>As the fitness value of the genetic algorithm, the number/>, of the current charging piles is recordedAnd the current fitness value/>；
S26: judging the stop condition if the genetic algorithm reaches the maximum iteration number or the current fitness valueThe change rate is smaller than the set value, and the number/>, of the current charging piles is outputAnd an optimal charging station address for each charging station to be generated; otherwise, set the iteration number/>And pair/>Performing selection, crossover, and mutation operations to generate a new population, comprising:
If it is Middle/>The number of (2) is greater than a preset number and the iteration number/>When judging the current fitness value/>And last fitness value/>If the ratio of (2) is greater than 0.3, if the ratio is greater than 0.3, then pair/>In/>Ordering from big to small while simultaneously ordering/>All/>Averaging to obtain an average value/>And will/>Least 8/>Are all replaced by average values/>Then, selecting, crossing and mutating operations are carried out to generate a new group; if the ratio is not greater than 0.3, directly executing the operations of selection, crossing and mutation to generate a new group; returning to the step S23 until the stopping condition is met;
If it is Middle/>The number of (2) is not greater than a preset number and the iteration number/>When judging the current fitness valueAnd last fitness value/>If the ratio of (2) is less than 0.15, if the ratio is less than 0.15, then pair/>In/>Ordering from big to small, and simultaneously ordering the smallest 50% number/>, of the ordering resultsAveraging to obtain an average value/>And will/>Maximum 3/>Are all replaced by average values/>Then, selecting, crossing and mutating operations are carried out to generate a new group; if the ratio is not less than 0.15, directly executing the operations of selection, crossing and mutation to generate a new group; returning to the step S23 until the stopping condition is met;
And finally, obtaining the optimal charging station address and the number of charging piles of each charging station to be generated, and finishing the site and volume selection of the electric vehicle charging stations.
2. An electric automobile charging station site selection constant volume system, characterized by: comprising the following steps:
the construction module comprises: the method comprises the steps of using the minimum social annual total cost as an objective function, and constructing an electric vehicle charging station planning model under preset constraint conditions;
And (3) an address and volume selection module: the method comprises the steps of setting n selected charging stations in a required station building area in advance, determining the number of charging piles corresponding to the n selected charging stations by solving an electric vehicle charging station planning model, and simultaneously selecting a plurality of charging stations from the n selected charging stations as optimal charging station addresses to finish the site selection and volume fixation of the electric vehicle charging stations;
The formula of the minimum social annual total cost is as follows:
；
wherein, Is the total cost of the society year,/>For the construction cost of charging station,/>For the annual operating costs of the charging station,Charging cost for the electric automobile user in the driving process;
Construction cost of the charging station The formula is:
；
wherein, Is the discount rate; /(I)Is the depreciated year; /(I)A set of alternative charging stations; /(I)Is a step function; /(I)For charging station/>The number of the internal charging piles; /(I)The fixed cost generated for each newly built charging station; /(I)Price for a single charging pile; /(I)Is an equivalent investment coefficient related to the number of the charging piles;
Annual operating costs of the charging station The formula is:
；
wherein, The labor cost for each charging station; /(I)Annual maintenance cost for a single charging pile;
Charging cost of electric automobile user during driving The formula is:
；
wherein, Is a driving cost coefficient in the charging process; /(I)The trip deadline cost coefficient of all people in one city; /(I)For charging station/>A set of all charging demand points within the service range; /(I)For charging demand points/>The corresponding number of electric vehicles; /(I)Is the speed of an electric automobile; /(I)For charging demand points/>To charging station/>Is a distance of (2);
The preset constraint condition comprises a limitation constraint of queuing passing time of the charging equipment, and the formula is as follows:
；
wherein, Maximum queuing time acceptable for electric automobile user,/>For charging station/>Vehicle queuing waiting time expectations,/>The size is determined by the M/M/s model of queuing theory, and the formula is:
；
wherein, For/>Service intensity of each charging pile,/>For/>Probability of idle of each charging pile,/>For the number of electric vehicles arriving at a charging station per unit time following poisson distribution,/>For the average service rate of charging piles,/>For charging station/>Number of internal charging piles,/>To iterate from zero to the number of charging piles minus a value of 1,/>Is a factorial operation symbol,/>For charging station/>Number of electric vehicles in service range,/>Charging period for electric automobile,/>Charging time for the charging pile bicycle;
The preset constraint conditions comprise the limit constraint of the number of the charging piles, and the formula is as follows:
；
wherein, For the upper limit of the number of charging piles in each charging station,/>For charging station/>The number of the internal charging piles;
The preset constraint conditions comprise power constraint, and the formula is as follows:
；
wherein, Charging power for charging pile single machine,/>Minimum charging power of electric automobile,/>For charging station/>Number of internal charging piles,/>A set of alternative charging stations;
The utility model provides a charging station, including electric automobile charging station planning model, the locating and sizing module, set up n in advance in the required district of building station in the locating and sizing module, through solving electric automobile charging station planning model in order to confirm the corresponding electric pile quantity of n selected charging stations, simultaneously select a plurality of charging stations as best charging station address from n selected charging stations, accomplish electric automobile charging station locating and sizing, include:
S21: dividing a required station building area into a plurality of subareas, taking a geometric center point of each subarea as a charging demand point, concentrating the number of electric vehicles in each subarea at the charging demand point, and setting n selected charging stations in the required station building area according to the position of the charging demand point of each subarea;
S22: selecting the number of charging stations to be generated from n selected charging stations; at the same time, the iteration times are set through a genetic algorithm Generating an initial population of the number of charging piles in n alternative charging stations，/>For charging station/>The number of the internal charging piles;
S23: confirming whether each selected charging station is effective according to the number of charging piles corresponding to each alternative charging station, if so Indicating that the charging peg is installed at the selected charging station, the selected charging station is valid; if/>Indicating that the charging post is not installed at the selected charging station, and the selected charging station is not effective;
S24: drawing a Voronoi diagram according to the coordinates of each effective selected charging station, forming each Voronoi diagram subarea by each effective selected charging station, and determining the number of electric vehicles in the Voronoi diagram subarea corresponding to each effective selected charging station Simultaneously determining the distance/>, of the charging demand point to the selected charging station；
S25: according to the number of the electric automobilesSum distance/>Calculating the social annual total cost/>, of all valid selected charging stationsWill social annual total cost/>As the fitness value of the genetic algorithm, the number/>, of the current charging piles is recordedAnd the current fitness value/>；
S26: judging the stop condition if the genetic algorithm reaches the maximum iteration number or the current fitness valueThe change rate is smaller than the set value, and the number/>, of the current charging piles is outputAnd an optimal charging station address for each charging station to be generated; otherwise, set the iteration number/>And pair/>Performing selection, crossover, and mutation operations to generate a new population, comprising:
If it is Middle/>The number of (2) is greater than a preset number and the iteration number/>When judging the current fitness value/>And last fitness value/>If the ratio of (2) is greater than 0.3, if the ratio is greater than 0.3, then pair/>In/>Ordering from big to small while simultaneously ordering/>All/>Averaging to obtain an average value/>And will/>Least 8/>Are all replaced by average values/>Then, selecting, crossing and mutating operations are carried out to generate a new group; if the ratio is not greater than 0.3, directly executing the operations of selection, crossing and mutation to generate a new group; returning to the step S23 until the stopping condition is met;
If it is Middle/>The number of (2) is not greater than a preset number and the iteration number/>When judging the current fitness valueAnd last fitness value/>If the ratio of (2) is less than 0.15, if the ratio is less than 0.15, then pair/>In/>Ordering from big to small, and simultaneously ordering the smallest 50% number/>, of the ordering resultsAveraging to obtain an average value/>And will/>Maximum 3/>Are all replaced by average values/>Then, selecting, crossing and mutating operations are carried out to generate a new group; if the ratio is not less than 0.15, directly executing the operations of selection, crossing and mutation to generate a new group; returning to the step S23 until the stopping condition is met;
And finally, obtaining the optimal charging station address and the number of charging piles of each charging station to be generated, and finishing the site and volume selection of the electric vehicle charging stations.
3. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the computer program, performs the steps of the electric vehicle charging station location and sizing method of claim 1.
4. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, performs the steps of the electric vehicle charging station location and sizing method of claim 1.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210237609A1 (en) * | 2019-03-29 | 2021-08-05 | Jiangsu University | A method to plan the optimal construction quantity and site selection scheme of electric vehicle charging stations |
| CN114297809A (en) * | 2021-12-20 | 2022-04-08 | 上海电机学院 | Electric vehicle charging station site selection and volume fixing method |
| US11584250B1 (en) * | 2022-01-13 | 2023-02-21 | Beta Air, Llc | Charging station for transferring power between an electric aircraft and a power grid |
| CN116307647A (en) * | 2023-05-24 | 2023-06-23 | 国网山西省电力公司太原供电公司 | Electric vehicle charging station site selection and volume determination optimization method and device and storage medium |
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210237609A1 (en) * | 2019-03-29 | 2021-08-05 | Jiangsu University | A method to plan the optimal construction quantity and site selection scheme of electric vehicle charging stations |
| CN114297809A (en) * | 2021-12-20 | 2022-04-08 | 上海电机学院 | Electric vehicle charging station site selection and volume fixing method |
| US11584250B1 (en) * | 2022-01-13 | 2023-02-21 | Beta Air, Llc | Charging station for transferring power between an electric aircraft and a power grid |
| CN116307647A (en) * | 2023-05-24 | 2023-06-23 | 国网山西省电力公司太原供电公司 | Electric vehicle charging station site selection and volume determination optimization method and device and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| TONGSHUO YANG 等: ""Research on the Optimization Strategy of Electric Vehicle Charging and Discharging Scheduling"", 《 2023 3RD POWER SYSTEM AND GREEN ENERGY CONFERENCE (PSGEC)》, 31 October 2023 (2023-10-31), pages 455 - 459 * |
| 肖白，等: ""含不同容量充电桩的电动汽车充电站选址定容优化方法"", 《电力自动化设备》, vol. 42, no. 10, 31 December 2022 (2022-12-31), pages 157 - 166 * |
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