CN110111001A - A kind of Site planning method of electric automobile charging station, device and equipment - Google Patents
A kind of Site planning method of electric automobile charging station, device and equipment Download PDFInfo
- Publication number
- CN110111001A CN110111001A CN201910372063.2A CN201910372063A CN110111001A CN 110111001 A CN110111001 A CN 110111001A CN 201910372063 A CN201910372063 A CN 201910372063A CN 110111001 A CN110111001 A CN 110111001A
- Authority
- CN
- China
- Prior art keywords
- charging station
- particle
- target
- default
- planning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 239000002245 particle Substances 0.000 claims abstract description 90
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 72
- 230000003044 adaptive effect Effects 0.000 claims description 55
- 238000003064 k means clustering Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 10
- 238000005553 drilling Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 5
- 230000008901 benefit Effects 0.000 claims description 4
- 230000001351 cycling effect Effects 0.000 claims description 2
- 230000002028 premature Effects 0.000 abstract description 4
- 238000005457 optimization Methods 0.000 description 12
- 230000006870 function Effects 0.000 description 8
- 230000006978 adaptation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0204—Market segmentation
- G06Q30/0205—Location or geographical consideration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a kind of Site planning method of electric automobile charging station, device, equipment and computer readable storage mediums, comprising: the initial location of the target charging station quantity and each charging station in default planning region is determined using pre-selection clustering algorithm；According to the initial location of the target charging pile quantity and each charging station, respectively determine linear decrease weight particle swarm algorithm population and each particle initial position；Using whole society's cost of the linear decrease weight particle swarm algorithm and each particle initial position, the target site of each charging station is determined.Method, apparatus, equipment and computer readable storage medium provided by the present invention greatly reduce the runing time for determining optimal charging station quantity.Solve the problems, such as that conventional particle group's algorithm is easy to appear " Premature Convergence ", local optimum.
Description
Technical field
The present invention relates to electric vehicle engineering fields, more particularly to a kind of siteselecting planning side of electric automobile charging station
Method, device, equipment and computer readable storage medium.
Background technique
Currently, global fossil energy is gradual exhausted, environmental problem is also constantly being aggravated, wherein fuel-engined vehicle
Use it is very big to injury accounting caused by environment, therefore, China for the energy using with it is environmental-friendly for the use of the considerations of,
Presumption principle energetically has been carried out to electric car.But will appear some continuation of the journey problems during Development of EV,
It compares with fuel-engined vehicle, the cruising ability of electric car is much smaller than fuel-engined vehicle, the wherein continuation of the journey of electric car
Ability depends on its internal cell, since current battery technology need to be improved, the cruising ability of electric car is by institute
It is determined in the charging station in region.
The step of planing method of electric automobile charging station is as follows in the prior art: being with charging station year operation Income Maximum
Objective function establishes electric automobile charging station plan model；Weighting volt Luo Nuoyi figure is introduced, charging station coverage is divided
Analysis；Passed through using the optimal value that conventional particle group (PS0) optimization algorithm solves charging station planning with characterization charging station location and appearance
The continuous searching process of the particle of amount, to simulate the optimizing selection of various charging station programmes.
The planing method of the electric automobile charging station of the prior art carries out electronic vapour using conventional particle colony optimization algorithm
The planning of vehicle charging station has many advantages, such as that easy-to-understand, iteration optimization speed is fast and adjustment parameter is few.But also there is following lack
Point: electric automobile charging station quantity K need it is artificial go to set, and setting value K and be not equal to optimal value K, it is optimal in order to obtain
K value need that optimization algorithm is run multiple times, occupy a large amount of runing time；Conventional particle colony optimization algorithm is vulnerable to weight and study
The influence of selecting predictors, the phenomenon that being easy to appear " Premature Convergence "；Conventional particle colony optimization algorithm is easily trapped into local optimum, leads
The case where causing the result of output not to be optimal solution, and convergence precision is not high.
In summary as can be seen that how quickly to determine charging station quantity optimal under current vehicle flow, to choose most
Excellent charging station site is current problem to be solved.
Summary of the invention
The object of the present invention is to provide a kind of Site planning method of electric automobile charging station, device, equipment and calculating
Machine readable storage medium storing program for executing determines optimal charging station quantity needs to solve the planing method of electric automobile charging station in the prior art
The problem of occupying a large amount of runing time.
In order to solve the above technical problems, the present invention provides a kind of Site planning method of electric automobile charging station, comprising: benefit
The initial location of the target charging station quantity and each charging station in default planning region is determined with pre-selection clustering algorithm；According to institute
The initial location of target charging pile quantity and each charging station is stated, determines the grain of linear decrease weight particle swarm algorithm respectively
Subnumber and each particle initial position；Utilize the linear decrease weight particle swarm algorithm and each particle initial position
Whole society's cost determines the target site of each charging station.
Preferably, described using the determining target charging station quantity preset in planning region of pre-selection clustering algorithm and each
The initial location of charging station includes:
Target charging station quantity and each charging station in the default planning region are determined using K- means clustering algorithm
Initial location.
Preferably, it is described using K- means clustering algorithm determine target charging station quantity in the default planning region and
The initial location of each charging station includes:
According to the traffic web frame and network of communication lines charge requirement point information in the default planning region, determine described default
The maximum quantity and minimum number of settable charging station in planning region；
Respectively using each charging station quantity in the minimum number to the maximum quantity as cluster numbers K value, input
In the K- means clustering algorithm, it is randomly assigned the position of K cluster centre point；
Circulation, which executes, calculates separately in the default planning region each charge requirement point to the K cluster centre point
Each charge requirement point is referred to and updates the K cluster with rear in the smallest cluster of its Euclidean distance by Euclidean distance
The operation of the position of central point, until the position of the K cluster centre point is no longer changed；
According to the Dai Weisenbaoding index of each charging station quantity, the target charging station quantity and described each is determined
The initial location of a charging station.
Preferably, the Dai Weisenbaoding index according to each charging station quantity, determines the target charging station
The initial location of quantity and each charging station includes:
The corresponding charging station quantity of the smallest Dai Weisenbaoding index is selected, as the target charging station quantity, and really
The initial location of fixed each charging station.
Preferably, described to utilize the complete of the linear decrease weight particle swarm algorithm and each particle initial position
Social cost determines that the target site of each charging station includes:
Set described for whole society's cost of each particle initial position of the linear decrease weight particle swarm algorithm
The minimum value in the individual adaptive optimal control value is set group's adaptive optimal control value by the individual adaptive optimal control value of each particle；
Iteration updates the exploration speed of each particle, recycles updated exploration speed and updates each grain
Sub- current location, and record the adaptive value of each particle current location；
According to default decision condition, judge whether the adaptive value of current particle in current cycle time is less than the current grain
The individual adaptive optimal control value of son updates the individual adaptive optimal control value of the current particle if being less than；
Judge whether the adaptive value of each particle described in the current cycle time is less than group's adaptive optimal control value,
If being less than, group's adaptive optimal control value is updated；
Whether the number for judging that the exploration speed iteration updates reaches default the number of iterations, if reaching the default iteration
Number then exports the individual adaptive optimal control value and group's adaptive optimal control value for completing to update；
According to the individual adaptive optimal control value and group's adaptive optimal control value, the Target Station of each charging station is determined
Location and target whole society totle drilling cost.
The present invention also provides a kind of siteselecting planning devices of electric automobile charging station, comprising:
Charging station quantity determining module, for determining the target charging station in default planning region using pre-selection clustering algorithm
The initial location of quantity and each charging station；
Population determining module, for the initial location according to the target charging pile quantity and each charging station,
The population of determining linear decrease weight particle swarm algorithm and each particle initial position respectively；
Target site determining module, for initial using the linear decrease weight particle swarm algorithm and each particle
Whole society's cost of position determines the target site of each charging station.
Preferably, the charging station quantity determining module is specifically used for:
Target charging station quantity and each charging station in the default planning region are determined using K- means clustering algorithm
Initial location.
Preferably, the charging station quantity determining module includes:
Quantitative range determination unit, for according to the traffic web frame and network of communication lines charging need in the default planning region
Information is sought, determines the maximum quantity and minimum number of settable charging station in the default planning region；
Allocation unit, for respectively using each charging station quantity in the minimum number to the maximum quantity as poly-
Class number K value, inputs in the K- means clustering algorithm, is randomly assigned the position of K cluster centre point；
Cycling element calculates separately in the default planning region each charge requirement point to the K for recycling to execute
The Euclidean distance of a cluster centre point, by each charge requirement point be referred to in the smallest cluster of its Euclidean distance after more
The operation of the position of the new K cluster centre point, until the position of the K cluster centre point is no longer changed；
Target charging station quantity determination unit, for the Dai Weisenbaoding index according to each charging station quantity, really
The initial location of fixed the target charging station quantity and each charging station.
The present invention also provides a kind of siteselecting planning equipment of electric automobile charging station, comprising:
Memory, for storing computer program；Processor realizes above-mentioned one kind when for executing the computer program
The step of Site planning method of electric automobile charging station.
The present invention also provides a kind of computer readable storage medium, meter is stored on the computer readable storage medium
Calculation machine program, the computer program realize a kind of Site planning method of above-mentioned electric automobile charging station when being executed by processor
The step of.
The Site planning method of electric automobile charging station provided by the present invention, need to only preselect clustering algorithm can quickly really
Determine charging station quantity optimal under current vehicle flow.And electric automobile charging station quantity needs artificial to go to set in the prior art
It is fixed, and the value being manually set does not wait one to be set to optimal charging station quantity, and charging station quantity optimal in order to obtain needs
Optimization algorithm is run multiple times, occupies a large amount of runing time.The default planning that the present invention is determined using the clustering algorithm of pre-selection
Optimal charging station quantity can be used as the population that linear recurrence subtracts weight example swarm optimization under vehicle flowrate in region；Determining
Initial location can be used as the initial position that the linear recurrence subtracts each particle of weight example swarm optimization.Using described linear
Whole society's cost of weight of successively decreasing particle swarm algorithm and each particle initial position, determines the target of each charging station
Site.Method provided by the present invention greatly reduces the runing time for determining optimal charging station quantity.And utilize linear decrease
Weight particle swarm algorithm determines the optimal site of charging station, solves conventional particle group's algorithm and is easy to appear " Premature Convergence ", office
The optimal problem in portion.
Detailed description of the invention
It, below will be to embodiment or existing for the clearer technical solution for illustrating the embodiment of the present invention or the prior art
Attached drawing needed in technical description is briefly described, it should be apparent that, the accompanying drawings in the following description is only this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the stream of the first specific embodiment of the Site planning method of electric automobile charging station provided by the present invention
Cheng Tu；
Fig. 2 is the stream of second of specific embodiment of the Site planning method of electric automobile charging station provided by the present invention
Cheng Tu；
Fig. 3 is the stream of the third specific embodiment of the Site planning method of electric automobile charging station provided by the present invention
Cheng Tu；
Fig. 4 is a kind of structural block diagram of the siteselecting planning device of electric automobile charging station provided in an embodiment of the present invention.
Specific embodiment
Core of the invention is to provide Site planning method, device, equipment and the calculating of a kind of electric automobile charging station
Machine readable storage medium storing program for executing quickly determines charging station quantity optimal under current vehicle flow using clustering algorithm, improves operation
Speed.
In order to enable those skilled in the art to better understand the solution of the present invention, with reference to the accompanying drawings and detailed description
The present invention is described in further detail.Obviously, described embodiments are only a part of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Under every other embodiment obtained, shall fall within the protection scope of the present invention.
Referring to FIG. 1, Fig. 1 be the Site planning method of electric automobile charging station provided by the present invention the first is specific
The flow chart of embodiment；Specific steps are as follows:
Step S101: the target charging station quantity and each charging in default planning region are determined using pre-selection clustering algorithm
The initial location stood；
Step S102: according to the initial location of the target charging pile quantity and each charging station, line is determined respectively
Property successively decrease weight particle swarm algorithm population and each particle initial position；
Step S103: the whole society of the linear decrease weight particle swarm algorithm and each particle initial position is utilized
Cost determines the target site of each charging station.
In order to which the method for solving electric automobile charging station planning in the prior art determines that optimal charging station quantity needs to occupy
The problem of a large amount of runing time and traditional PS O algorithm is easy to appear " Premature Convergence ", local optimum phenomenon, the present embodiment utilizes
Clustering algorithm determines the optimal value of charging station quantity；And it combines linear decrease weight particle swarm algorithm and determines the optimal of charging station
Site.
Based on the above embodiment, in the present embodiment, it is determined in the default planning region using K- means clustering algorithm
Target charging station quantity and each charging station initial location.Referring to FIG. 2, Fig. 2 is electric car provided by the present invention
The flow chart of second of specific embodiment of the Site planning method of charging station；Specific steps are as follows:
Step S201: according to the traffic web frame and network of communication lines charge requirement point information in the default planning region, really
The maximum quantity and minimum number of settable charging station in the fixed default planning region；
The maximum quantity of settable charging station and minimum number are respectively as follows: in the default planning region
Wherein, QtotalCharge requirement amount is concentrated for electric car in planning region；Respectively filled in charging station
The maximum of electric equipment permission, minimum number；SchFor separate unit charging equipment capacity.
Step S202: respectively using each charging station quantity in the minimum number to the maximum quantity as cluster numbers
K value inputs in the K- means clustering algorithm, is randomly assigned the position of K cluster centre point；
Circulation input Nch_minTo Nch_maxBetween number as cluster numbers K value, and be randomly assigned the position of K cluster centre
It sets.
Step S203: it is poly- to the K that circulation execution calculates separately each charge requirement point in the default planning region
Each charge requirement point is referred to and updates institute with rear in the smallest cluster of its Euclidean distance by the Euclidean distance of class central point
The operation of the position of K cluster centre point is stated, until the position of the K cluster centre point is no longer changed；
Calculate each charge requirement point to cluster centre point Euclidean distance
And each charge requirement point to be grouped into nearest cluster apart from minimum foundation；Wherein, Op is cluster centre point x1With
Charge requirement point x2Euclidean distance；
Utilize zhongxinK=((x1x+x2x+…xix)/i,(x1y+x2y+…xiy)/i) update K cluster centre position
It sets；Wherein, zhongxinKIt is K cluster centre point, xixFor the x coordinate in i-th of data in the data acquisition system, xiyFor the number
According to the y-coordinate in set in i-th of data；
Circulation executes above-mentioned steps, until the position of the K cluster centre point is no longer changed, that is, meets convergence item
Part.
Step S204: according to the Dai Weisenbaoding index of each charging station quantity, the target charging station number is determined
The initial location of amount and each charging station；
Calculate the Dai Weisenbaoding index D BI of each charging station quantity:
Wherein, DBI is the evaluation index of clustering algorithm, and the smaller then Clustering Effect of DBI is better,WithFor any two class
Class in average distance, WiAnd WjFor the cluster centre of two classifications.
Step S205: according to the initial location of the target charging pile quantity and each charging station, line is determined respectively
Property successively decrease weight particle swarm algorithm population and each particle initial position；
Obtain the Dai Weisenbaoding index D BI under each charging station quantity, and after circulation terminates with Dai Weisenbaoding index
DBI is foundation, finds out charging station quantity K corresponding when minimum Dai Weisenbaoding index D BI, as optimal required for us
Charging station quantity, the process we other than obtaining optimal charging station quantity K, also obtained the initial location of K charging station
And the charging pile quantity in each website.
Step S206: the whole society of the linear decrease weight particle swarm algorithm and each particle initial position is utilized
Cost determines target site and the target whole society totle drilling cost of each charging station.
In the present embodiment, the target charging station quantity in the default planning region is determined using K- means clustering algorithm
With the initial location of each charging station.The maximum quantity of settable charging station in the default planning region to minimum number is made
For cluster centre, cluster operation is carried out, until meeting the condition of convergence.The Dai Weisenbaoding index of each charging station quantity is calculated,
The corresponding charging station quantity K of minimum Dai Weisenbaoding index is chosen as optimal charging station quantity, and obtains K charging station
Charging pile quantity in initial location and each website.Optimal charging station number is obtained using linear decrease weight particle swarm algorithm
The totle drilling cost of optimal addressing and the whole society under amount.
Based on the above embodiment, in the present embodiment, it is calculated optimal charging station quantity K as linear decrease weight population
The population of method, using whole society's cost of the initial position of K particle as their own individual adaptive optimal control value, with individual
The smallest one is used as group's adaptive optimal control value in adaptive optimal control value, is changed using the linear decrease weight particle swarm algorithm
In generation, calculates, and obtains the target site for determining each charging station and target whole society totle drilling cost.Referring to FIG. 3, Fig. 3 is this hair
The flow chart of the third specific embodiment of the Site planning method of electric automobile charging station provided by bright；Concrete operation step
It is as follows:
Step S301: target charging station quantity in default planning region is determined using K- means clustering algorithm and each is filled
The initial location in power station；
Step S302: according to the initial location of the target charging pile quantity and each charging station, line is determined respectively
Property successively decrease weight particle swarm algorithm population and each particle initial position；
Step S303: whole society's cost of each particle initial position of the linear decrease weight particle swarm algorithm is set
It is set to the individual adaptive optimal control value of each particle, it is optimal to set group for the minimum value in the individual adaptive optimal control value
Adaptive value；
Step S304: iteration updates the exploration speed of each particle, recycles updated exploration speed and updates
Each particle current location, and record the adaptive value of each particle current location；
Each particle is first explored with an exploration speed v to surrounding, and during each iteration, explore speed
V can be withIt is updated；
Wherein, the number of iterations t, r1、r2For the arbitrary constant between [0,1]；X is the position of each particle；Pi, i=1,
2 ... .K is the individual adaptive optimal control value of each particle；GiFor group's adaptive optimal control value.
After each particle has updated position by the exploration speed, the adaptive value of each position can be write down, position is more
New function and adaptation value function.
Wherein, the location updating function is
The adaptation value function are as follows:
Wherein, C is overall society cost, C1iIt is the Installed capital cost (i=1,2 ..., K) of i-th of charging station, C2i is
The operation cost of i charging station, C3i are average annual cost depletions in automobile user to the road of i-th of charging station, and K is charging
It stands number, m is the number transformer of charging station, and F is the unit price of transformer, and a is the charger quantity of charging station, and B is charging station
Capital cost, r0For charging station discount rate, z is that charging station runs the time limit, and λ is proportionality coefficient, and L is automobile user to accordingly
Charging station distance, g is the operating range of electric car per unit electricity, and p is the charging electricity price of electric car.
Step S305: according to default decision condition, judge whether the adaptive value of current particle in current cycle time is less than
The individual adaptive optimal control value of the current particle updates the individual adaptive optimal control value of the current particle if being less than；
Step S306: judge whether the adaptive value of each particle described in the current cycle time is less than the group most
Excellent adaptive value updates group's adaptive optimal control value if being less than；
WithAs decision condition, if the t times i-th of particle adaptive value than it
Individual adaptive optimal control value wants small, then more new individual adaptive optimal control value, otherwise constant.If K particle group of the t times iteration is most
Group adaptive optimal control value of the excellent adaptive value than the t-1 times is small, then updates group's adaptive optimal control value, and record K charging station at this time
Position, it is otherwise constant.
Circulation executes step S304 to step S306, until meeting given number of iterations, exports group's adaptive optimal control
Value.
Step S307: whether the number for judging that the exploration speed iteration updates reaches default the number of iterations, if reaching institute
Default the number of iterations is stated, then exports the individual adaptive optimal control value and group's adaptive optimal control value for completing to update；
Step S308: according to the individual adaptive optimal control value and group's adaptive optimal control value, each charging is determined
The target site stood and target whole society totle drilling cost.
In the present embodiment, charging station number optimal under current vehicle flow can be quickly determined using K- means clustering algorithm
Amount；It solves and obtains optimal charging station quantity in the prior art and need that optimization algorithm is run multiple times, when occupying a large amount of operation
Between the problem of.And the present embodiment combination linear recurrence subtract weight example swarm optimization will the K- means clustering algorithm determine described in
Optimal charging station quantity in default planning region can be used as the population that the linear recurrence subtracts weight example swarm optimization；It utilizes
Whole society's cost of the linear decrease weight particle swarm algorithm and each particle initial position, determines each charging
The target site stood.Method provided by the present embodiment greatly reduces the runing time for determining optimal charging station quantity.And benefit
The optimal site that charging station is determined with linear decrease weight particle swarm algorithm solves conventional particle group's algorithm and is easy to appear " early
The problem of ripe convergence ", local optimum.
Referring to FIG. 4, Fig. 4 is a kind of knot of the siteselecting planning device of electric automobile charging station provided in an embodiment of the present invention
Structure block diagram；Specific device may include:
Charging station quantity determining module 100, for determining that the target in default planning region is filled using pre-selection clustering algorithm
The initial location of power station quantity and each charging station；
Population determining module 200, for the first initial station according to the target charging pile quantity and each charging station
Location, respectively determine linear decrease weight particle swarm algorithm population and each particle initial position；
Target site determining module 300, for utilizing the linear decrease weight particle swarm algorithm and each particle
Whole society's cost of initial position determines the target site of each charging station.
The siteselecting planning device of the electric automobile charging station of the present embodiment is for realizing electric automobile charging station above-mentioned
Site planning method, therefore visible hereinbefore electronic of specific embodiment in the siteselecting planning device of electric automobile charging station
The embodiment part of the Site planning method of vehicle charging station, for example, charging station quantity determining module 100, population determines mould
Block 200, target site determining module 300, is respectively used to step in the Site planning method for realizing above-mentioned electric automobile charging station
S101, S102 and S103, so, specific embodiment is referred to the description of corresponding various pieces embodiment, herein not
It repeats again.
The specific embodiment of the invention additionally provides a kind of siteselecting planning equipment of electric automobile charging station, comprising: memory,
For storing computer program；Processor realizes a kind of above-mentioned electric automobile charging station when for executing the computer program
Site planning method the step of.
The specific embodiment of the invention additionally provides a kind of computer readable storage medium, the computer readable storage medium
On be stored with computer program, the computer program realizes a kind of choosing of above-mentioned electric automobile charging station when being executed by processor
The step of location planing method.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with it is other
The difference of embodiment, same or similar part may refer to each other between each embodiment.For being filled disclosed in embodiment
For setting, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is referring to method part
Explanation.
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 step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
Above to the Site planning method of electric automobile charging station provided by the present invention, device, equipment and computer
Readable storage medium storing program for executing is described in detail.Specific case used herein carries out the principle of the present invention and embodiment
It illustrates, the above description of the embodiment is only used to help understand the method for the present invention and its core ideas.It should be pointed out that for this
For the those of ordinary skill of technical field, without departing from the principle of the present invention, the present invention can also be carried out several
Improvement and modification, these improvements and modifications also fall within the scope of protection of the claims of the present invention.
Claims (10)
1. a kind of Site planning method of electric automobile charging station characterized by comprising
The initial location of the target charging station quantity and each charging station in default planning region is determined using pre-selection clustering algorithm；
According to the initial location of the target charging pile quantity and each charging station, linear decrease weight particle is determined respectively
The population of group's algorithm and each particle initial position；
Using whole society's cost of the linear decrease weight particle swarm algorithm and each particle initial position, determine described in
The target site of each charging station.
2. Site planning method as described in claim 1, which is characterized in that the benefit is determined default using pre-selection clustering algorithm
The initial location of target charging station quantity and each charging station in planning region includes:
Using K- means clustering algorithm determine target charging station quantity in the default planning region and each charging station just
Initial station location.
3. Site planning method as claimed in claim 2, which is characterized in that described in the utilization K- means clustering algorithm determines
The initial location of target charging station quantity and each charging station in default planning region includes:
According to the traffic web frame and network of communication lines charge requirement point information in the default planning region, the default planning is determined
The maximum quantity and minimum number of settable charging station in region；
Respectively using each charging station quantity in the minimum number to the maximum quantity as cluster numbers K value, described in input
In K- means clustering algorithm, it is randomly assigned the position of K cluster centre point；
Circulation, which executes, calculates separately in the default planning region each charge requirement point to the European of the K cluster centre point
Each charge requirement point is referred to and updates the K cluster centre with rear in the smallest cluster of its Euclidean distance by distance
The operation of the position of point, until the position of the K cluster centre point is no longer changed；
According to the Dai Weisenbaoding index of each charging station quantity, determines the target charging station quantity and described each fill
The initial location in power station.
4. Site planning method as claimed in claim 3, which is characterized in that the wearing according to each charging station quantity
Wei Senbaoding index determines that the initial location of the target charging station quantity and each charging station includes:
The corresponding charging station quantity of the smallest Dai Weisenbaoding index is selected, as the target charging station quantity, and determines institute
State the initial location of each charging station.
5. Site planning method as claimed in claim 4, which is characterized in that described to utilize the linear decrease weight population
Whole society's cost of algorithm and each particle initial position determines that the target site of each charging station includes:
Set described each for whole society's cost of each particle initial position of the linear decrease weight particle swarm algorithm
The minimum value in the individual adaptive optimal control value is set group's adaptive optimal control value by the individual adaptive optimal control value of particle；
Iteration updates the exploration speed of each particle, recycles updated exploration speed update each particle and works as
Front position, and record the adaptive value of each particle current location；
According to default decision condition, judge whether the adaptive value of current particle in current cycle time is less than the current particle
Individual adaptive optimal control value updates the individual adaptive optimal control value of the current particle if being less than；
Judge whether the adaptive value of each particle described in the current cycle time is less than group's adaptive optimal control value, if small
In then updating group's adaptive optimal control value；
Whether the number for judging that the exploration speed iteration updates reaches default the number of iterations, if reaching the default iteration time
Number then exports the individual adaptive optimal control value and group's adaptive optimal control value for completing to update；
According to the individual adaptive optimal control value and group's adaptive optimal control value, determine each charging station target site and
Target whole society totle drilling cost.
6. a kind of siteselecting planning device of electric automobile charging station characterized by comprising
Charging station quantity determining module, for determining the target charging station quantity in default planning region using pre-selection clustering algorithm
With the initial location of each charging station；
Population determining module, for the initial location according to the target charging pile quantity and each charging station, respectively
Determine linear decrease weight particle swarm algorithm population and each particle initial position；
Target site determining module, for utilizing the linear decrease weight particle swarm algorithm and each particle initial position
Whole society's cost, determine the target site of each charging station.
7. siteselecting planning device as claimed in claim 6, which is characterized in that the charging station quantity determining module is specifically used
In:
Using K- means clustering algorithm determine target charging station quantity in the default planning region and each charging station just
Initial station location.
8. siteselecting planning device as claimed in claim 7, which is characterized in that the charging station quantity determining module includes:
Quantitative range determination unit, for according to the traffic web frame and network of communication lines charge requirement point in the default planning region
Information determines the maximum quantity and minimum number of settable charging station in the default planning region；
Allocation unit, for respectively using each charging station quantity in the minimum number to the maximum quantity as cluster numbers
K value inputs in the K- means clustering algorithm, is randomly assigned the position of K cluster centre point；
It is poly- to the K to calculate separately each charge requirement point in the default planning region for circulation execution for cycling element
Each charge requirement point is referred to and updates institute with rear in the smallest cluster of its Euclidean distance by the Euclidean distance of class central point
The operation of the position of K cluster centre point is stated, until the position of the K cluster centre point is no longer changed；
Target charging station quantity determination unit determines institute for the Dai Weisenbaoding index according to each charging station quantity
State the initial location of target charging station quantity and each charging station.
9. a kind of siteselecting planning equipment of electric automobile charging station characterized by comprising
Memory, for storing computer program；
Processor realizes that a kind of electric car as described in any one of claim 1 to 5 fills when for executing the computer program
The step of Site planning method in power station.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program realizes a kind of electric car charging as described in any one of claim 1 to 5 when the computer program is executed by processor
The step of Site planning method stood.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910372063.2A CN110111001B (en) | 2019-05-06 | 2019-05-06 | Site selection planning method, device and equipment for electric vehicle charging station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910372063.2A CN110111001B (en) | 2019-05-06 | 2019-05-06 | Site selection planning method, device and equipment for electric vehicle charging station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110111001A true CN110111001A (en) | 2019-08-09 |
| CN110111001B CN110111001B (en) | 2023-07-28 |
Family
ID=67488404
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910372063.2A Active CN110111001B (en) | 2019-05-06 | 2019-05-06 | Site selection planning method, device and equipment for electric vehicle charging station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110111001B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110705864A (en) * | 2019-09-27 | 2020-01-17 | 广东电网有限责任公司 | Site selection and volume fixing method for charging station |
| CN110930037A (en) * | 2019-11-27 | 2020-03-27 | 国网四川省电力公司天府新区供电公司 | Coordination planning method for electric taxi charging station and power distribution facility |
| CN117557069A (en) * | 2024-01-10 | 2024-02-13 | 长峡数字能源科技(湖北)有限公司 | Charging pile site selection method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951978A (en) * | 2017-02-20 | 2017-07-14 | 国网天津市电力公司 | A kind of city concentrated charging station planing method based on improvement K means algorithms |
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| US20180240047A1 (en) * | 2015-08-19 | 2018-08-23 | Tianjin University | Planning method of electric vehicle fast charging stations on the expressway |
| CN108460487A (en) * | 2018-03-07 | 2018-08-28 | 国网江苏省电力有限公司无锡供电分公司 | Electric vehicle rapid charging station Optimizing Site Selection constant volume method based on APSO algorithms |
| CN108805321A (en) * | 2017-05-02 | 2018-11-13 | 南京理工大学 | A kind of electric automobile charging station planing method |
-
2019
- 2019-05-06 CN CN201910372063.2A patent/CN110111001B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180240047A1 (en) * | 2015-08-19 | 2018-08-23 | Tianjin University | Planning method of electric vehicle fast charging stations on the expressway |
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| CN106951978A (en) * | 2017-02-20 | 2017-07-14 | 国网天津市电力公司 | A kind of city concentrated charging station planing method based on improvement K means algorithms |
| CN108805321A (en) * | 2017-05-02 | 2018-11-13 | 南京理工大学 | A kind of electric automobile charging station planing method |
| CN108460487A (en) * | 2018-03-07 | 2018-08-28 | 国网江苏省电力有限公司无锡供电分公司 | Electric vehicle rapid charging station Optimizing Site Selection constant volume method based on APSO algorithms |
Non-Patent Citations (8)
| Title |
|---|
| 周玉光等: "改进粒子群优化算法及其在4G网络基站选址中的应用", 《广东工业大学学报》, no. 02, pages 73 - 77 * |
| 张卫明等: "基于改进粒子群算法的野战油库选址优化", 《兵器装备工程学报》 * |
| 张卫明等: "基于改进粒子群算法的野战油库选址优化", 《兵器装备工程学报》, no. 08, 25 August 2016 (2016-08-25), pages 105 - 110 * |
| 李慧媛等: "改进粒子群算法的电动汽车时空优化分配策略", 《安徽工程大学学报》 * |
| 李慧媛等: "改进粒子群算法的电动汽车时空优化分配策略", 《安徽工程大学学报》, no. 04, 15 December 2014 (2014-12-15), pages 52 - 55 * |
| 杜欣慧等: "基于LD-SAPSO的分布式电源选址和定容", 《电测与仪表》 * |
| 杜欣慧等: "基于LD-SAPSO的分布式电源选址和定容", 《电测与仪表》, no. 07, 10 April 2015 (2015-04-10), pages 118 - 122 * |
| 齐琳;姚俭;王心月;: "基于改进粒子群算法的电动汽车充电站布局优化", 公路交通科技, no. 06, pages 140 - 147 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110705864A (en) * | 2019-09-27 | 2020-01-17 | 广东电网有限责任公司 | Site selection and volume fixing method for charging station |
| CN110705864B (en) * | 2019-09-27 | 2022-05-17 | 广东电网有限责任公司 | Site selection and volume fixing method for charging station |
| CN110930037A (en) * | 2019-11-27 | 2020-03-27 | 国网四川省电力公司天府新区供电公司 | Coordination planning method for electric taxi charging station and power distribution facility |
| CN117557069A (en) * | 2024-01-10 | 2024-02-13 | 长峡数字能源科技(湖北)有限公司 | Charging pile site selection method and system |
| CN117557069B (en) * | 2024-01-10 | 2024-03-26 | 长峡数字能源科技(湖北)有限公司 | Charging pile address selection method and system, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110111001B (en) | 2023-07-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109711630A (en) | A kind of electric car fast charge station addressing constant volume method based on trip probability matrix | |
| CN110111001A (en) | A kind of Site planning method of electric automobile charging station, device and equipment | |
| CN106203720B (en) | A kind of schedulable capacity prediction methods of Multiple Time Scales electric car cluster | |
| CN108062601A (en) | A kind of parking lot Dynamic Pricing method and apparatus | |
| CN105976108A (en) | Distributed energy storage planning method of power distribution network | |
| CN108764634A (en) | A kind of electric automobile charging station dynamic programming method for considering charge requirement and increasing | |
| CN107719164A (en) | The orderly charging method of residential block electric automobile based on TOPSIS sequences | |
| CN110046986A (en) | The overdue customer grouping method and device of loaning bill based on big data | |
| CN109741177A (en) | Appraisal procedure, device and the intelligent terminal of user credit | |
| CN109788489A (en) | A kind of base station planning method and device | |
| CN109754168A (en) | Charging station site selecting method and device | |
| CN109615260A (en) | The method for determining the installation addresses of charging pile | |
| Yao et al. | Allocation method for transit lines considering the user equilibrium for operators | |
| CN107566133A (en) | A kind of resource billing method, device and platform | |
| CN107527071A (en) | A kind of sorting technique and device that k nearest neighbor is obscured based on flower pollination algorithm optimization | |
| CN108876076A (en) | The personal credit methods of marking and device of data based on instruction | |
| CN106250933A (en) | Method, system and the FPGA processor of data clusters based on FPGA | |
| CN107958314A (en) | A kind of electric automobile fast charge station charging service takes pricing method and device | |
| CN109066660A (en) | A kind of power distribution network congestion management and decreasing loss method and apparatus based on optimal reconfiguration | |
| CN106650930A (en) | Model parameter optimizing method and device | |
| CN109086900A (en) | Power Material guarantee and deployment platform based on multi-objective particle | |
| CN109445906A (en) | A kind of virtual machine quantity required prediction technique | |
| CN107886717A (en) | A kind of parking induction method, device, computing device and storage medium | |
| CN108985518A (en) | A kind of traffic congestion charging policy optimization method based on VISSIM emulation | |
| CN112036598A (en) | Charging pile use information prediction method based on multi-information coupling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |