CN117557069B - Charging pile address selection method and system, electronic equipment and storage medium - Google Patents
Charging pile address selection method and system, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a charging pile address selection method and system, electronic equipment and a storage medium, and belongs to the technical field of electric automobile charging, wherein the method comprises the following steps: acquiring the number of electric vehicles in a target area, existing charging pile data and electric vehicle parking distribution data in different time periods; determining the number range of charging piles to be expanded according to the number of electric vehicles in the target area and the existing charging pile data; determining a charging demand point set according to existing charging pile data and electric vehicle parking distribution data of different time periods, and establishing a charging pile candidate address set; establishing a charging pile site selection model by taking the minimum cost loss and the highest charging convenience as targets; and solving an objective function of the charging pile site selection model by adopting an intelligent optimization algorithm to obtain a charging pile site selection result. The invention can effectively select the number and the positions of the charging piles to be expanded based on the existing charging piles, thereby improving the rationality of the layout planning of the charging piles and the utilization rate of the charging piles.
Description
Technical Field
The invention belongs to the technical field of electric automobile charging, and particularly relates to a charging pile location method and system, electronic equipment and a storage medium.
Background
With the popularization of electric vehicles, the location of charging piles becomes an important problem. The site selection of the charging stake needs to take into account a number of factors including user demand, accessibility of the charging stake, etc. In order to better meet the charging requirements of users, reduce the construction cost of the charging pile, improve the utilization rate of the charging pile and adopt a scientific method for site selection. With the increasing amount of electric vehicles, the existing charging pile layout cannot meet the actual requirements.
In the prior art, the charging requirements of users are generally analyzed by utilizing big data, hot spot areas and parking lots for traveling of the users are found out by combining technologies such as a Geographic Information System (GIS) and the like, and the optimal position of a charging pile is determined. Or modeling the charging requirement of the user by using a mathematical model, and determining the optimal position of the charging pile through simulation and optimization. In recent years, by utilizing technologies such as artificial intelligence and the like, travel rules and charging demands of users are analyzed and predicted, so that the determination of the optimal position of a charging pile becomes a new trend. However, the existing site selection methods of the charging piles are easy to be interfered by various factors, the situation that partial charging piles are distributed in a target area is rarely considered, the problems of unreasonable layout planning of the charging piles and low utilization rate of the charging piles still exist,
disclosure of Invention
In view of the above, the invention provides a charging pile site selection method and system, electronic equipment and storage medium, which are used for solving the problem of unreasonable layout planning of the existing charging piles.
The invention discloses a charging pile site selection method, which comprises the following steps:
acquiring the number of electric vehicles in a target area, existing charging pile data and electric vehicle parking distribution data in different time periods;
determining the number range of charging piles to be expanded according to the number of electric vehicles in the target area and the existing charging pile data;
determining a charging demand point set according to existing charging pile data and electric vehicle parking distribution data of different time periods, and establishing a charging pile candidate address set;
based on the charging demand point set and the charging pile candidate address set, establishing a charging pile site selection model with the minimum cost loss and the highest charging convenience as targets;
and solving an objective function of the charging pile site selection model by adopting an intelligent optimization algorithm to obtain a charging pile site selection result.
On the basis of the above technical solution, preferably, the existing charging pile data includes existing charging pile base data and existing charging pile usage data;
the existing charging pile foundation data comprises the number of charging pilesN c Position distribution, charging power per charging pileThe existing charging pile usage data includes a utilization rate of each charging pile +.>Average charging speed->Average waiting time->Average use time->WhereinsThe number of the charging piles is given,s=1,2,…,N c 。
on the basis of the above technical solution, preferably, the determining the number range of the charging piles to be expanded according to the number of the electric vehicles in the target area and the existing charging pile data specifically includes:
estimating a minimum required charge amount Q of the target area based on the existing charging pile foundation data and the existing charging pile usage data 1 ；
According to the number of existing charging pilesN c Average waiting time per charging pileMaximum charge demand Q is estimated to electric automobile quantity in target area 2 ；
Charge amount Q according to minimum demand 1 Maximum charge demand Q 2 Determining the number of charging piles to be enlargedNumIs expressed as:
wherein,Numis an integer of the number of the times,in order to round up the operator,qfor the average amount of electricity used by each charging stake,，is the firstsThe power of each charging pile;k 1 、k 2 are all adjustment coefficients.
On the basis of the above aspect, preferably, the estimating the minimum required charge amount Q of the target area based on the existing charging pile foundation data and the existing charging pile usage data 1 The formula of (2) is:
；
said number of charging piles is based on the existing number of charging pilesN c Average waiting time per charging pileMaximum charge demand Q is estimated to electric automobile quantity in target area 2 The formula of (2) is:
；
wherein,N e as the number of electric vehicles in the target area,s=1,2,…,N c 。
on the basis of the above technical solution, preferably, the objective function of the charging pile site selection model is:
；
wherein the method comprises the steps ofFAs a function of the object to be processed,C 1 is the total construction cost loss of the charging pile,C 2 Is the total operation cost loss of the charging pile,S 1 Is a distance convenience index,S 2 As an index of the convenience of the time,w 1 、w 2 、w 3 、w 4 are all weight coefficients;
the constraint conditions of the objective function are:
；
wherein,Ifor the set of charging demand points,Jfor the set of charging pile candidate addresses,Pfor the existing set of charge pile position distributions,to the point of charge demandiWith charging pile candidate addressjThe distance between the two plates is set to be equal,d max for a preset maximum distance threshold, +.>For arbitrary charging pile candidate addressjWith any existing charging stake locationslThe distance between the two plates is set to be equal,D min a preset minimum distance threshold;Numthe number of the charging piles to be expanded;Representing candidate addresses at charging pilesjThe place is established with a charging pile->Representing non-charging pile candidate addressjThe place is established with a charging pile->Representing charging pile candidate addressesjCharging pile at position for charging pointiCharging->Representing charging pile candidate addressesjCharging pile at position without charging pointiAnd (5) charging.
On the basis of the above technical solution, preferably, the solving the objective function of the charging pile site selection model by using the intelligent optimization algorithm specifically includes:
selecting the smallest integer value in the number range of the charging piles to be expanded as the current number of the charging piles to be expanded；
Taking an objective function of the charging pile site selection model as an fitness function, and solving a global optimal solution and a corresponding fitness value of the charging pile site selection model by adopting an intelligent optimization algorithm;
if it isThe number of the charging piles which are required to be expanded at presentN u Adding 1 to the value of (1), and solving a global optimal solution and a corresponding fitness value of the charging pile site selection model by adopting an intelligent optimization algorithm again;
comparing different numbers of charging pilesN u Screening out the number of charging piles with minimum fitness value according to the fitness value of each global optimal solutionN u And the corresponding global bestAnd the optimal solution is used as a final charging pile site selection result.
Based on the above technical solution, preferably, the intelligent optimization algorithm adopts an improved raccoon optimization algorithm, and the process of solving the global optimal solution and the corresponding fitness value of the charging pile site selection model through the improved raccoon optimization algorithm includes:
initializing the population position, and the population scale isM；
Calculating the fitness of each individual by taking an objective function of the charging pile site selection model as a fitness function;
updating the location of each individual by setting a location update strategy, the location update strategy comprising an exploration phase and a development phase;
in the exploration phase, half individuals update positions in a random mode; the rest half of individuals introduce a Lewy flight strategy to update the position, and the position updating formula is as follows:
；
wherein t is the iteration number,position of the nth dimension of individual m at t-th, t+1-th iterations, respectively,/->，n=1,2,…,N u ，For the historic optimal position at the t-th iteration, F (·) is the fitness function, ++>For obeying parameters ofβIs of Lei Wei distribution, lei Lu>，For randomly generated positions in the solution space,I 0 =1 or 2;
in the development phase, a [0,1 ] is generated]Random number betweenδSine and cosine operators are introduced to simulate the escape strategy of the prey to update the position, and the position updating formula is as follows:
wherein the method comprises the steps ofRepresenting the random number between the generation of (0, 2 pi,)>；
Re-calculating the fitness value of each individual, and updating the historical optimal position;
and judging whether the iteration termination condition is met, if so, outputting a global optimal solution and a corresponding fitness value, and if not, repeating the processes of position updating and fitness value calculation until the iteration termination condition is met.
In a second aspect of the present invention, a charging pile site selection system is disclosed, the system comprising:
and a data acquisition module: the method comprises the steps of acquiring the number of electric vehicles in a target area, existing charging pile data and electric vehicle parking distribution data in different time periods;
a range estimation module: the method comprises the steps of determining the number range of charging piles to be expanded according to the number of electric vehicles in a target area and existing charging pile data;
and a model building module: the method comprises the steps of determining a charging demand point set according to existing charging pile data and electric vehicle parking distribution data of different time periods, and establishing a charging pile candidate address set; based on the charging demand point set and the charging pile candidate address set, establishing a charging pile site selection model with the minimum cost loss and the highest charging convenience as targets;
model solving module: and the method is used for solving an objective function of the charging pile site selection model by adopting an intelligent optimization algorithm to obtain a charging pile site selection result.
In a third aspect of the present invention, an electronic device is disclosed, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete communication with each other through the bus;
the memory stores program instructions executable by the processor which the processor invokes to implement the method according to the first aspect of the invention.
In a fourth aspect of the invention, a computer-readable storage medium is disclosed, storing computer instructions that cause a computer to implement the method according to the first aspect of the invention.
Compared with the prior art, the invention has the following beneficial effects:
1) According to the invention, the number range of the charging piles to be expanded is determined according to the number of electric vehicles in a target area and the existing charging pile data, a charging pile site selection model is established based on the charging demand point set and the charging pile candidate address set by taking the minimum cost loss and the highest charging convenience as targets, and the charging pile site selection result is obtained by solving.
2) According to the invention, the minimum required charge quantity of the target area is estimated according to the existing charging pile basic data and the existing charging pile use data, the maximum charging demand quantity is estimated according to the existing charging pile quantity, the average waiting time of each charging pile and the electric vehicle quantity of the target area, and finally the quantity range of the charging piles to be expanded is determined according to the minimum required charge quantity and the maximum charging demand quantity, so that the quantity of the charging piles to be expanded can be effectively contracted into a reasonable range, the charging demand can be met, blind expansion can be avoided, the objective function of the charging pile site selection model can be solved in the range, the optimal charging pile site selection result can be obtained rapidly, and the site selection efficiency is improved.
3) According to the invention, the objective function of the charging pile site selection model is established based on the total construction cost loss of the charging pile, the total operation cost loss of the charging pile, the distance convenience index and the time convenience index, the maximum distance constraint is added between the charging demand point and the charging pile candidate address, the minimum distance constraint is set between any charging pile candidate address and any existing charging pile position, and the service range and the distribution density of the charging pile can be more reasonably planned while the cost loss is minimum and the convenience is highest.
4) When the objective function of the charging pile site selection model is solved through the intelligent optimization algorithm, the fitness values of all global optimal solutions under different numbers of the charging piles are compared respectively, the optimal number of the charging piles and the corresponding positions are screened out to serve as a final charging pile site selection result, the relation between the expansion number and the positions of the charging piles is effectively balanced, and the reliability of the charging pile site selection is further improved.
5) According to the invention, an improved raccoon optimization algorithm is adopted to solve the objective function of the charging pile site selection model, and in the exploration stage, the position update is carried out based on the current optimal position and a random position and in combination with a Lewy flight strategy, so that the convergence rate is increased when a certain randomness is maintained, and the influence on the optimizing speed due to the strong randomness caused by carrying out the position update through the random position each time is avoided; in the exploration stage, sine and cosine operators are introduced to simulate the escaping strategy of the prey to update the position, so that the population diversity is increased, the position approaches to the current optimal position with a certain probability, the local search and the global search are effectively balanced, and the quality of the solution is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for selecting a charging pile according to the present invention;
fig. 2 is a flowchart for solving the charging pile site selection model according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Referring to fig. 1, the present invention provides a method for selecting a charging pile, which includes:
s1, acquiring the number of electric vehicles in a target area, existing charging pile data and electric vehicle parking distribution data of different time periods.
Generally, the location of the charging piles should take into account the number of electric vehicles in the target area to meet the charging needs of the users. When the positions of the charging piles are selected, the distribution condition of existing charging facilities needs to be considered, and resource waste caused by repeated arrangement of excessive charging piles in the same area is avoided.
The existing charging pile data reflects the current charging requirement condition of the electric vehicle in the target area to a certain extent. Under the condition that the number of the current electric vehicles is continuously increased, the problems of insufficient number or unreasonable position distribution of the charging pile facilities often exist, and the problems of waiting to charge in a queue of the electric vehicles, searching idle charging piles by part of the electric vehicles in a cross-range manner, uneven utilization rate of the charging piles and the like are presented. Therefore, the invention collects existing charging pile data to provide a data basis for the extension of the charging pile. The existing charging pile data comprises existing charging pile foundation data and existing charging pile usage data, wherein the existing charging pile foundation data comprises the number of charging pilesN c Position distribution setPEach charging pilesCharging power of (2)Existing charging pile usage data includes each charging pilesIs>Average charging speed->Average waiting time->Average use time->And the like,s=1,2,…,N c 。
the electric vehicles are generally distributed in parking lots or temporary parking areas near public parking lots, commercial areas, residential areas, public transportation hubs, office buildings, factories and the like, the charging piles are generally arranged near the positions, parking data of different periods of a target area can be analyzed according to the parking data of the areas, specific position information and traffic flow data of the electric vehicles can be acquired by combining vehicle positioning data and road cameras, and accordingly parking distribution data of the electric vehicles of different periods of the target area can be obtained through analysis, and basis is provided for setting the charging piles.
S2, determining the number range of the charging piles to be expanded according to the number of the electric vehicles in the target area and the existing charging pile data.
The existing charging pile data reflects the current charging demand, and the minimum required charging amount and the maximum charging demand of the target area are estimated according to the number of electric vehicles in the target area and the existing charging pile data, so that the range of the number of charging piles to be expanded is determined, and the problem that resources are wasted or the increasing charging demand cannot be met due to blind setting of the number of charging piles to be expanded is avoided.
The step S2 specifically comprises the following sub-steps:
s21, estimating the minimum required charge quantity Q of the target area according to the existing charging pile foundation data and the existing charging pile use data 1 Calculating the minimum required charge amount Q 1 The formula is:
wherein,N c for the number of existing charging piles,sthe number of the charging piles is given,s=1,2,…,N c 。for charging pilessIs>For charging pilessAverage charging speed, ±>For charging pilessThe average use time of the charging piles s is the average time spent for charging the electric automobile every day.
S22, according to the number of the existing charging pilesN c Average waiting time per charging pileMaximum charge demand Q is estimated to electric automobile quantity in target area 2 Calculating the maximum charge demand Q 2 The formula of (2) is:
wherein,charging pile for electric automobilesThe desired average waiting time is that which is required,N e the number of electric vehicles in the target area.
S23, charging quantity Q according to minimum requirement 1 Maximum charge demand Q 2 Determining the number of charging piles to be enlargedNumIs used in the range of (a),Numthe range expression of (2) is:
wherein,Numis an integer of the number of the times,to round up operatorsqFor the average daily power consumption of each charging pile,，is the firstsThe power of each charging pile;k 1 、k 2 are all adjustment coefficients.
According to the invention, the minimum required charge quantity of the target area is estimated according to the existing charging pile basic data and the existing charging pile use data, the maximum charging demand quantity is estimated according to the existing charging pile quantity, the average waiting time of each charging pile and the electric vehicle quantity of the target area, and finally the quantity range of the charging piles to be expanded is determined according to the minimum required charge quantity and the maximum charging demand quantity, so that the quantity of the charging piles to be expanded can be effectively contracted into a reasonable range, the charging demand can be met, blind expansion can be avoided, the objective function of the charging pile site selection model can be solved in the range, the optimal charging pile site selection result can be obtained rapidly, and the site selection efficiency is improved.
And S3, determining a charging demand point set according to existing charging pile data and electric vehicle parking distribution data of different time periods, and establishing a charging pile candidate address set.
Screening out parking points of which the parking time of the electric vehicle exceeds a preset time threshold according to the electric vehicle parking distribution data of different time periods, acquiring existing charging pile data of each parking point within a preset radius range, and merging the charging demand points positioned in the same preset radius range into one charging demand point through cluster analysis by taking the corresponding parking point as the charging demand point if no existing charging pile exists or the average waiting time of the existing charging pile is larger than the preset waiting time threshold.
And screening each parking point of the electric vehicle, wherein the parking time of each parking point exceeds a preset time threshold, screening a plurality of local areas with relatively dense parking points, judging the distribution condition of existing charging piles in each local area, removing the service range of the existing charging piles from the local area, screening out all charging pile candidate addresses meeting the requirements in the residual range according to the construction conditions of the charging piles, evaluating the candidate addresses, including factors such as traffic convenience, land use condition, safety and the like, and screening out proper charging pile construction addresses from the candidate addresses to establish a charging pile candidate address set.
In addition, the parking distribution rules of the electric vehicles may be different according to different time periods, for example, the parking distribution of the electric vehicles in the daytime and at night is different, so that the parking distribution data of the electric vehicles in different time periods can be separately processed, and the candidate address sets of the charging piles obtained in different time periods such as the daytime and at night are cross-considered to screen suitable candidate addresses as far as possible.
And S4, based on the charging demand point set and the charging pile candidate address set, establishing a charging pile site selection model with the aim of minimum cost loss and highest charging convenience.
The objective function of the charging pile site selection model is as follows:
wherein the method comprises the steps ofFAs a function of the object to be processed,C 1 is the total construction cost loss of the charging pile,C 2 Is the total operation cost loss of the charging pile,S 1 Is a distance convenience index,S 2 As an index of the convenience of the time,w 1 、w 2 、w 3 、w 4 are weight coefficients.
Total construction cost loss of charging pileC 1 =Num×C 01 ，C 01 For the construction cost of a single charging pile, the construction cost of the charging pile specifically comprises equipment cost, infrastructure transformation cost, land cost and environment evaluationEstimate and approve costs, etc.
Total cost of operation of the charging pileC 2 =Num×C 02 ，C 02 The operation cost of the single charging pile mainly comprises daily management cost, such as manual maintenance cost, electric power cost, network communication cost, insurance cost and the like.
Distance convenience indexS 1 For convenience in the distance between the charging demand point and the charging pile address,，N I for the total number of charge demand points,Numin order to require the number of charging piles to be enlarged,λfor the route tortuosity coefficient->For the average power consumption per kilometer of the electric automobile, < >>To the point of charge demandiWith charging pile candidate addressjDistance between->Is a decision variable.
Time convenience indexS 2 The convenience of queuing waiting time after the electric automobile reaches the charging pile address,，charging pile address for electric automobilejThe average waiting time after the charging pile is added, and the charging pressure of the existing charging pile in the charging peak period is shared after the charging pile is added, so the average waiting time is reduced, and the charging pile is added>For existing charging pilessAverage latency +.>Is a function of the decay of (a). Assuming that the average waiting time decays exponentially, +.>Attenuation coefficienta、bCan be determined by fitting experimental data.
The constraint conditions of the objective function are:
wherein,Ifor the set of charging demand points,Jfor the set of charging pile candidate addresses,Pfor the existing set of charge pile position distributions,to the point of charge demandiWith charging pile candidate addressjThe distance between the two plates is set to be equal,d max for a preset maximum distance threshold, +.>For arbitrary charging pile candidate addressjWith any existing charging pileslThe distance between the two plates is set to be equal,D min a preset minimum distance threshold;representing candidate addresses at charging pilesjThe place is established with a charging pile->Representing non-charging pile candidate addressjThe place is established with a charging pile->Representing charging pile candidate addressesjCharging pile at position for charging pointiCharging->Representing charging pile candidate addressesjCharging pile at position without charging pointiAnd (5) charging.
The invention establishes the objective function of the charging pile site selection model based on the total construction cost loss of the charging pile, the total operation cost loss of the charging pile, the distance convenience index and the time convenience index, and adds the maximum distance constraint between the charging demand point and the charging pile candidate addressSetting a minimum distance constraint between an arbitrary charging pile candidate address and an arbitrary existing charging pile position>The method avoids the repetition of the position of the existing charging pile, and can reasonably plan the service range and distribution density of the charging pile while minimizing cost loss and maximizing convenience.
And S5, solving an objective function of the charging pile site selection model by adopting an intelligent optimization algorithm to obtain a charging pile site selection result.
Fig. 2 is a flowchart of solving the charging pile site selection model by adopting an intelligent optimization algorithm, and step S5 specifically includes the following sub-steps:
s51, selecting the smallest integer value in the number range of the charging piles to be expanded as the current number of the charging piles to be expanded。
S52, solving a global optimal solution and a corresponding fitness value of the charging pile site selection model by using an intelligent optimization algorithm by taking an objective function of the charging pile site selection model as a fitness function.
The invention adopts an improved raccoon optimization algorithm to solve the objective function of the charging pile site selection model, and the step S52 specifically comprises the following steps:
s521, initializing a raccoon optimization algorithm population with a population scale ofMEach individual vector in the population represents an addressing scheme, each addressing scheme comprises the address of each charging pile to be expanded, and the number of the addresses of the charging piles to be expanded currently isN u Thus each timeThe dimension of individual vectors isN u Each dimension represents a candidate address.
In general, existing charging piles have the problem of unreasonable layout, but are also generally arranged at positions with larger charging demands, and the scheme of expanding the charging piles is also based on the existing charging piles, so that the position distribution of the existing charging piles can be brought into the initialization positions of the population for accelerating convergence. If the number of addresses of the charging piles to be expanded is requiredN u >N c Will thenN c The locations of the existing charging piles are used as part of the initial population location of the raccoon optimization algorithm, with the remaining locations being generated in a random manner.N u <N c ThenN c Random selection of locations of individual existing charging pilesN u The individual positions are used as initial positions of the population of the intelligent optimization algorithm. Compared with the random initialization mode of the general intelligent optimization algorithm, the method can provide a better initial value, and is beneficial to accelerating the convergence speed.
S522, solving the fitness of each individual by taking an objective function of the charging pile site selection model as a fitness function, and storing the optimal fitness value and the corresponding current optimal position.
Because the objective function is the minimum value, the minimum fitness value is the optimal fitness value, and the corresponding individual position is the current optimal position.
S523, setting a location update policy to update the location of each individual.
The raccoon optimization algorithm of the present invention includes an exploration phase that simulates the prey's strategy when attacking the prey and a development phase that simulates the prey's strategy to escape the prey.
In the exploration stage, half individuals update the positions through a direct attack strategy, and the position updating formula is as follows:
wherein,respectively the firsttNext time,tIndividuals in +1 iterationmIs the first of (2)nThe position of the dimension, where m=1, 2, …, +>，In order to round down the operator,n=1,2,…,N u 。，I 0 =1 or 2.Is a randomly generated position in the solution space.
The attack strategy is started after the rest half of individuals wait in situ, the Lewy flight strategy is introduced to update the position, and the position update formula is as follows:
wherein,m=+1,+2,…,M，as the current optimal position of the object to be processed,F(. Cndot.) is the fitness function,for obeying parameters ofβIs a distribution of the Lewy of (C).
In the development phase, a [0,1 ] is generated]Random number betweenδSine and cosine operators are introduced to simulate the escape of a preyThe running strategy updates the position, and the position updating formula is as follows:
wherein the method comprises the steps ofRepresenting the random number between the generation of (0, 2 pi,)>。
And after each position update, selecting a candidate address closest to each dimension of the current position from the candidate address set of the charging pile as data of each dimension of a new individual vector so as to be consistent with the position in the candidate address set.
The raccoon optimization algorithm of the present invention is modified such that half of the individuals pass through a random location during the exploration phaseThe individual location update is performed, the other half of the individuals are according to +.>The adaptation level of (a) selects the location update mode when the random location + ->When the position of (2) is better, the random position is +.>Approach, otherwise based on the current optimal position +.>Random position->And the method combines with the Lewy flight strategy to update the position, keeps certain randomness, approaches to the current optimal position, accelerates the convergence speed, and avoids the situation that the position is updated by one random position every timeThe mechanization is too strong, and the optimizing speed is influenced.
The improved raccoon optimization algorithm of the invention introduces sine and cosine operators to simulate the escape strategy of the prey to update the position, increase the population diversity and lead the position to the current optimal position with a certain probability in the exploration stageNear, local search and global search are effectively balanced, and the quality of solutions is improved.
S524, calculating the fitness value of each individual again, and updating the historical optimal position.
And S525, judging whether the iteration termination condition is met, if so, outputting a global optimal solution, otherwise, returning to the step S523, and repeating the processes of position updating and fitness value calculation until the iteration termination condition is met, and if so, outputting the global optimal solution.
S53, ifThe number of the charging piles which are required to be expanded at presentN u =N u +1, re-executing step S52 to solve the global optimal solution and the corresponding fitness value of the charging pile site selection model.
S54, comparing the number of different charging pilesN u Screening out the number of charging piles with minimum fitness value according to the fitness value of each global optimal solutionN u And the corresponding global optimal solution is used as a final charging pile address selection result.
The final charging pile site selection result comprises the number of the charging pilesNumAnd the address of each charging stake.
According to the invention, the number range of the charging piles to be expanded is determined according to the number of electric vehicles in a target area and the existing charging pile data, a charging pile site selection model is established based on a charging demand point set and a charging pile candidate address set by taking the minimum cost loss and the highest charging convenience as targets, an objective function of the charging pile site selection model is solved through an intelligent optimization algorithm, the fitness values of all global optimal solutions under different numbers of the charging piles are respectively compared, the optimal number of the charging piles and the corresponding positions are screened out as a final charging pile site selection result, the relation between the expansion number and the positions of the charging piles is effectively balanced, and the reliability of the charging pile site selection is further improved. The invention can effectively select the number and the positions of the charging piles to be expanded on the basis of the existing charging piles, and improves the rationality of the layout planning of the charging piles.
On the basis of the embodiment of the method, the invention also provides a charging pile site selection system, which comprises the following steps:
and a data acquisition module: the method comprises the steps of acquiring the number of electric vehicles in a target area, existing charging pile data and electric vehicle parking distribution data in different time periods;
a range estimation module: the method comprises the steps of determining the number range of charging piles to be expanded according to the number of electric vehicles in a target area and existing charging pile data;
and a model building module: the method comprises the steps of determining a charging demand point set according to existing charging pile data and electric vehicle parking distribution data of different time periods, and establishing a charging pile candidate address set; based on the charging demand point set and the charging pile candidate address set, establishing a charging pile site selection model with the minimum cost loss and the highest charging convenience as targets;
model solving module: and the method is used for solving an objective function of the charging pile site selection model by adopting an intelligent optimization algorithm to obtain a charging pile site selection result.
The system embodiments and the method embodiments are in one-to-one correspondence, and the brief description of the system embodiments is just to refer to the method embodiments.
The invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the memory stores program instructions executable by the processor that the processor invokes to implement the aforementioned methods of the present invention.
The invention also discloses a computer readable storage medium storing computer instructions for causing a computer to implement all or part of the steps of the methods of the embodiments of the invention. The storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, i.e., may be distributed over a plurality of network elements. One of ordinary skill in the art may select some or all of the modules according to actual needs without performing any inventive effort to achieve the objectives of the present embodiment.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. A method for locating a charging pile, the method comprising:
acquiring the number of electric vehicles in a target area, existing charging pile data and electric vehicle parking distribution data in different time periods;
determining the number range of charging piles to be expanded according to the number of electric vehicles in the target area and the existing charging pile data;
determining a charging demand point set according to existing charging pile data and electric vehicle parking distribution data of different time periods, and establishing a charging pile candidate address set;
based on the charging demand point set and the charging pile candidate address set, establishing a charging pile site selection model with the minimum cost loss and the highest charging convenience as targets;
solving an objective function of the charging pile site selection model by adopting an intelligent optimization algorithm to obtain a charging pile site selection result;
the existing charging pile data comprises existing charging pile foundation data and existing charging pile usage data;
the existing charging pile foundation data comprises the number of charging pilesN c Position distribution, charging power per charging pileThe existing charging pile usage data includes a utilization rate of each charging pile +.>Average charging speed->Average waiting timeAverage use time->WhereinsThe number of the charging piles is given,s=1,2,…,N c ；
the determining the number range of the charging piles to be expanded according to the number of the electric vehicles in the target area and the existing charging pile data specifically comprises the following steps:
estimating a minimum required charge amount Q of the target area based on the existing charging pile foundation data and the existing charging pile usage data 1 ；
According to the number of existing charging pilesN c Average waiting time per charging pileMaximum charge demand Q is estimated to electric automobile quantity in target area 2 ；
Charge amount Q according to minimum demand 1 Maximum charge demand Q 2 Determining the number of charging piles to be enlargedNumIs expressed as:
；
wherein,Numis an integer of the number of the times,in order to round up the operator,qfor the average amount of electricity used by each charging stake,，is the firstsThe power of each charging pile;k 1 、k 2 are all adjusting coefficients;
the minimum required charge quantity Q of the target area is estimated according to the existing charging pile foundation data and the existing charging pile use data 1 The formula of (2) is:
；
said number of charging piles is based on the existing number of charging pilesN c Average waiting time per charging pileMaximum charge demand Q is estimated to electric automobile quantity in target area 2 The formula of (2) is:
；
wherein,N e as the number of electric vehicles in the target area,s=1,2,…,N c ；
the objective function of the charging pile site selection model is as follows:
；
wherein the method comprises the steps ofFAs a function of the object to be processed,C 1 is the total construction cost loss of the charging pile,C 2 Is the total operation cost loss of the charging pile,S 1 Is a distance convenience index,S 2 As an index of the convenience of the time,w 1 、w 2 、w 3 、w 4 are all weight coefficients;
the constraint conditions of the objective function are:
；
wherein,Ifor the set of charging demand points,Jfor the set of charging pile candidate addresses,Pfor the existing set of charge pile position distributions,to the point of charge demandiWith charging pile candidate addressjThe distance between the two plates is set to be equal,d max for a preset maximum distance threshold, +.>For arbitrary charging pile candidate addressjWith any existing charging stake locationslThe distance between the two plates is set to be equal,D min a preset minimum distance threshold;Numthe number of the charging piles to be expanded;Representing candidate addresses at charging pilesjThe place is established with a charging pile->Representing non-charging pile candidate addressjThe place is established with a charging pile->Representing charging pile candidate addressesjCharging pile at position for charging pointiCharging->Representing charging pile candidate addressesjCharging pile at position without charging pointiAnd (5) charging.
2. The method for locating a charging pile according to claim 1, wherein the step of solving the objective function of the charging pile locating model by using the intelligent optimization algorithm to obtain the charging pile locating result specifically comprises the following steps:
selecting the smallest integer value in the number range of the charging piles to be expanded as the current number of the charging piles to be expanded；
Taking an objective function of the charging pile site selection model as an fitness function, and solving a global optimal solution and a corresponding fitness value of the charging pile site selection model by adopting an intelligent optimization algorithm;
if it isThe number of the charging piles which are required to be expanded at presentN u Adding 1 to the value of (1), and solving a global optimal solution and a corresponding fitness value of the charging pile site selection model by adopting an intelligent optimization algorithm again;
comparing different numbers of charging pilesN u Screening out the number of charging piles with minimum fitness value according to the fitness value of each global optimal solutionN u And the corresponding global optimal solution is used as a final charging pile address selection result.
3. The method of locating a charging pile according to claim 2, wherein the intelligent optimization algorithm employs a modified raccoon optimization algorithm, and solving the globally optimal solution and corresponding fitness value of the charging pile locating model by the modified raccoon optimization algorithm comprises:
initializing the population position, and the population scale isM；
Calculating the fitness of each individual by taking an objective function of the charging pile site selection model as a fitness function;
updating the location of each individual by setting a location update strategy, the location update strategy comprising an exploration phase and a development phase;
in the exploration phase, half individuals update positions in a random mode; the rest half of individuals introduce a Lewy flight strategy to update the position, and the position updating formula is as follows:
；
wherein t is the iteration number,the position of the nth dimension of the individual m at the t-th and t+1-th iterations respectively,，n=1,2,…,N u ，for the historic optimal position at the t-th iteration, F (·) is the fitness function, ++>For obeying parameters ofβIs of Lei Wei distribution, lei Lu>，For randomly generated positions in the solution space,I 0 =1 or 2;
in the development phase, a [0,1 ] is generated]Random number betweenδSine and cosine operators are introduced to simulate the escape strategy of the prey to update the position, and the position updating formula is as follows:
；
wherein the method comprises the steps ofRepresenting the random number between the generation of (0, 2 pi,)>；
Re-calculating the fitness value of each individual, and updating the historical optimal position;
and judging whether the iteration termination condition is met, if so, outputting a global optimal solution and a corresponding fitness value, and if not, repeating the processes of position updating and fitness value calculation until the iteration termination condition is met.
4. A charging pile site selection system using the method of any one of claims 1 to 3, characterized in that the system comprises:
and a data acquisition module: the method comprises the steps of acquiring the number of electric vehicles in a target area, existing charging pile data and electric vehicle parking distribution data in different time periods;
a range estimation module: the method comprises the steps of determining the number range of charging piles to be expanded according to the number of electric vehicles in a target area and existing charging pile data;
and a model building module: the method comprises the steps of determining a charging demand point set according to existing charging pile data and electric vehicle parking distribution data of different time periods, and establishing a charging pile candidate address set; based on the charging demand point set and the charging pile candidate address set, establishing a charging pile site selection model with the minimum cost loss and the highest charging convenience as targets;
model solving module: and the method is used for solving an objective function of the charging pile site selection model by adopting an intelligent optimization algorithm to obtain a charging pile site selection result.
5. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete communication with each other through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the method of any of claims 1-3.
6. A computer readable storage medium storing computer instructions for causing a computer to implement the method of any one of claims 1 to 3.
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