CN109685251A - A kind of electronic facility charging station Optimization Method for Location-Selection, device and storage medium - Google Patents
A kind of electronic facility charging station Optimization Method for Location-Selection, device and storage medium Download PDFInfo
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- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
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Abstract
The invention discloses a kind of electronic facility charging station Optimization Method for Location-Selection, device and storage mediums, belong to electronic facility charging station plan optimization field, method is comprising steps of be based on multiple constraint conditions, electronic facility charging station location optimization model is established, multiple constraint conditions include at least cost constraint, charging station capacity constraints and coverage constraint condition；Electronic facility charging station location optimization model is solved using genetic algorithm.The embodiment of the present invention is able to solve the location problem of electronic facility charging station, while guaranteeing to minimize cost, and coverage area is made to be extended to whole region to meet the needs of driver and convenience.
Description
Technical field
The present invention relates to electronic facility charging station plan optimization field, in particular to a kind of electronic based on genetic algorithm sets
Apply charging station Optimization Method for Location-Selection and device.
Background technique
The world today, energy and environment problem become increasingly conspicuous, the shortage of traditional fuel energy and caused by environment
Harm, so that people have turned one's attention to new direction, i.e. new energy field.New energy development utilization not only improves the mankind's
Energy resource structure, and meet the long-term goal of sustainable development.Electric car as a kind of energy-saving and environment-friendly new traffic tool,
It has been increasingly becoming countries in the world focus of attention, has been the inexorable trend of development of automobile industry.Advanced electronic facility technology is
The key of national economy and living standards of the people is improved through becoming.
Important component of the electric car as new energy strategy and smart grid, it is necessary to be realized with other field common
Coordinated development.In regular period, battery higher cost may become the restraining factors of Development of Electric Vehicles, but can seek
Preferred solution is to reduce user cost, to improve the occupation rate of market of electric car.In other words, electric car is necessary
There is matched charging station to service for it.For the bottleneck for breaking through Development of Electric Vehicles, the purchase enthusiasm of the masses is improved, is established rapidly
A batch is extremely urgent for the charging station that automobile user uses.
Currently, most of influences about the research of electrically-charging equipment all to the operation of charging station to power grid are related, charging station
Addressing Consideration is more single, fails to comprehensively consider economic cost, charging station capacity, coverage area and convenience for users etc.
Multiple factors, inconvenient, the unpractical problem that causes in charging station addressing that there are automobile user chargings.
Summary of the invention
In view of this, the embodiment of the present invention proposes the Optimizing Site Selection method, apparatus and storage of a kind of electronic facility charging station
Medium, is able to solve the location problem of electronic facility charging station, while guaranteeing to minimize cost, and it is whole to be extended to coverage area
A region meets the needs of driver and convenience.
Technical solution provided in an embodiment of the present invention is as follows:
In a first aspect, providing a kind of electronic facility charging station Optimization Method for Location-Selection, include the following steps:
Based on multiple constraint conditions, electronic facility charging station location optimization model is established, the multiple constraint condition is at least
Including cost constraint, charging station capacity constraints and coverage constraint condition；
The electronic facility charging station location optimization model is solved using genetic algorithm.
In some embodiments, the electronic facility charging station location optimization model are as follows:
Wherein, I={ 1 ..., m } is the demand point set of residential area；
J=1 ..., and n } it is possible charging station set；
fjIndicate the cost of investment of charging station j；
dijIndicate the transportation cost of demand point i to charging station j；
xjIndicate the binary variable of charging station j；
zijIndicate the binary variable of distribution requirements point i to charging station j；
XiIndicate the demand of demand point i；
cjIndicate the capacity of charging station j；
C indicates economic cost；
It indicates to be the Website Hosting within β Q with charging station j distance；
β indicates that Variable Factors, β are greater than 0 and are less than or equal to 1；
The distance of D (j, k) expression charging station j to charging station k；
Q indicates the enforcement distance in the fully charged electrically-charging equipment of charging station；
l0Indicate the residual flow that do not used up by figure, l0≥0；
yklIt indicates from charging station k to the magnitude of traffic flow of charging station l；
ylpIt indicates from charging station l to the magnitude of traffic flow of charging station p；
The length of v expression V；
V indicates the set for being able to satisfy the charging station of driver demand；
E indicates the side between charging station.
In some embodiments, described that the electronic facility charging station location optimization model packet is solved using genetic algorithm
It includes:
S1, possible multiple charging stations and demand point data are imported, and genetic algorithm parameter is set；
S2, chromosome coding is carried out to the multiple charging station, is formed initial population R (t), evolutionary generation t=0；
S3, calculate the population R (t) each chromosome fitness function value, and according to fitness function value to R
(t) it is screened, is generated parent population S (t)；
S4, clonal propagation is carried out to the parent population S (t), is formed population T (t), and to every in the population T (t)
A chiasma variation, forms population T'(t)；
S5, the population R (t) and the population T'(t are calculated) and each chromosome of concentration fitness function value；
Described in S6, calculating and the select probability of each chromosome concentrated, and according to the select probability selective staining body,
It generates population R (t+1)；
S7, judge whether the current iteration number t of genetic algorithm reaches maximum number of iterations, if not up to, enabling t=t+
1, and the step S3 is jumped to, otherwise, output is used to indicate the result of optimal charging station addressing.
In some embodiments, chromosome coding is carried out to the multiple charging station in the step S2, forms population R
(t), comprising:
The multiple charging station is numbered and is sorted using binary coding, and presses collating sequence for each charging
A gene of the website as chromosome.
In some embodiments, it makes a variation, is formed to each chiasma in the population T (t) in the step S4
Population T'(t) include:
Each chromosome in the population T (t) is performed the following operations:
Judge whether the chromosome meets the cost constraint；
If not satisfied, then selecting two at random from the gene that the value of the chromosome is 0 is set as 1；
It is negated if satisfied, then selecting two at random from the full gene of the chromosome.
In some embodiments, the fitness function value of the chromosome is calculated using following chromosome fitness function
It obtains:
Wherein, guChromosome is represented, B is one big number, guarantees F (gu) value be positive number.
In some embodiments, the select probability of the chromosome is calculated using following select probability calculation formula
It arrives:
Wherein, M (gu) indicate select probability function, density (gu) it is antibody concentration function.
Second aspect provides a kind of dress applied to electronic facility charging station Optimization Method for Location-Selection described in first aspect
It sets, described device includes:
Model building module, it is described for establishing electronic facility charging station location optimization model based on multiple constraint conditions
Multiple constraint conditions include at least cost constraint, charging station capacity constraints and coverage constraint condition；
Model solution module, for solving the electronic facility charging station location optimization model using genetic algorithm.
The third aspect provides a kind of electronic facility charging station addressing optimization device, comprising:
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processing
Device realizes electronic facility charging station Optimization Method for Location-Selection as described in relation to the first aspect.
Fourth aspect provides a kind of computer readable storage medium, is stored thereon with computer program, and feature exists
In the electronic facility charging station Optimization Method for Location-Selection of realization as described in relation to the first aspect when described program is executed by processor.
The beneficial effect of the embodiment of the present invention compared to existing technologies is, by being based on multiple constraint conditions, establishes
Electronic facility charging station location optimization model, multiple constraint conditions include at least cost constraint, charging station capacity-constrained item
Part and coverage constraint condition, and optimization problem is solved using genetic algorithm, thus solve the choosing of electronic facility charging station
Location problem, while guaranteeing to minimize cost, and coverage area is made to be extended to whole region to meet the needs of driver and convenience.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is a kind of flow chart of electronic facility charging station Optimization Method for Location-Selection provided in an embodiment of the present invention；
Fig. 2 is the schematic diagram of the original flow of charging station provided in an embodiment of the present invention；
Fig. 3 is the stream provided in an embodiment of the present invention that electronic facility charging station location optimization model is solved using genetic algorithm
Cheng Tu；
Fig. 4 is demand point provided in an embodiment of the present invention and possible multiple charging station distribution maps；
Fig. 5 is the distribution map of the corresponding charging station being selected and demand point as β=0.9；
Fig. 6 is the distribution map of the corresponding charging station being selected and demand point as β=0.8；
Fig. 7 is simulation result contrast schematic diagram when β takes different value；
Target function value and selected charging station quantity result schematic diagram of the Fig. 8 for cost constraint；
Fig. 9 is the structural block diagram that a kind of electronic facility charging station addressing provided in an embodiment of the present invention optimizes device.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached in the embodiment of the present invention
Figure, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only this
Invention a part of the embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art exist
Every other embodiment obtained under the premise of creative work is not made, shall fall within the protection scope of the present invention.
For the location problem for solving electronic facility charging station, the embodiment of the present invention provides a kind of electronic facility charging station addressing
Optimization method, by establishing electronic facility charging station location optimization model, it is intended to make cost of investment and communications and transportation cost minimization,
Meet multiple restrictive conditions such as charging station capacity, coverage and convenience simultaneously, and optimization problem is solved using genetic algorithm.
It is understood that method provided in an embodiment of the present invention can be applied to the vehicles bus station positions such as mobile trip or electric vehicle fills
Electric bus station position, further, it is also possible to be applied to the bus station position under other scenes.
Embodiment one
Fig. 1 is a kind of flow chart of electronic facility charging station Optimization Method for Location-Selection provided in an embodiment of the present invention, such as Fig. 1 institute
Show, this method specifically includes:
101, multiple constraint conditions are based on, establish electronic facility charging station location optimization model, wherein multiple constraint conditions
Including at least cost constraint, charging station capacity constraints and coverage constraint condition.
In the present embodiment, each constraint condition of multiple constraint conditions is according to excellent with electronic facility charging station addressing
The relevant multiple parameters of change problem are defined.
Multiple parameters are defined as follows:
I={ 1 ..., m } indicates the demand point set of residential area；
J=1 ..., and n } indicate possible charging station set；
fjIndicate the cost of investment of charging station j；
dijIndicate the transportation cost of demand point i to charging station j；
xjIndicate the binary variable of charging station j；
zijIndicate the binary variable of distribution requirements point i to charging station j；
XiIndicate the demand of demand point i；
cjIndicate the capacity of charging station j；
C indicates economic cost；
It indicates to be the Website Hosting within β Q with charging station j distance；
β indicates that Variable Factors, β are greater than 0 and are less than or equal to 1；
The distance of D (j, k) expression charging station j to charging station k；
Q indicates the enforcement distance in the fully charged electrically-charging equipment of charging station；
l0Indicate the residual flow that do not used up by figure, l0≥0；
yklIt indicates from charging station k to the magnitude of traffic flow of charging station l；
ylpIt indicates from charging station l to the magnitude of traffic flow of charging station p；
The length of v expression V；
V indicates the set for being able to satisfy the charging station of driver demand；
E indicates the side between charging station.
Cost constraint, charging station capacity constraints and coverage constraint condition are defined, is specifically included:
1. cost constraint: the foundation of charging station is heavily dependent on initial cost of investment, in addition, driver
Transportation cost is also one and has to the factor considered.Therefore, the objective function of cost constraint is defined as follows:
If charging station is established, xjNon-zero.If demand point i is electrically charged station j service, zijNon-zero.
2. charging station capacity constraints: for each j,
It allowsIt indicates to be the Website Hosting within β Q with charging station j distance.For each charging station, need to guarantee to charge
Capacity of standing meets total charge requirement, therefore,
It can guarantee that electrically-charging equipment can be again charged up within distance beta Q in this way.Wherein, β is a factor,
Its maximum value is 1.β value is bigger, and location optimization model is not guarded, it is meant that less charging station needs are arranged.
Above-mentioned equation (6) guarantees demand point i only by a charging station service, and equation (7) shows that charging station is only established
Service can be provided.
3. coverage constraint condition: being based on charging station capacity constraints, take out figure H=(V, E), V indicates energy here
Meet the set of the charging station of driver demand, E indicates the side between charging station.In order to meet the convenience of driver, one electronic
Facility can access any one charging station in its ability, so that electrically-charging equipment once charges and can travel to area
It, in the specific implementation process, can be using based on mixed integer linear programming model from anywhere in domain.
Mixed integer linear programming model is illustrated below with reference to Fig. 2, the original flow of charging station shown in Fig. 2
Schematic diagram, in FIG. 2, it is assumed that original site has 9 unit of flow, driver accesses charging station 1, if he wants to go to position 9, directly
It is impossible for taking over too far, but finally can be achieved on step by step in constraint by intermediate stations, about
Beam ensure that electrically-charging equipment once charge can travel to region from anywhere in, the distance at every two station is in course continuation mileage
Interior and these websites are to establish, and the concept that flow is employed herein constrains, to ensure that the convenience of driver.
If do not connected in feasible distance beta Q between website, the flow from origin site will not be consumed, i.e.,
Electrically-charging equipment can reach other websites and be charged again.Based on capacity-constrained, coverage constraint definition is as follows:
Above-mentioned equation (8) indicates not by the flow of figure consumption and from original site flow and is v, equation (9)
Indicate flow from origin site and by the flow that website absorbs be it is equal, formula (10) constrains flow and can only be stood by one
Consumption, formula (11) guarantee that the total flow at one station of outflow is equal to the total flow for flowing into a station plus the flow at a station.
About according to above-mentioned cost constraint, charging station capacity constraints and coverage constraint condition, it establishes electronic
Facility charging station location optimization model is as follows:
102, electronic facility charging station location optimization model is solved using genetic algorithm.
In the present embodiment, electronic facility charging station location optimization model is solved using genetic algorithm, it can be by the model
Solution regard chromosome as.As shown in figure 3, may include step:
S1, possible multiple charging stations and demand point data are imported, and genetic algorithm parameter is set.
S2, chromosome coding is carried out to multiple charging stations, is formed initial population R (t), evolutionary generation t=0.
In the present embodiment, possible multiple charging stations are numbered and are sorted using binary coding, and by row
Sequence sequence is using each charging station as a gene of chromosome, when genic value is 1, indicates that corresponding charging station is selected
In；When genic value is 0, indicate that corresponding charging station is not selected, it is hereby achieved that multiple with different genes value
Chromosome, each chromosome in multiple chromosome end indicate that a kind of siteselecting planning scheme, the length of chromosome are equal to charging
Multiple chromosome is generated group R (t) by the number stood.For example, to 7 websites and chromosome g in some networkuFor
When { 1010101 }, illustrate that the program is will to number the charging station for being the 1st, 3,5, No. 7 to be selected as charging station.
S3, calculate population R (t) each chromosome fitness function value, and according to fitness function value to R (t) into
Row screening, generates parent population S (t).
Wherein, chromosome fitness is codetermined by economic cost, capacity and coverage, wherein economic cost formula (1)
In C indicate, value is smaller to illustrate that cost is lower, and economy is better；Capacity is usedIt indicates, generation
Table charging station capacity must satisfy total charge requirement；Coverage l0>=0 indicates.Due to the corresponding dye in the optimization direction of objective function
The increased direction of colour solid fitness, therefore shown in the fitness function of chromosome such as formula (13):
Wherein, the B in the formula (13) is one big number, guarantees wherein F (gu) value be positive；It can be seen from formula (13)
Only in the case where meeting capacity and spreadability constrains, chromosome fitness just has value, and otherwise its value is 0.
In the present embodiment, the suitable of each chromosome of population R (t) can be calculated by the fitness function of above-mentioned chromosome
Response functional value, and according to the fitness function value of each chromosome, R (t) carries out descending sort, n before selectingsA chromosome is raw
At parent population S (t).
S4, clonal propagation is carried out to parent population S (t), is formed population T (t), and to each chromosome in population T (t)
Cross and variation forms population T'(t).
Wherein, clonal propagation is carried out to parent population S (t), is formed population T (t), which can be with are as follows:
Each chromosome in parent population S (t) is cloned, ghost, constitute filial generation intersection, parent is dyed
Body equal proportion clone, generates population T (t), and population T (t) scale is identical as initial population R (t) scale.
To each chiasma variation in population T (t), population T'(t is formed), which can be with are as follows:
It is made a variation to each chromosome in population T (t) using two o'clock, judges whether the chromosome meets cost constraint item
Part, if not satisfied, then selecting two at random from the gene that the value of chromosome is 0 is set as 1, if satisfied, then from chromosome
Two are selected in full gene at random to be negated.
S5, population R (t) and population T'(t are calculated) and each chromosome of concentration fitness function value.
In the present embodiment, population R (t) and population can be calculated by the fitness function formula (13) of above-mentioned chromosome
T'(t) and each chromosome of concentration fitness function value.
The select probability of each chromosome of S6, calculating and concentration, and according to select probability selective staining body, generate population
R(t+1)。
In the present embodiment, in selective staining body, need to guarantee that outstanding dyeing physical efficiency is selected with biggish select probability,
Therefore the select probability of chromosome is calculated using following select probability calculation formula:
Wherein, M (gu) indicate select probability function, density (gu) it is antibody concentration function.
The select probability of each chromosome indicates that the chromosome is selected as general in next-generation group in current group
Rate, as soon as the select probability of chromosome is bigger, the probability selected is also bigger.
S7, judge whether the current iteration number t of genetic algorithm reaches maximum number of iterations, if not up to, enabling t=t+
1, and otherwise the S3 that gos to step executes step S8.
In the present embodiment, the current iteration number t of genetic algorithm is judged, if the genetic algorithm is not up to pre-
If maximum number of iterations, regard the population R (t+1) of a new generation as current population, and return step S3, repeat step
S3 to step S6 stops the chromosome for calculating, and the calculated result of genetic algorithm preferably being gone out until reaching maximum number of iterations
As optimal charging station addressing.
S8, output are used to indicate the result of optimal charging station addressing.
Technical solution provided in an embodiment of the present invention is described further below in conjunction with emulation testing, and confirms this
Method provided by inventive embodiments has preferable validity, practicability.
According to investigation and research, the demand of cell be it is relevant with the size of population, transportation cost with user to charge
The distance stood increases and increases.In first emulation testing, 40 demand points of random distribution and 10 possible charging stations exist
60×60km2Place, distribution 30~50 random number give demand Xi, and by Q, fjAnd cjIt is respectively set to 30km, 4,500,000
It is first and 400 daily.Demand point and possible multiple charging station distribution maps are as shown in Figure 4, wherein star symbol represents possibility
Charging station, circle symbol represents demand point.
Genetic algorithm parameter is set, it is assumed that Population in Genetic Algorithms scale takes 100, and maximum evolutionary generation takes 400, mutation probability
0.1 is taken, crossover probability 0.6.
By changing Variable Factors β (β is allowed to be respectively 0.9 and 0.8), Fig. 5 is seen in the site and demand point distribution map being selected
And Fig. 6, Fig. 4 are the distribution map of the corresponding charging station being selected and demand point as β=0.9, Fig. 5 is as β=0.8 pair
The distribution map of the charging station being selected and demand point answered, wherein star symbol represents possible charging station, circle symbol
Represent demand point.
It can intuitively find out that electronic setting charging station Optimization Method for Location-Selection provided in an embodiment of the present invention is from Fig. 5 and Fig. 6
Practical.When capacity-constrained is satisfied, each electronic facility can access a charging station.One electronic facility can be with
It is linked into any one charging station made in limit of power of being expert at, once such driver charge can reach any in region
Place.
Due to the effect of contraction of formula (5), when β is reduced, selected charging station quantity increases.When β is configured to 0.9
When 5 charging stations it is selected.When β is configured to 0.8,6 charging stations are selected.
Next, verifying method provided in an embodiment of the present invention by three groups of experiments, genetic algorithm parameter is set, it is assumed that
Population in Genetic Algorithms scale takes 100, and maximum evolutionary generation takes 400, and mutation probability takes 0.1, and crossover probability 0.6 changes variable
Factor-beta (β is allowed to be respectively 0.7,0.8 and 0.9) available β as shown in Figure 7 takes simulation result comparison signal when different value
Figure.As seen from Figure 7, when β takes different values, the chromosome fitness of genetic algorithm is in initial stage rapid increase of evolving, and
Convergence can be reached in limited evolutionary generation, illustrate that method provided in an embodiment of the present invention is had under based on various boundary conditions
Preferable performance has higher flexibility；When β reduces, cost be will increase because fitness function value can be reduced.
Further, it in order to verify the scalability of this method, is emulated over larger areas.First 100 ×
100km2Region 65 demand points and 16 possible charging sites are randomly generated.The parameter of genetic algorithm remains unchanged, and works as β
When respectively 1,0.8 and 0.6, the target function value of cost constraint and selected charging station quantity result schematic diagram are shown in figure
Shown in 8, shows that the site selecting method proposed has good scalability, can be applied to bigger region.
To sum up, the Optimizing Site Selection method of electronic facility charging station provided in an embodiment of the present invention, solves electronic facility and fills
The location problem in power station, while ensure that minimum cost, and so that coverage area is extended to whole region to meet the need of driver
Summation convenience.
Embodiment two
Fig. 9 is the structural block diagram that a kind of electronic facility charging station addressing provided in an embodiment of the present invention optimizes device, electronic
Facility charging station addressing optimization device is used to execute the electronic facility charging station Optimization Method for Location-Selection such as embodiment one, such as Fig. 9 institute
Show, which includes:
Model building module 91 is more for establishing electronic facility charging station location optimization model based on multiple constraint conditions
A constraint condition includes at least cost constraint, charging station capacity constraints and coverage constraint condition；
Model solution module 92, for solving electronic facility charging station location optimization model using genetic algorithm.
Electronic facility charging station addressing provided in this embodiment optimizes device, sets with electronic provided by the embodiment of the present invention
It applies charging station Optimization Method for Location-Selection and belongs to same inventive concept, electronic facility provided by any embodiment of the invention can be performed and fill
Power station Optimization Method for Location-Selection has and executes the corresponding functional module of electronic facility charging station Optimization Method for Location-Selection and beneficial effect.
The not technical detail of detailed description in the present embodiment, reference can be made to electronic facility charging station addressing provided in an embodiment of the present invention is excellent
Change method, is not repeated here herein.
In addition, another embodiment of the present invention also provides a kind of electronic facility charging station addressing optimization device, comprising:
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processing
Device realizes the electronic facility charging station Optimization Method for Location-Selection as described in embodiment one.
In addition, another embodiment of the present invention also provides a kind of computer readable storage medium, it is stored with computer program,
The electronic facility charging station addressing as described in the embodiment one of above-described embodiment is realized when the program is executed by processor
Optimization method.
It should be understood by those skilled in the art that, the embodiment in the embodiment of the present invention can provide as method, system or meter
Calculation machine program product.Therefore, complete hardware embodiment, complete software embodiment can be used in the embodiment of the present invention or combine soft
The form of the embodiment of part and hardware aspect.Moreover, being can be used in the embodiment of the present invention in one or more wherein includes meter
Computer-usable storage medium (including but not limited to magnetic disk storage, CD-ROM, the optical memory of calculation machine usable program code
Deng) on the form of computer program product implemented.
It is referring to the method for middle embodiment, equipment (system) according to embodiments of the present invention and to calculate in the embodiment of the present invention
The flowchart and/or the block diagram of machine program product describes.It should be understood that can be realized by computer program instructions flow chart and/or
The combination of the process and/or box in each flow and/or block and flowchart and/or the block diagram in block diagram.It can mention
For the processing of these computer program instructions to general purpose computer, special purpose computer, Embedded Processor or other programmable datas
The processor of equipment is to generate a machine, so that being executed by computer or the processor of other programmable data processing devices
Instruction generation refer to for realizing in one or more flows of the flowchart and/or one or more blocks of the block diagram
The device of fixed function.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although the preferred embodiment in the embodiment of the present invention has been described, once a person skilled in the art knows
Basic creative concept, then additional changes and modifications may be made to these embodiments.So appended claims are intended to explain
Being includes preferred embodiment and all change and modification for falling into range in the embodiment of the present invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (10)
1. a kind of electronic facility charging station Optimization Method for Location-Selection, which is characterized in that comprising steps of
Based on multiple constraint conditions, electronic facility charging station location optimization model is established, the multiple constraint condition includes at least
Cost constraint, charging station capacity constraints and coverage constraint condition；
The electronic facility charging station location optimization model is solved using genetic algorithm.
2. the method according to claim 1, wherein the electronic facility charging station location optimization model are as follows:
Wherein, I={ 1 ..., m } is the demand point set of residential area；
J=1 ..., and n } it is possible charging station set；
fjIndicate the cost of investment of charging station j；
dijIndicate the transportation cost of demand point i to charging station j；
xjIndicate the binary variable of charging station j；
zijIndicate the binary variable of distribution requirements point i to charging station j；
XiIndicate the demand of demand point i；
cjIndicate the capacity of charging station j；
C indicates economic cost；
It indicates to be the Website Hosting within β Q with charging station j distance；
β indicates that Variable Factors, β are greater than 0 and are less than or equal to 1；
The distance of D (j, k) expression charging station j to charging station k；
Q indicates the enforcement distance in the fully charged electrically-charging equipment of charging station；
l0Indicate the residual flow that do not used up by figure, l0≥0；
yklIt indicates from charging station k to the magnitude of traffic flow of charging station l；
ylpIt indicates from charging station l to the magnitude of traffic flow of charging station p；
The length of v expression V；
V indicates the set for being able to satisfy the charging station of driver demand；
E indicates the side between charging station.
3. method according to claim 1 or 2, which is characterized in that described to solve the electronic facility using genetic algorithm
Charging station location optimization model includes:
S1, possible multiple charging stations and demand point data are imported, and genetic algorithm parameter is set；
S2, chromosome coding is carried out to the multiple charging station, is formed initial population R (t), evolutionary generation t=0；
S3, calculate the population R (t) each chromosome fitness function value, and according to fitness function value to R (t) into
Row screening, generates parent population S (t)；
S4, clonal propagation is carried out to the parent population S (t), is formed population T (t), and to each dye in the population T (t)
Colour solid cross and variation forms population T'(t)；
S5, the population R (t) and the population T'(t are calculated) and each chromosome of concentration fitness function value；
Described in S6, calculating and the select probability of each chromosome concentrated, and according to the select probability selective staining body, generate
Population R (t+1)；
S7, judge whether the current iteration number t of genetic algorithm reaches maximum number of iterations, if not up to, t=t+1 is enabled, and
The step S3 is jumped to, otherwise, output is used to indicate the result of optimal charging station addressing.
4. according to the method described in claim 3, it is characterized in that, contaminating in the step S2 the multiple charging station
Colour solid coding, forms population R (t), comprising:
The multiple charging station is numbered and is sorted using binary coding, and presses collating sequence for each charging station
A gene as chromosome.
5. according to the method described in claim 3, it is characterized in that, to each dye in the population T (t) in the step S4
Colour solid cross and variation forms population T'(t) include:
Each chromosome in the population T (t) is performed the following operations:
Judge whether the chromosome meets the cost constraint；
If not satisfied, then selecting two at random from the gene that the value of the chromosome is 0 is set as 1；
It is negated if satisfied, then selecting two at random from the full gene of the chromosome.
6. according to the method described in claim 3, it is characterized in that, the fitness function value of the chromosome is using following dye
Colour solid fitness function is calculated:
Wherein, guChromosome is represented, B is one big number, guarantees F (gu) value be positive number.
7. according to the method described in claim 6, it is characterized in that, the select probability of the chromosome is using following selection
Probability calculation formula is calculated:
Wherein, M (gu) indicate select probability function, density (gu) it is antibody concentration function.
8. a kind of device applied to electronic facility charging station Optimization Method for Location-Selection as described in any one of claim 1 to 7,
It is characterized in that, described device includes:
Model building module, it is the multiple for establishing electronic facility charging station location optimization model based on multiple constraint conditions
Constraint condition includes at least cost constraint, charging station capacity constraints and coverage constraint condition；
Model solution module, for solving the electronic facility charging station location optimization model using genetic algorithm.
9. a kind of electronic facility charging station addressing optimizes device characterized by comprising
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
Existing electronic facility charging station Optimization Method for Location-Selection as described in any one of claim 1 to 7.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that described program is processed
Device realizes electronic facility charging station Optimization Method for Location-Selection as described in any one of claim 1 to 7 when executing.
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