CN110110947A - A kind of Optimization Method for Location-Selection and system of charging station - Google Patents
A kind of Optimization Method for Location-Selection and system of charging station Download PDFInfo
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Abstract
The present invention provides the Optimization Method for Location-Selection and system of a kind of charging station, this method are as follows: using the location information of the electric car in region to be optimized, determines the initial position of each charging station to be optimized in region to be optimized.Using the characteristic in region to be optimized as the input of preset Random Forest model, the utilization rate of each charging station to be optimized in region to be optimized is calculated.According to the initial position of to be optimized charging station of the utilization rate in pre-set interval, the optimal location of to be optimized charging station of the utilization rate in pre-set interval is calculated using particle swarm optimization algorithm.In the present solution, calculating separately the utilization rate and initial position of each charging station to be optimized in region to be optimized using the location information of Random Forest model and electric car.And the optimal location of charging station to be optimized is calculated using particle swarm optimization algorithm.The utilization rate and addressing of manually inferring charging station are avoided, keeps the addressing of charging station more reasonable, reduces cost and increase operation rate.
Description
Technical field
The present invention relates to technical field of data processing, and in particular to a kind of Optimization Method for Location-Selection and system of charging station.
Background technique
With the development of science and technology, environmental issue is increasingly becoming one of current all trades and professions focus of attention problem.It is right
In automobile industry, for the energy-saving and emission-reduction proposal of response country, electric car is increasingly becoming the vehicles common in people's life.
For the continuation of the journey for guaranteeing electric car, need to configure enough charging stations for electric car.The usual people of charging station at present
To be arbitrarily set to community parking field, store parking lot and hospital parking lot etc..But aforementioned several addresses equipped with charging station
In, the utilization rate of the only charging station of community parking field is higher, and the utilization rate of other charging stations is lower.Due to setting charging station
Higher cost, therefore on the one hand the position addressing of charging station more arbitrarily wastes construction cost, be on the other hand unable to fully utilize
Each charging station, charging station utilization rate are low.
Summary of the invention
In view of this, the embodiment of the present invention provides the Optimization Method for Location-Selection and system of a kind of charging station, to solve existing people
The problems such as work is at high cost low with utilization rate existing for charging station addressing.
To achieve the above object, the embodiment of the present invention provides the following technical solutions:
First aspect of the embodiment of the present invention provides a kind of Optimization Method for Location-Selection of charging station, which comprises
For each preset region to be optimized, using the location information of the electric car in the region to be optimized, really
The initial position of each charging station to be optimized in the fixed region to be optimized；
Using the characteristic in each region to be optimized as the input of preset Random Forest model, calculate it is described to
Optimize the utilization rate of each charging station to be optimized in region, wherein the characteristic includes at least: area information to be optimized,
Power distribution information and electric car information；
For each region to be optimized, according to the initial bit of to be optimized charging station of the utilization rate in pre-set interval
It sets, the optimal location of to be optimized charging station of the utilization rate in pre-set interval is calculated using particle swarm optimization algorithm.
Preferably, the location information using the electric car in the region to be optimized, determines the area to be optimized
The initial position of each charging station to be optimized in domain, comprising:
Determine the service range of each charging station to be optimized in the region to be optimized；
The location information of electric car in service range based on each charging station to be optimized, utilizes M=(avg
(X1+X2+…+Xi),avg(Y1+Y2+…+Yi)) calculating the initial position M of each charging station to be optimized, wherein i is each to excellent
Change the electric car total quantity in the service range of charging station, XiAnd YiThe respectively longitude and latitude of electric car.
Preferably, the initial position of the charging station to be optimized according to utilization rate in pre-set interval, utilizes population
Optimization algorithm calculates the optimal location of to be optimized charging station of the utilization rate in pre-set interval, comprising:
According to the initial position of to be optimized charging station of the utilization rate in pre-set interval, utilizeCalculate the optimal location of to be optimized charging station of the utilization rate in pre-set interval, wherein MiFor to
Optimize the initial position of charging station, VidFor particle rapidity, k is current iteration number, d=1,2 ..., D, i=1,2 ..., n.
Preferably, after the optimal location of the charging station to be optimized of the calculating utilization rate in pre-set interval, also
Include:
Calculate the fitness value of to be optimized charging station of the utilization rate in pre-set interval；
Compare the fitness value of the charging station to be optimized and the size of global extremum；
If the fitness value is less than the global extremum, the global extremum is remained unchanged；
If the fitness value is greater than the global extremum, the value of the global extremum is updated to the fitness
Value；
It usesUtilization rate is calculated in pre-set interval
The charging station to be optimized optimal charge efficiency, wherein ω be the inertia weight factor, k be current iteration number, d=1,
2 ..., D, i=1,2 ..., n, MiFor the initial position of charging station to be optimized, c1And c2For accelerated factor, r1And r2For 0 to 1
Random number in section, PidAnd PgdRespectively individual known preferred solution and population known preferred solution, viFor the charging to be optimized
The initial charge rate stood, the PidIt is equal with the global extremum.
It is preferably, described to calculate in the region to be optimized after the utilization rate of each charging station to be optimized, further includes:
Based on the utilization rate of each charging station to be optimized in the region to be optimized, according to service rating from high to low suitable
Charging stations to be optimized all in the region to be optimized are carried out service rating division by sequence.
Second aspect of the embodiment of the present invention discloses a kind of addressing optimization system of charging station, the system comprises:
Determination unit utilizes the electric car in the region to be optimized for being directed to each preset region to be optimized
Location information, determine the initial position of each charging station to be optimized in the region to be optimized；
Computing unit, for using the characteristic in each region to be optimized as the defeated of preset Random Forest model
Enter, calculates the utilization rate of each charging station to be optimized in the region to be optimized, wherein the characteristic includes at least: to
Optimize area information, power distribution information and electric car information；
Optimize unit, for being directed to each region to be optimized, is filled according to be optimized in pre-set interval of utilization rate
The initial position in power station calculates the optimal position of to be optimized charging station of the utilization rate in pre-set interval using particle swarm optimization algorithm
It sets.
Preferably, the determination unit includes:
Determining module, for determining the service range of each charging station to be optimized in the region to be optimized；
Computing module, the position for the electric car in the service range based on each charging station to be optimized are believed
Breath, utilizes M=(avg (X1+X2+…+Xi),avg(Y1+Y2+…+Yi)) the initial position M of each charging station to be optimized is calculated,
In, i is the electric car total quantity in the service range of each charging station to be optimized, XiAnd YiThe respectively longitude and latitude of electric car
Degree.
Preferably, the optimization unit includes:
Position optimization module is utilized for the initial position of the charging station to be optimized according to utilization rate in pre-set intervalCalculate the optimal location of the to be optimized charging station of the utilization rate in pre-set interval, wherein Mi
For the initial position of charging station to be optimized, VidFor particle rapidity, k is current iteration number, d=1,2 ..., D, i=1,
2,...,n。
Preferably, the optimization unit further include:
Computing module, for calculating the fitness value of to be optimized charging station of the utilization rate in pre-set interval；
Comparison module, for the fitness value of the charging station to be optimized and the size of global extremum, if described suitable
Angle value is answered to be less than the global extremum, the global extremum remains unchanged, will if the fitness value is greater than the global extremum
The value of the global extremum is updated to the fitness value；
Charge efficiency optimization module, for usingMeter
Calculate the optimal charge efficiency of to be optimized charging station of the utilization rate in pre-set interval, wherein ω is the inertia weight factor, and k is to work as
Preceding the number of iterations, d=1,2 ..., D, i=1,2 ..., n, MiFor the initial position of charging station to be optimized, c1And c2For accelerate because
Son, r1And r2For the random number in 0 to 1 section, PidAnd PgdRespectively individual known preferred solution and population known preferred solution, vi
For the initial charge rate of the charging station to be optimized, the PidIt is equal with the global extremum.
Preferably, the system also includes:
Division unit, for the utilization rate based on each charging station to be optimized in the region to be optimized, according to using
Charging stations to be optimized all in the region to be optimized are carried out service rating division by the sequence of grade from high to low.
Optimization Method for Location-Selection and system based on a kind of charging station that the embodiments of the present invention provide, this method are as follows: needle
To each region to be optimized, using the location information of the electric car in region to be optimized, determine in region to be optimized it is each to
Optimize the initial position of charging station.Using the characteristic in region to be optimized as the input of preset Random Forest model, calculate
The utilization rate of each charging station to be optimized in region to be optimized.According to the first of to be optimized charging station of the utilization rate in pre-set interval
Beginning position calculates the optimal location of to be optimized charging station of the utilization rate in pre-set interval using particle swarm optimization algorithm.We
In case, using the location information of Random Forest model and electric car, each charging to be optimized in region to be optimized is calculated separately
The utilization rate and initial position stood.And the optimal location of charging station to be optimized is calculated using particle swarm optimization algorithm.It avoids artificial
The utilization rate and addressing for inferring charging station, keep the addressing of charging station more reasonable, reduce cost and increase operation rate.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of Optimization Method for Location-Selection flow chart of charging station provided in an embodiment of the present invention；
Fig. 2 is the flow chart provided in an embodiment of the present invention for calculating the optimal charge efficiency of charging station；
Fig. 3 is a kind of structural block diagram of the addressing optimization system of charging station provided in an embodiment of the present invention；
Fig. 4 is a kind of structural block diagram of the addressing optimization system of charging station provided in an embodiment of the present invention；
Fig. 5 is a kind of structural block diagram of the addressing optimization system of charging station provided in an embodiment of the present invention；
Fig. 6 is a kind of structural block diagram of the addressing optimization system of charging station provided in an embodiment of the present invention；
Fig. 7 is a kind of structural block diagram of the addressing optimization system of charging station provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In this application, the terms "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion,
So that the process, method, article or equipment for including a series of elements not only includes those elements, but also including not having
The other element being expressly recited, or further include for elements inherent to such a process, method, article, or device.Do not having
There is the element limited in the case where more limiting by sentence "including a ...", it is not excluded that in the mistake including the element
There is also other identical elements in journey, method, article or equipment.
It can be seen from background technology that charging station is usually artificially arbitrarily set to community parking field, store parking lot and doctor at present
Institute parking lot etc., addressing is more random.The position addressing of charging station more arbitrarily on the one hand waste construction cost, on the other hand without
Method makes full use of each charging station, and charging station utilization rate is low.
Therefore the embodiment of the present invention provides the Optimization Method for Location-Selection and system of a kind of charging station, using Random Forest model and
The location information of electric car calculates separately the utilization rate and initial position of each charging station to be optimized in region to be optimized.And
The optimal location of charging station to be optimized is calculated using particle swarm optimization algorithm.To improve the utilization rate of charging station and reduce cost.
With reference to Fig. 1, a kind of Optimization Method for Location-Selection flow chart of charging station provided in an embodiment of the present invention is shown, including with
Lower step:
Step S101: it is directed to each preset region to be optimized, utilizes the position of the electric car in the region to be optimized
Confidence breath, determines the initial position of each charging station to be optimized in the region to be optimized.
It, will be described to be optimized previously according to the constraint condition of region to be optimized during implementing step S101
Region zones are multiple regions to be optimized.For example, Beijing is divided into multiple previously according to the constraint condition of Beijing
Region to be optimized.Wherein, the constraint condition of the region to be optimized includes at least: the electric car stream of the region to be optimized
Amount, existing public charging station position, parking location, power distribution network capacity, charge requirement and existing charging station utilization rate etc..
It should be noted that being that multiple regions to be optimized are included but are not limited to lower section by the region zones to be optimized
Formula.
Mode one: choosing a charging station of the region to be optimized, with the charging station periphery preset the range of kilometer into
Row region division to be optimized.For example region division to be optimized is carried out with the range on 5 kilometers of the charging station periphery.
Mode two: region division to be optimized is carried out according to the power distribution network capacity of the region to be optimized.
Mode three: being multiple regions to be optimized by the region zones to be optimized according to administrative region.
During implementing step S101, the service of each charging station to be optimized in the region to be optimized is determined
Range.I.e. using charging station to be optimized as the center of circle, circle is formed by using the corresponding Weighted distance of the charging station to be optimized as radius
For service range.The location information of electric car in service range based on each charging station to be optimized, utilizes formula
(1) the initial position M of each charging station to be optimized is calculated.In the formula (1), i is the service of each charging station to be optimized
Electric car total quantity in range, XiAnd YiThe respectively longitude and latitude of electric car.
M=(avg (X1+X2+…+Xi),avg(Y1+Y2+…+Yi)) (1)
It should be noted that the above-mentioned Weighted distance being related to specifically: region to be optimized for one, the region to be optimized
Interior all electric cars go to same charging station to be optimized at same time point, can get the weighting of the charging station to be optimized away from
From.
Step S102: using the characteristic in each region to be optimized as the input of preset Random Forest model,
Calculate the utilization rate of each charging station to be optimized in the region to be optimized.
It is right in conjunction with a kind of index and two class indexs in each region to be optimized before implementing step S102
Each region to be optimized carries out data processing.Problem data in the region to be optimized are maked corrections or deleted, with
And the data in the region to be optimized are summarized, such as summarize filling in existing charging station in the region to be optimized
Electric stake quantity and total rate that charges.
It should be noted that a kind of index includes at least: street information, parking lot information, building, existing charging station letter
Breath can use extension power distribution network capacity, can use distribution capacity and car category etc..Two class indexs include at least: vehicle flowrate, charging need
It asks, remaining capacity, charging probability, charging times, Rechargeable vehicle quantity, the fully charged required time of electric car and single charge are put down
The equal time.
During implementing step S102, what a Random Forest model trained in advance based on sample data set,
Using the characteristic in each region to be optimized as the input of preset Random Forest model, the region to be optimized is calculated
In each charging station to be optimized utilization rate.The characteristic in the region to be optimized includes at least: area information to be optimized is matched
Power information and electric car information, i.e., the characteristic in the described region to be optimized are that the above-mentioned a kind of index being related to and two classes refer to
Mark.
Preferably, the utilization rate based on each charging station to be optimized in the region to be optimized, according to service rating by height
To low sequence, charging stations to be optimized all in the region to be optimized are subjected to service rating division.Such as: utilization rate is existed
The service rating of m1 to the charging station to be optimized between m2 is set as level-one, by utilization rate in m2 to the charging station to be optimized between m3
Service rating be set as second level, the service rating by utilization rate in m3 to the charging station to be optimized between m4 is set as three-level, will be sharp
The service rating of charging station to be optimized with rate less than m4 is set as level Four.
Step S103: it is directed to each region to be optimized, according to be optimized charging station of the utilization rate in pre-set interval
Initial position, utilize particle swarm optimization algorithm to calculate the optimal location of to be optimized charging station of the utilization rate in pre-set interval.
During implementing step S013, using each charging station to be optimized as one in particle swarm optimization algorithm
A analysis particle.According to the initial position of to be optimized charging station of the utilization rate in pre-set interval, is calculated and utilized using formula (2)
The optimal location of to be optimized charging station of the rate in pre-set interval.Such as it is using service rating shown in above-mentioned steps S102
The initial position of the charging station to be optimized of firsts and seconds, calculate service rating be firsts and seconds charging station to be optimized most
Excellent position.In the formula (2), MiFor the initial position of charging station to be optimized, VidFor particle rapidity, k is current iteration time
Number, d=1,2 ..., D, i=1,2 ..., n.
It should be noted that presetting the boundary value and particle rapidity boundary value of optimal location in above-mentioned formula (2).
Boundary value i.e. in the position of d dimension is [Mmin,d, Mmax,d], the boundary value of particle rapidity is [- Vmax,d, Vmax,d]。
In embodiments of the present invention, for each region to be optimized, the position of the electric car in region to be optimized is utilized
Information determines the initial position of each charging station to be optimized in region to be optimized.Using the characteristic in region to be optimized as pre-
If Random Forest model input, calculate the utilization rate of each charging station to be optimized in region to be optimized.Existed according to utilization rate
The initial position of charging station to be optimized in pre-set interval calculates utilization rate in pre-set interval using particle swarm optimization algorithm
The optimal location of charging station to be optimized.The utilization rate and addressing of manually inferring charging station are avoided, keeps the addressing of charging station more reasonable,
It reduces cost and increases operation rate.
Preferably, in executing above-mentioned Fig. 1 after step S104, utilization rate also is calculated pre- using particle swarm optimization algorithm
If the optimal charge efficiency of the charging station to be optimized in section shows calculating provided in an embodiment of the present invention with reference to Fig. 2
The flow chart of the optimal charge efficiency of charging station, comprising the following steps:
Step S201: the fitness value of to be optimized charging station of the utilization rate in pre-set interval is calculated.
Step S202: the fitness value of the charging station to be optimized and the size of global extremum, if the fitness
Value is less than the global extremum, executes step S203, if the fitness value is greater than the global extremum, executes step S204.
During implementing step S202, there are a global extremums for each charging station to be optimized, by comparing
The size of the fitness value and global extremum updates the value of global extremum based on comparative result.
It should be noted that global extremum indicates the optimal solution that each individual has been found in particle swarm optimization algorithm.
Step S203: the global extremum remains unchanged, and executes step S205.
Step S204: the value of the global extremum is updated to the fitness value.
Step S205: to be optimized charging station optimal of the utilization rate in pre-set interval is calculated using formula (3) and is filled
Electrical efficiency.
In the formula (3), ω is the inertia weight factor, and k is current iteration number, d=1,2 ..., D, i=1,
2 ..., n, MiFor the initial position of charging station to be optimized, c1And c2For accelerated factor, c1And c2Nonnegative number, r1And r2For 0 to 1
Random number in section, PidAnd PgdRespectively individual known preferred solution and population known preferred solution, viFor the charging to be optimized
The initial charge rate stood, the PidIt is equal with the global extremum.
It should be noted that charge rate refers to: the charge rate of idle charging pile in charging station.In each charging station
Include more than one charging pile.
In embodiments of the present invention, the initial position of the charging station to be optimized based on utilization rate in pre-set interval utilizes
Particle swarm optimization algorithm calculates the optimal charge efficiency of to be optimized charging station of the utilization rate in pre-set interval.For each to excellent
Change region, charging station is carried out according to the optimal location of to be optimized charging station of the utilization rate in pre-set interval and optimal charge efficiency
Addressing avoids utilization rate, charge efficiency and the addressing of manually inferring charging station, keeps the addressing of charging station more reasonable, reduce cost
With increase operation rate.
The Optimization Method for Location-Selection of a kind of charging station provided with the embodiments of the present invention, with reference to Fig. 3, the embodiment of the present invention
A kind of structural block diagram of the addressing optimization system of charging station is also provided, the system comprises: determination unit 301, computing unit 302
With optimization unit 303.
Determination unit 301 utilizes the electronic vapour in the region to be optimized for being directed to each preset region to be optimized
The location information of vehicle determines the initial position of each charging station to be optimized in the region to be optimized.The region to be optimized
Partition process is referring to corresponding content in embodiments of the present invention Fig. 1 step S101.
Computing unit 302, for using the characteristic in each region to be optimized as preset Random Forest model
Input, calculate the utilization rate of each charging station to be optimized in the region to be optimized, wherein the characteristic is at least wrapped
It includes: area information, power distribution information and electric car information to be optimized.The process of utilization rate is calculated referring to the embodiments of the present invention
Corresponding content in Fig. 1 step S102.
Optimize unit 303, it is to be optimized in pre-set interval according to utilization rate for being directed to each region to be optimized
The initial position of charging station calculates the optimal of to be optimized charging station of the utilization rate in pre-set interval using particle swarm optimization algorithm
Position.The process of optimal location is calculated referring to corresponding content in embodiments of the present invention Fig. 1 step S103.
In embodiments of the present invention, for each region to be optimized, the position of the electric car in region to be optimized is utilized
Information determines the initial position of each charging station to be optimized in region to be optimized.Using the characteristic in region to be optimized as pre-
If Random Forest model input, calculate the utilization rate of each charging station to be optimized in region to be optimized.Existed according to utilization rate
The initial position of charging station to be optimized in pre-set interval calculates utilization rate in pre-set interval using particle swarm optimization algorithm
The optimal location of charging station to be optimized.The utilization rate and addressing of manually inferring charging station are avoided, keeps the addressing of charging station more reasonable,
It reduces cost and increases operation rate.
Preferably, a kind of addressing optimization system of charging station provided in an embodiment of the present invention is shown with reference to Fig. 4 in conjunction with Fig. 3
The structural block diagram of system, the determination unit 301 include:
Determining module 3011, for determining the service range of each charging station to be optimized in the region to be optimized.
Computing module 3012, the position for the electric car in the service range based on each charging station to be optimized
Information calculates the initial position of each charging station to be optimized using formula (1).Calculate the initial position of each charging station to be optimized
Process referring to corresponding content in embodiments of the present invention Fig. 1 step S101.
In embodiments of the present invention, according to the location information of electric car in the service range of each charging station to be optimized,
The initial position of charging station to be optimized is calculated.Based on the initial position of charging station to be optimized, particle swarm optimization algorithm is utilized
Calculate the optimal location of to be optimized charging station of the utilization rate in pre-set interval.Avoid the utilization rate and choosing of manually inferring charging station
Location keeps the addressing of charging station more reasonable, reduces cost and increases operation rate.
Preferably, a kind of addressing optimization system of charging station provided in an embodiment of the present invention is shown with reference to Fig. 5 in conjunction with Fig. 3
The structural block diagram of system, the optimization unit 303 include:
Position optimization module 3031, for the initial position of the charging station to be optimized according to utilization rate in pre-set interval,
The optimal location of to be optimized charging station of the utilization rate in pre-set interval is calculated using formula (2).Specific calculating process is referring to upper
State corresponding content in Fig. 1 step of embodiment of the present invention S103.
Preferably, a kind of addressing optimization system of charging station provided in an embodiment of the present invention is shown with reference to Fig. 6 in conjunction with Fig. 3
The structural block diagram of system, the optimization unit 303 further include: computing module 3032, comparison module 3033 and charge efficiency optimize mould
Block 3034.
Computing module 3032, for calculating the fitness value of to be optimized charging station of the utilization rate in pre-set interval.
Comparison module 3033, for the fitness value of the charging station to be optimized and the size of global extremum, if institute
Fitness value is stated less than the global extremum, the global extremum remains unchanged, if the fitness value is greater than the global pole
Value, is updated to the fitness value for the value of the global extremum.
Charge efficiency optimization module 3034 is filled for calculating to be optimized in pre-set interval of utilization rate using formula (3)
The optimal charge efficiency in power station.
In embodiments of the present invention, the initial position of the charging station to be optimized based on utilization rate in pre-set interval uses
Particle swarm optimization algorithm calculates the optimal charge efficiency of to be optimized charging station of the utilization rate in pre-set interval.For each to excellent
Change region, charging station is carried out according to the optimal location of to be optimized charging station of the utilization rate in pre-set interval and optimal charge efficiency
Addressing avoids utilization rate, charge efficiency and the addressing of manually inferring charging station, keeps the addressing of charging station more reasonable, reduce cost
With increase operation rate.
Preferably, a kind of addressing optimization system of charging station provided in an embodiment of the present invention is shown with reference to Fig. 7 in conjunction with Fig. 3
The structural block diagram of system, the system also includes:
Division unit 304, for the utilization rate based on each charging station to be optimized in the region to be optimized, according to using
Charging stations to be optimized all in the region to be optimized are carried out service rating division by the sequence of grade from high to low.
In conclusion the embodiment of the present invention provides the Optimization Method for Location-Selection and system of a kind of charging station, this method are as follows: be directed to
Each region to be optimized is determined each to excellent in region to be optimized using the location information of the electric car in region to be optimized
Change the initial position of charging station.Using the characteristic in region to be optimized as the input of preset Random Forest model, calculate to
Optimize the utilization rate of each charging station to be optimized in region.According to the initial of to be optimized charging station of the utilization rate in pre-set interval
Position calculates the optimal location of to be optimized charging station of the utilization rate in pre-set interval using particle swarm optimization algorithm.This programme
In, using the location information of Random Forest model and electric car, calculate separately each charging station to be optimized in region to be optimized
Utilization rate and initial position.And the optimal location of charging station to be optimized is calculated using particle swarm optimization algorithm.It avoids manually pushing away
The utilization rate and addressing of disconnected charging station, keep the addressing of charging station more reasonable, reduce cost and increase operation rate.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system or
For system embodiment, since it is substantially similar to the method embodiment, so describing fairly simple, related place is referring to method
The part of embodiment illustrates.System and system embodiment described above is only schematical, wherein the conduct
The unit of separate part description may or may not be physically separated, component shown as a unit can be or
Person may not be physical unit, it can and it is in one place, or may be distributed over multiple network units.It can root
According to actual need that some or all of the modules therein is selected to achieve the purpose of the solution of this embodiment.Ordinary skill
Personnel can understand and implement without creative efforts.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Profession
Technical staff can use different methods to achieve the described function each specific application, but this realization is not answered
Think beyond the scope of this invention.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (10)
1. a kind of Optimization Method for Location-Selection of charging station, which is characterized in that the described method includes:
Institute is determined using the location information of the electric car in the region to be optimized for each preset region to be optimized
State the initial position of each charging station to be optimized in region to be optimized；
Using the characteristic in each region to be optimized as the input of preset Random Forest model, calculate described to be optimized
The utilization rate of each charging station to be optimized in region, wherein the characteristic includes at least: area information to be optimized, distribution
Information and electric car information；
For each region to be optimized, according to the initial position of to be optimized charging station of the utilization rate in pre-set interval, benefit
The optimal location of to be optimized charging station of the utilization rate in pre-set interval is calculated with particle swarm optimization algorithm.
2. the method according to claim 1, wherein the electric car using in the region to be optimized
Location information determines the initial position of each charging station to be optimized in the region to be optimized, comprising:
Determine the service range of each charging station to be optimized in the region to be optimized；
The location information of electric car in service range based on each charging station to be optimized, utilizes M=(avg (X1+X2
+…+Xi),avg(Y1+Y2+…+Yi)) calculating the initial position M of each charging station to be optimized, wherein i is each to be optimized fills
Electric car total quantity in the service range in power station, XiAnd YiThe respectively longitude and latitude of electric car.
3. the method according to claim 1, wherein to be optimized in pre-set interval according to utilization rate fills
The initial position in power station calculates the optimal position of to be optimized charging station of the utilization rate in pre-set interval using particle swarm optimization algorithm
It sets, comprising:
According to the initial position of to be optimized charging station of the utilization rate in pre-set interval, utilizeIt calculates
The optimal location of to be optimized charging station of the utilization rate in pre-set interval, wherein MiFor the initial position of charging station to be optimized, Vid
For particle rapidity, k is current iteration number, d=1,2 ..., D, i=1,2 ..., n.
4. according to the method described in claim 3, it is characterized in that, the calculating utilization rate in pre-set interval described in excellent
After the optimal location for changing charging station, further includes:
Calculate the fitness value of to be optimized charging station of the utilization rate in pre-set interval；
Compare the fitness value of the charging station to be optimized and the size of global extremum；
If the fitness value is less than the global extremum, the global extremum is remained unchanged；
If the fitness value is greater than the global extremum, the value of the global extremum is updated to the fitness value；
It usesCalculate institute of the utilization rate in pre-set interval
State the optimal charge efficiency of charging station to be optimized, wherein ω be the inertia weight factor, k be current iteration number, d=1,
2 ..., D, i=1,2 ..., n, MiFor the initial position of charging station to be optimized, c1And c2For accelerated factor, r1And r2For 0 to 1
Random number in section, PidAnd PgdRespectively individual known preferred solution and population known preferred solution, viFor the charging to be optimized
The initial charge rate stood, the PidIt is equal with the global extremum.
5. the method according to claim 1, wherein described calculate each in the region to be optimized to be optimized fill
After the utilization rate in power station, further includes:
Based on the utilization rate of each charging station to be optimized in the region to be optimized, according to the sequence of service rating from high to low,
Charging stations to be optimized all in the region to be optimized are subjected to service rating division.
6. a kind of addressing optimization system of charging station, which is characterized in that the system comprises:
Determination unit utilizes the position of the electric car in the region to be optimized for being directed to each preset region to be optimized
Confidence breath, determines the initial position of each charging station to be optimized in the region to be optimized；
Computing unit, for using the characteristic in each region to be optimized as the input of preset Random Forest model,
Calculate the utilization rate of each charging station to be optimized in the region to be optimized, wherein the characteristic includes at least: to be optimized
Area information, power distribution information and electric car information；
Optimize unit, for being directed to each region to be optimized, according to be optimized charging station of the utilization rate in pre-set interval
Initial position, utilize particle swarm optimization algorithm to calculate the optimal location of to be optimized charging station of the utilization rate in pre-set interval.
7. system according to claim 6, which is characterized in that the determination unit includes:
Determining module, for determining the service range of each charging station to be optimized in the region to be optimized；
Computing module, for the location information of the electric car in the service range based on each charging station to be optimized, benefit
With M=(avg (X1+X2+…+Xi),avg(Y1+Y2+…+Yi)) calculate the initial position M of each charging station to be optimized, wherein i
For the electric car total quantity in the service range of each charging station to be optimized, XiAnd YiThe respectively longitude and latitude of electric car.
8. system according to claim 6, which is characterized in that the optimization unit includes:
Position optimization module is utilized for the initial position of the charging station to be optimized according to utilization rate in pre-set intervalCalculate the optimal location of the to be optimized charging station of the utilization rate in pre-set interval, wherein Mi
For the initial position of charging station to be optimized, VidFor particle rapidity, k is current iteration number, d=1,2 ..., D, i=1,
2,...,n。
9. system according to claim 8, which is characterized in that the optimization unit further include:
Computing module, for calculating the fitness value of to be optimized charging station of the utilization rate in pre-set interval；
Comparison module, for the fitness value of the charging station to be optimized and the size of global extremum, if the fitness
Value is less than the global extremum, and the global extremum remains unchanged, will be described if the fitness value is greater than the global extremum
The value of global extremum is updated to the fitness value；
Charge efficiency optimization module, for usingCalculate benefit
With the optimal charge efficiency of to be optimized charging station of the rate in pre-set interval, wherein ω is the inertia weight factor, and k is current changes
Generation number, d=1,2 ..., D, i=1,2 ..., n, MiFor the initial position of charging station to be optimized, c1And c2For accelerated factor,
r1And r2For the random number in 0 to 1 section, PidAnd PgdRespectively individual known preferred solution and population known preferred solution, viFor
The initial charge rate of the charging station to be optimized, the PidIt is equal with the global extremum.
10. system according to claim 6, which is characterized in that the system also includes:
Division unit, for the utilization rate based on each charging station to be optimized in the region to be optimized, according to service rating by
Charging stations to be optimized all in the region to be optimized are carried out service rating division by high to low sequence.
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Application publication date: 20190809 |