CN114118536A - Planning method for centralized charging station and battery replacement station, planning device and chip thereof - Google Patents

Planning method for centralized charging station and battery replacement station, planning device and chip thereof Download PDF

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CN114118536A
CN114118536A CN202111314847.3A CN202111314847A CN114118536A CN 114118536 A CN114118536 A CN 114118536A CN 202111314847 A CN202111314847 A CN 202111314847A CN 114118536 A CN114118536 A CN 114118536A
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battery replacement
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徐婷婷
胡晓锐
吴高林
龙方家
张雨晴
朱彬
龙羿
汪会财
池磊
李智
谢晓念
谢涵
袁秀娟
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State Grid Chongqing Electric Power Co Marketing Service Center
State Grid Corp of China SGCC
State Grid Chongqing Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Chongqing Electric Power Co Ltd
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Abstract

The invention discloses a planning method for an electric vehicle centralized charging station and a battery replacing station, relates to the technical field of power grid planning, and solves the problems that the centralized charging station and the battery replacing station in the prior art are high in construction cost and inconvenient for users to replace batteries. The method comprises the steps of respectively establishing a centralized charging station and a battery replacement station location and capacity-fixing mathematical model by taking the cost of the charging and replacement station and the cost in the battery replacement and charging processes as the minimum targets; and then optimizing and solving the optimal number and address of the battery replacement stations and the centralized charging stations. The method provided by the invention can reduce the repeated coverage rate of the service area of the battery replacement station on one hand, and further save the construction cost of facilities such as the battery replacement station; on the other hand, the battery replacement requirement of the electric automobile user can be met in time, and the battery replacement cost of the user is saved.

Description

Planning method for centralized charging station and battery replacement station, planning device and chip thereof
Technical Field
The invention relates to the technical field of power grid planning, in particular to the technical field of electric vehicle charging and battery replacing facility planning, and more particularly relates to a planning method for an electric vehicle centralized charging station and a battery replacing station.
Background
The battery replacement mode of the battery leasing mode is matched with a large-scale centralized charging mode of the batteries, and the electric vehicle energy battery replacement mode has been developed into a competitive operation mode of electric vehicle energy supply due to the advantages of high energy supply speed, low battery leasing cost, higher speed of electric vehicle energy battery replacement compared with direct charging and the like.
In the distribution link of the batteries of the electric automobile, if the position layout of the centralized charging station and the battery replacing station of the electric automobile is unreasonable, on one hand, the vehicles need to go to the battery replacing station to replace the batteries before going around more routes, more traveling time can be consumed, and the satisfaction degree of customers can be reduced after a long time; and the vehicle detour distance and time are reduced by consuming more manpower, battery logistics vehicle distribution resources and battery replacement stations, so that the distribution cost and facility configuration cost of enterprises can be increased, and the income of the operated battery replacement stations can be reduced. On the other hand, in the prior art, the repeated coverage rate of the power conversion service area is lack of professional consideration in the site selection process of the centralized charging station and the battery power conversion station, so that the phenomena of service area overlapping and layout redundancy of the constructed service facilities occur, and the construction cost is wasted.
Therefore, it is necessary to provide a reasonable centralized charging station and battery charging station planning method for electric vehicles, so that the convenience of charging is improved for users, and the costs of service, construction and the like of a service party are controlled.
Disclosure of Invention
The invention aims to: in order to solve the technical problems, the invention provides a planning method for an electric vehicle centralized charging station and a battery replacement station.
The invention provides a planning method for an electric vehicle centralized charging station and a battery replacement station, and aims to solve the problems that the centralized charging station and the battery replacement station in the prior art are high in construction cost and inconvenient for users to replace batteries. Firstly, the invention considers the factors of user traffic flow and convenient battery replacement in a planning area, and establishes a location and volume selection mathematical model (namely a location selection mathematical model and a volume determination mathematical model) of the battery charging station by taking the minimum annual construction cost (namely annual construction investment cost) of the battery replacement station and the minimum loss cost (namely user annual battery replacement cost) in the battery replacement process as the target; secondly, solving a mathematical model for locating the battery power exchanging station by using a method of combining a particle swarm-genetic hybrid algorithm and a variable-weight Voronoi diagram, determining the optimal location of the battery power exchanging station, and determining the number of total battery power exchanging machines, namely service stations, in the planned area and the capacity of the battery power exchanging machines in the battery power exchanging station based on a constant-volume mathematical model; finally, the method establishes a mathematical model of the site selection of the centralized charging station by taking the minimum sum of the annual construction cost (namely annual construction investment cost) of the centralized charging station and the battery distribution cost as a target, and determines the optimal site selection of the centralized charging station by combining a particle swarm-genetic hybrid algorithm and a Voronoi diagram.
The invention specifically adopts the following technical scheme for realizing the purpose:
on one hand, the invention provides a planning method for an electric vehicle centralized charging station and a battery replacement station, which comprises the following steps:
s1, determining the value of the planning number Z of the battery replacing stations in the planning area:
Figure BDA0003343297570000021
in the formula: smaxRepresents the maximum service area, S, of the battery replacement stationminRepresents the minimum service area, S, of the battery replacement stationtotalRepresenting the total area of the planning zone;
s2, performing primary address selection on the Z battery changing stations through a geometric method to obtain primary address selection points of the battery changing stations;
s3, taking the primarily selected address points of each battery power exchanging station as growth cores, and performing service division on the growth cores through a variable-weight Voronoi diagram to obtain Voronoi diagrams of Z battery power exchanging stations;
s4, establishing a location mathematical model of the battery changing station by taking the sum of annual construction cost of the battery changing station and electricity changing cost of a user as a minimum target, and solving the location mathematical model of the battery changing station through a particle swarm-genetic hybrid algorithm and the obtained variable weight Voronoi diagram to obtain an optimal location diagram of Z battery changing stations;
s5, comparing the sum of the annual construction cost of the battery replacement station and the annual battery replacement cost of the user under each value of Z, and selecting the Z value with the minimum sum as the optimal value of the number of the battery replacement stations, namely Z*；
S6, determining the value of the planning number H of the centralized charging stations in the planning area:
Figure BDA0003343297570000031
in the formula: mmaxMaximum number of battery change stations, M, representing a single centralized charging station serviceminRepresenting the minimum number of battery charging stations served by a single centralized charging station;
s7, aligning Z by a geometric method*H centralized charging stations configured in an optimal site selection diagram of each battery changing station perform initial site selection to obtain initial address points of the centralized charging stations;
s8, establishing a site selection mathematical model of the centralized charging stations by taking the sum of annual construction cost and battery distribution cost of the centralized charging stations as a minimum target, and solving the site selection mathematical model of the centralized charging stations through a particle swarm-genetic hybrid algorithm to obtain the optimal sites of H centralized charging stations;
s9, comparing the sum of annual construction cost and battery distribution cost of the centralized charging station under each value of H, and selecting the H value with the minimum sum of cost as the optimal value of the centralized charging station, namely H*；
S10, through Voronoi diagram pair H*The centralized charging station divides the service to obtain H*Voronoi diagram of a centralized charging station, namely H*The service range of the individual centralized charging stations.
Further, the method also comprises a step of changing the capacity of the batteries contained in the planning area, which is as follows:
the battery replacement behavior of the user is represented by an M/M/c/∞/infinity mathematical model in a queuing theory, wherein a plurality of independent service desks are arranged in a queuing system, c represents the number of single-team parallel service desks, M represents negative exponential distribution, and n is arranged in a battery replacement station iProbability P when electric vehicle carries out battery replacement servicen,iIs composed of
Figure BDA0003343297570000032
Figure BDA0003343297570000033
Figure BDA0003343297570000034
In the formula, ρiFor the charging service intensity of the battery charging station i, lambda is equal to nkThe/t is the number of electric vehicles arriving at the battery replacement station in unit time and obeys Poisson distribution, wherein nkThe daily battery replacement demand of the electric automobile, tbFor the time of peak period of daily battery replacement, mu is 1/tsAverage service rate for battery change machines, where tsAverage battery replacement time of each electric automobile;
according to the Ritt formula, the average value L of the length of a queue of a user waiting for battery replacement in a battery replacement station i can be obtainediIs composed of
Figure BDA0003343297570000041
The objective function of the sum of the operation cost of the battery charging station and the queuing charging time cost of the user is as follows:
minT(c)＝CRC·c+CWC·Li (7)
in the formula: cRCRepresenting the operating cost of the power change service desk in unit time, CwcThe time-consuming cost of queuing and waiting of the user to be charged in the unit time of the battery charging station is solved;
the optimal number C of the power change service stations corresponding to the lowest value of the objective function*I.e. it is constrained by equation (8):
Figure BDA0003343297570000042
the electricity changing service desks solved by the simultaneous formulas (6), (7) and (8) are the optimal number C of the battery changing machines*。
Further, the method further comprises a step of changing the capacity of the batteries contained in each battery changing station, which specifically comprises the following steps:
electric automobile daily battery replacement demand n for calculating user battery replacement demand point s by daily battery replacement demand load predictions
Figure BDA0003343297570000043
In the formula (I); i isBSFor a battery replacement station set, fceilThe value is an upward rounding function, S is the total number of the electric automobiles in the battery replacement service area of each battery replacement station, and beta is the daily battery replacement probability which is taken as 0.1; psPredicting the daily battery replacement demand load of a battery replacement demand point s; pi,ΣPredicting the total amount of daily battery replacement demand load in a battery replacement service area of a battery replacement station i;
the number N of battery replacing machines in each battery replacing stationiThe mathematical expression of (a) is:
Figure BDA0003343297570000044
in the formula (I); r iscThe probability of simultaneously reaching the battery replacement station for the users in each battery replacement service area is 0.4; n isqTaking the maximum number of queued vehicles of each battery replacement machine as 4; sUSAnd charging the user with the demand point set.
Further, the annual construction cost of the battery changing station comprises facility investment cost of the battery changing station and operation management cost of the battery changing station;
the annual construction cost mathematical model of the battery replacement station is as follows:
Figure BDA0003343297570000051
wherein, wiThe fixed investment cost for the battery replacing station i; q is the unit price of each battery service desk; n is a radical ofiThe number of battery service desks in the battery replacement station i is counted; g is the conversion coefficient of the related investment cost of the battery replacing machine; r is0The current rate is the current rate; m isiThe operation age of the power change station i is set; sigma1Is an equivalent coefficient of the operation management cost.
Further, the facility investment cost of the battery replacement station comprises land rental cost and battery replacement machine cost.
Further, the user battery replacement cost mathematical model is as follows:
Figure BDA0003343297570000052
wherein d represents the number of days of the year, tbIndicating a period of peak time of daily battery change, IBSRepresenting a collection of battery change stations.
Further, the annual construction cost mathematical model of the centralized charging station is as follows:
Figure BDA0003343297570000053
in the formula, ωjCapital investment cost for centralized charging stations j; e.g. of the typejThe number of sets of chargers and related facilities purchased in the centralized charging station j; c is the unit price of the charger facility; r is0The current rate is the current rate; m isjThe operation period of the centralized charging station j; sigma2The method is an equivalent coefficient of the operation management cost of the centralized charging station.
Further, the mathematical model of the battery distribution cost is as follows:
Figure BDA0003343297570000054
in the formula, d is annual operation days; gamma is the equivalent coefficient of the battery distribution cost; epsilon is the nonlinear coefficient of the urban highway; m is the standby coefficient of the battery; siThe logistics vehicle number distributed for each day of the battery replacement station i; r isijThe Euclidean distance from the battery replacement station i to the centralized charging station j; i isBSIs a battery replacement station set.
In one aspect, the present invention further provides a planning apparatus for an electric vehicle centralized charging station and a battery replacement station, where the planning apparatus includes a data input/output interface, a processor, and a memory storing program instructions, and the processor is configured to execute the program instructions stored in the memory to execute the planning method provided above;
on the other hand, the invention also provides a chip, which comprises a processing unit and a storage unit for storing the computer operation instruction, wherein the processing unit is used for executing the planning method provided by the invention by calling the computer operation instruction stored in the storage unit.
The invention has the following beneficial effects:
1. the invention can determine the optimal address of the battery replacing station and the optimal address of the centralized charging station in the planning area, and can also determine the optimal number of the battery replacing stations and the number of the centralized charging stations based on the lowest corresponding cost control, thereby meeting the support of battery replacement for users and the support of centralized charging of the batteries in the planning area, and effectively controlling the cost of a service provider and a served provider, so as to obtain the optimal battery replacing station address selection and the optimal battery replacing related cost control under the two conditions of considering the convenience and the cost of the users; meanwhile, under the two conditions of considering the convenience and cost of centralized charging, the optimal centralized charging station site selection and the optimal battery centralized charging related cost control are obtained;
2. the method provided by the invention can reduce the repeated coverage rate of the service area of the battery replacement station on one hand, and further save the construction cost of facilities such as the battery replacement station; on the other hand, the battery replacement requirement of the electric automobile user can be met in time, and the battery replacement cost of the user is saved.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a trend of the relative cost of the battery charging station with the number of the battery charging stations;
FIG. 3 is a distribution of sites and service areas of a battery swapping station;
FIG. 4 is a graph of the trend of the relative cost sums for different numbers of centralized charging stations;
fig. 5 is a site distribution and service range of a centralized charging station.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, the method for planning the electric vehicle centralized charging station and the battery replacement station provided in this embodiment is shown in fig. 1, and specifically includes the following steps:
s1, determining the value of the planning number Z of the battery replacing stations in the planning area:
Figure BDA0003343297570000071
in the formula: smaxRepresents the maximum service area, S, of the battery replacement stationminRepresents the minimum service area of the battery replacement station,Stotalrepresenting the total area of the planning zone;
s2, performing primary address selection on the Z battery changing stations through a geometric method to obtain primary address selection points of the battery changing stations;
s3, taking the primarily selected address points of each battery power exchanging station as growth cores, and performing service division on the growth cores through a variable-weight Voronoi diagram to obtain Voronoi diagrams of Z battery power exchanging stations;
s4, establishing a location mathematical model of the battery changing station by taking the sum of annual construction cost of the battery changing station and electricity changing cost of a user as a minimum target, and solving the location mathematical model of the battery changing station through a particle swarm-genetic hybrid algorithm and the obtained variable weight Voronoi diagram to obtain an optimal location diagram of Z battery changing stations;
s5, comparing the sum of the annual construction cost of the battery power changing station and the power changing cost of the user under each value of Z, and selecting the Z value with the minimum sum as the optimal value of the number of the battery power changing stations, namely Z*；
S6, determining the value of the planning number H of the centralized charging stations in the planning area:
Figure BDA0003343297570000081
in the formula: mmaxMaximum number of battery change stations, M, representing a single centralized charging station serviceminRepresenting the minimum number of battery charging stations served by a single centralized charging station;
s7, aligning Z by a geometric method*H centralized charging stations configured in an optimal site selection diagram of each battery changing station perform initial site selection to obtain initial address points of the centralized charging stations;
s8, establishing a site selection mathematical model of the centralized charging stations by taking the sum of annual construction cost and battery distribution cost of the centralized charging stations as a minimum target, and solving the site selection mathematical model of the centralized charging stations through a particle swarm-genetic hybrid algorithm to obtain the optimal sites of H centralized charging stations;
s9, comparing the sum of annual construction cost and battery distribution cost of the centralized charging station under each value of H, and selecting H with the minimum sum of costThe value is the preferred value of the centralized charging station, namely H*；
S10, through Voronoi diagram pair H*The centralized charging station divides the service to obtain H*Voronoi diagram of a centralized charging station, namely H*The service range of the individual centralized charging stations.
The battery replacement station is planned as follows: the method considers the factors of using the user traffic flow and the convenience of battery replacement in the planned area, and firstly, the value range of the planned number Z of the battery replacement stations in the area is taken into consideration; then establishing a mathematical model for location selection of the battery charging station by taking the annual construction cost of the battery charging station and the minimum loss cost in the battery changing process of a user as targets; secondly, solving a mathematical model of the location of the battery power exchanging station by using a method of combining a particle swarm-genetic hybrid algorithm and a variable-weight Voronoi diagram, and determining the optimal location of the battery power exchanging station; and then comparing the sum of the annual construction cost of the battery power changing station and the user power changing cost under each Z value based on each Z value and the corresponding optimal site of the battery power changing station, and selecting the Z value with the minimum sum of the costs as the optimal value of the number of the battery power changing stations, namely the Z value is the optimal value of the number of the battery power changing stations*。
The centralized charging station is planned as follows: according to determined Z*Determining the value range of the planned quantity Z of the centralized charging stations in the area; then, establishing a mathematical model of site selection of the centralized charging station by taking the minimum sum of annual construction cost and battery distribution cost of the centralized charging station as a target, and determining the optimal site selection of the centralized charging station by combining a particle swarm-genetic hybrid algorithm and a Voronoi diagram; then based on each H value and the corresponding optimal site of the centralized charging station, comparing the sum of annual construction cost and battery distribution cost of the centralized charging station under each value of H, and selecting the H value with the minimum sum as the optimal value of the number of battery replacement stations, namely H*。
Therefore, based on the method, the optimal address of the battery replacement station and the optimal address of the centralized charging station in the planning area can be determined, the optimal number of the battery replacement stations and the number of the centralized charging stations can be determined based on the lowest corresponding cost control, so that the support for user battery replacement and the support for centralized charging of the batteries in the planning area are met, the cost of a service provider and a service receiver can be effectively controlled, and the optimal battery replacement station location and the optimal battery replacement related cost control are obtained under the two conditions of user convenience and cost. Meanwhile, under the two conditions of considering the centralized charging convenience and the cost, the optimal centralized charging station site selection and the optimal battery centralized charging related cost control are obtained.
In conclusion, the method provided by the invention can reduce the repeated coverage rate of the service area of the battery replacement station, thereby saving the construction cost of facilities such as the battery replacement station; on the other hand, the battery replacement requirement of the electric automobile user can be met in time, and the battery replacement cost of the user is saved.
Example 2
Step 1, establishing mathematical models of the battery replacement station and the centralized charging station, and the implementation is as follows.
Firstly, a mathematical model for site selection of a battery changing station is as follows:
step 1.1, establishing a battery replacement station annual construction investment cost model
The annual construction cost (namely the annual construction investment cost) of the battery changing station consists of two parts of the facility investment cost of the battery changing station and the operation management cost of the battery changing station. The facility investment cost takes the land purchase expense and the battery replacing expense into consideration. The operation management cost of the battery replacement station mainly comprises the cost of wages of workers in the battery replacement station, equipment maintenance, vehicle distribution and back-and-forth oil consumption of the vehicle distribution and the like. The annual construction investment cost of the battery replacement station i is shown as the formula:
Figure BDA0003343297570000091
wherein, wiThe fixed investment cost for the battery replacing station i; q is the unit price of each battery service desk; n is a radical ofiThe number of battery service desks (namely a battery replacing machine, the same below) in the battery replacing station i; g is the conversion coefficient of the related investment cost of the battery replacing machine; r is0The current rate is the current rate; m isiThe operation age of the power change station i is set; sigma1For equivalence of operation management costsAnd (4) the coefficient.
Step 1.2, establishing a mathematical model of the electricity changing cost of a user
The site selection planning of the battery replacement station depends on the number of battery replacement machines in each station, so that the replacement service operation cost of each station is determined; the number of the battery replacement machines in the station determines the time consumption of queuing and waiting of the users to be replaced.
The queuing model has the model form: X/Y/Z/A/B/C, wherein the meaning represented by X is the rule presented by the time interval distribution of the arrival of the customer, the meaning represented by Y is the rule presented by the time distribution of the service enjoyed by the customer, the meaning represented by Z is the number of service platforms owned by the queuing system, the meaning represented by A is the specific capacity owned by the system, the meaning represented by B is the number owned by the customer source, and the meaning represented by C is the service rule.
In the invention, the battery replacement behavior of a user is expressed by adopting a standard model in a multi-service-station queuing model in a queuing theory, namely an M/M/c/∞/∞ model, wherein a plurality of independent service stations are arranged in a queuing system, c represents the number of single-queue parallel service stations, and M represents negative exponential distribution, so that the probability P of the battery replacement service of n electric vehicles in a battery replacement station in,iIs composed of
Figure BDA0003343297570000101
Figure BDA0003343297570000102
Figure BDA0003343297570000103
In the formula, ρiThe battery replacement service intensity of a battery replacement station i; λ ═ nkThe/t is the number of electric vehicles arriving at the battery replacement station in unit time and obeys Poisson distribution, wherein nkThe daily battery replacement demand of the electric automobile, tbIn the time of the peak period of daily battery replacement; mu.s＝1/tsAverage service rate for battery change machines, where tsAnd (4) the average power change time of each electric automobile. In the invention, the service desk is equivalent to a battery-powered machine.
According to a Ritter formula, the average value L of the length of a queue of a user to be charged in a battery charging station i can be obtainediIs composed of
Figure BDA0003343297570000104
To sum up, the annual battery replacement cost is expressed as:
Figure BDA0003343297570000111
in the formula, CwcD represents the number of days of one year and takes 356 days for the time-consuming queuing cost of the user to be charged in the battery charging station in unit time; t is tbWhat represents: the peak period of the daily change point is determined according to statistical data, IBSRepresenting a collection of battery change stations.
The volumetric capacity of the battery changing machine in the planned area is as follows:
step 1.3 of establishing a constant volume model of the battery replacement station
Firstly, a constant volume mathematical model of the battery replacement machine in a planning region is established, as follows,
the invention considers the operation cost of the battery changing station and the queuing changing time of the user, and the objective function is as follows:
minT(c)＝CRC·c+CWC·Li
in the formula: cRCAnd the operation cost of the battery replacement service desk in unit time is shown.
The invention sets the power exchange service desk to be rounded up, and the target function of the power exchange service desk is constrained by the following formula
Figure BDA0003343297570000112
In the formula, C*Expressing the optimal number of the battery replacement service desks, namely the total number of the battery replacement machines in the area, and a simultaneous formula LiminT (C) and the above constraint formula, and solving the optimal number C of the power change service desks*。
A battery change machine volumetric mathematical model within the battery charging station is established, as follows,
electric automobile daily battery replacement demand n for calculating user battery replacement demand point s by daily battery replacement demand load predictions，
Figure BDA0003343297570000113
In the formula (I); i isBSIs a battery replacement station set. f. ofceilIs an upward rounding function; s is the total number of the electric automobiles in the battery replacement service area of each battery replacement station; beta is the daily battery replacement probability and is taken as 0.1; psPredicting the daily battery replacement demand load of a battery replacement demand point s; pi, Σ is the total amount of daily charging demand load prediction in the charging service area of the battery charging station i.
The mathematical expression of the number Ni of battery changing machines in each battery changing station is as follows:
Figure BDA0003343297570000121
in the formula (I); r iscThe probability of simultaneously reaching the battery replacement station for the users in each battery replacement service area is 0.4; n isqThe maximum number of queued vehicles for each battery change machine is taken as 4, SUSAnd charging the user with the demand point set. Secondly, the site selection mathematical model of the centralized charging station is as follows:
step 1.4, establishing a mathematical model of annual construction investment cost of the centralized charging station
The annual construction cost (i.e., annual construction investment cost) of the centralized charging station j is shown by the following formula:
Figure BDA0003343297570000122
in the formula, ωjCapital investment cost for centralized charging stations j; e.g. of the typejThe number of sets of chargers and related facilities purchased in the centralized charging station j; c is the unit price of the charger facility; r is0The current rate is the current rate; m isjThe operation period of the centralized charging station j; sigma2The equivalent coefficient of the operation management cost such as the related operation and maintenance cost, the staff wage and the like. Step 1.5 establishing a mathematical model of battery distribution cost
The annual battery distribution cost is mainly determined by the distribution number of the batteries and the distribution distance between the centralized charging station and the battery replacement station. The annual battery distribution cost from the centralized charging station j to each battery replacement station i is expressed as
Figure BDA0003343297570000123
In the formula, d is annual operation days and is taken as 365; gamma is the equivalent coefficient of the battery distribution cost; epsilon is the nonlinear coefficient of the urban highway; m is the standby coefficient of the battery; siThe logistics vehicle number distributed for each day of the battery replacement station i; r isijThe Euclidean distance from the battery replacement station i to the centralized charging station j; i isBSIs a battery replacement station set.
The constraint conditions of the mathematical models are established as follows.
Third, constraint conditions
Step 1.6 build constraints of the model
1) Battery changing station quantity constraint
The planned number of the battery replacement stations refers to the service area of the battery replacement stations, and if the service area is too small, more battery replacement stations need to be built, so that the capital construction cost of the system is increased; the excessive service area reduces the convenience and economy of the user in replacing the battery. The maximum planning number Z is thus determined using the total area of the planning region and the minimum service areamaxOtherwise, the minimum planning number Z is determinedminThen the optimal, minimum and maximum projected quantity models are as follows:
Figure BDA0003343297570000131
Figure BDA0003343297570000132
Zmin<Z<Zmax
in the formula: z represents the planned number of the battery power changing stations in the planned area, and when the sum of the annual construction cost of the battery power changing stations and the battery changing cost of users is minimum, the corresponding Z value is the optimal value of the number of the battery power changing stations, namely Z is*。
2) Centralized charging station quantity constraints
The centralized charging stations are used as charging and maintaining centers of the power batteries, and are expensive in construction investment and large in occupied area in the planning and site selection process, so that each centralized charging station generally serves a plurality of battery replacement stations. Therefore, in the planning and site selection process, the minimum number of the centralized charging stations can be determined by the planned number of the battery charging stations and the maximum number of the battery charging stations serving a single centralized charging station, otherwise, the maximum number of the centralized charging stations is determined, the optimal number falls within the interval, and the planned number model can be expressed as:
Figure BDA0003343297570000133
Figure BDA0003343297570000134
in the formula: z*An optimal number, M, representing the number of battery stationsmaxMaximum number of battery change stations, M, representing a single centralized charging station serviceminThe minimum number of battery charging stations served by a single centralized charging station is represented.
3) Number constraint of battery replacing machine
Ns,min≤Ni≤Ns,max,i∈IBS
NS,minAnd Ns，maxThe minimum and maximum values of the number of the battery changing machines in the battery changing station are respectively configured.
4) To avoid the layout of the battery replacement stations from being too dense, the distance constraint between the battery replacement stations is expressed as
Figure BDA0003343297570000135
In the formula (I), the compound is shown in the specification,
Figure BDA0003343297570000141
for changing batteries i1,i2The euclidean distance between; dminThe minimum distance between battery replacement stations.
5) The charger and related facilities are configured and restricted as
Figure BDA0003343297570000142
In the formula, ej,minLimiting the minimum set number of motors and related facilities in the centralized charging station j; b is charging efficiency; pj,∑Predicting the total amount of daily battery replacement demand load in a battery distribution area of a centralized charging station j; and alpha is the maximum output power of each charger in the centralized charging station.
6) Service strength constraints for battery change stations
In the battery replacement station, in order to make the queue of the user not tend to be infinite long, the service strength value of the queue is less than 1, namely
Figure BDA0003343297570000143
NiThe number of battery change machines for the battery change station; rhoiThe power conversion service intensity of the battery power conversion station; lambda is the number of electric vehicles reaching the battery replacement station in unit time obeying Poisson distribution; μ is the average service rate of the battery change machine.
Step 2, combining the Voronoi graph with variable weights and a particle swarm-genetic hybrid algorithm to jointly solve
Step 2.1 of establishing a variable-weight Voronoi diagram for a mathematical model of a battery changing station
Voronoi diagrams, also known as teson polygons, are continuous polygons made up of perpendicular bisectors made by straight lines connecting two nearest neighbors. Let pkIs set of points p ═ p1,p2,...,pnA seed point in (1), its Voronoi region rkIs defined mathematically as
rk＝{x∈X∣d(x,pk)<d(x,pj)}
The weighted Voronoi diagram is an evolutionary form in the expansion process of the ordinary V diagram, and each vertex is set with different weights to play different roles. In the planning process of the patent, with the gradual expansion of the battery swapping service range, the common V diagram does not meet the requirement of actual planning, so the variable-weight V diagram is adopted to determine the service range.
Let Q be { Q ═ Q1,q2...qnIs a set of points on a plane, the definition of a weighted Voronoi diagram is:
Figure BDA0003343297570000151
in the formula, d (x, q)mAnd d (x, q)1) Respectively being any point x and q on the planemAnd q is1The Euclidean distance between; omegamIs a vertex qmThe weight of (c). The variable weight Voronoi diagram divides the plane into n regions, each vertex corresponding to a region V (q)m,ωm) And ω is1，ω2，...ωnAre not identical.
Suppose that during the generation of a Voronoi diagram with variable weights, the battery swapping station is taken as a vertex with ωkFor the uniform speed expansion, the area formed by the region is MkThe circle of (c). Rated capacity S of battery replacement station kkShould meet the power change requirement W within the service rangekIn which S isk∝Wk＝Mkρk。
Figure BDA0003343297570000152
In the formula, ρkThe required power conversion density in the service range of the battery power conversion station k, the value of the required power conversion density and the number N of electric vehicles in the areak，ρk∝Nk；SkFor the rated capacity of the battery changing station k, the maximum service capacity W of the battery changing stationKNIs in direct proportion, so the weight of the battery replacement station is mainly composed of WKNAnd NkDetermine that the weights become after t expansions
Figure BDA0003343297570000153
In the formula
Figure BDA0003343297570000154
For maximum service capability after t-1 expansion
Figure BDA0003343297570000155
The number of regional electric vehicles after t-1 expansion. After normalization, a fixed weight formula is obtained
Figure BDA0003343297570000156
In the evolution process, the service capability W of the battery replacement station kKNAnd service radius DkThe descending amplitude is set as a variable weight ω ″)kWherein the weight determination formula is as follows
Figure BDA0003343297570000157
Figure BDA0003343297570000158
Figure BDA0003343297570000159
In the formula, WtkThe actual number of the served electric vehicles of the charging station k after the t expansion; dtkThe actual maximum service radius of the battery replacement station k after the t-th expansion; dkmaxThe maximum service radius of the battery replacement station k.
To be provided with
Figure BDA00033432975700001510
The weight of the variable-weight Voronoi diagram is reduced along with the expansion of the number and the service range of the electric automobiles served by the battery charging station, when the service area of the battery charging station is expanded to a certain degree, the weight gradually tends to 0, the expansion speed is gradually reduced, and the finally generated Voronoi diagram can ensure that the battery charging station meets all charging requirements and simultaneously reduces the repeated coverage rate of the battery charging service area.
The Voronoi diagram generation of the mathematical model of the centralized charging station is a prior art operation and is not described here in detail.
Step 2.2 establishing a particle swarm-genetic hybrid algorithm
The invention adopts a conventional particle swarm-genetic hybrid algorithm, which specifically comprises the following steps:
the specific steps for realizing the particle swarm-genetic hybrid algorithm are as follows:
step (1): initializing population parameters to obtain a total population scale N, and then obtaining an evolution total algebra Maxgen and a learning factor c through a hybrid algorithm1，c2Denotes the maximum velocity VmaxValue, evolutionary algebra maximum value T; p is used for poor probability and mutation probability respectivelycAnd PmRepresents;
step (2): the population is initialized to produce N particles, each particle representing a location of a different centralized charging station or battery swap station.
And (3): calculating the specific numerical value of the fitness function;
and (4): setting k to 1;
and (5): comparing whether the k < Maxgen relation exists, if so, continuing to the next step, otherwise, skipping to the step (15)
And (6): the particle swarm evolution algebra t is 1
And (7): judging whether the relation T is less than or equal to T, if yes, continuing the next step, otherwise, skipping to the step (10)
And (8): updating the particle swarm operation position and operation speed according to a formula;
and (9): increasing the iteration times by t as t + 1;
step (10): according to the specific numerical value of the fitness function, N individuals are sorted, the mean value of the numerical values of the fitness function is calculated, and M is extractedkIndividual value;
step (11): evolution of the top and bottom individuals using genetic algorithms
Step (12): mixing the above two groups N-MkCombining the individuals, and selecting stronger individuals according to the fitness function of the individuals;
step (13): the individual M obtained in the step (12)kIndividuals and N-M obtained in the above stepkMixing individuals to form a new population;
step (14): k is k +1, jumping to the step (5);
step (15): and outputting the position of the optimal centralized charging station or battery replacement station.
Example 3
Carrying out example simulation by using the combined planning and site selection of a centralized charging station and a battery replacement station in a certain area, and setting the area of the planning area to be 60km2And 36 road network nodes. The typical daily traffic flow of each road network node is determined by using a monte carlo random sampling method, as shown in table 1:
TABLE 1 typical day each road network node traffic flow
Figure BDA0003343297570000171
The values of the relevant parameters of the centralized charging station and the battery replacing station are shown in a table 2:
table 2 values of relevant parameters of the centralized charging station and the battery replacement station
Figure BDA0003343297570000172
Figure BDA0003343297570000181
The planning result of the battery replacement station is as follows:
firstly, determining the value range of the quantity Z of the battery replacement stations; then establishing a mathematical model of location selection of the battery changing station by taking the minimum sum of the user battery changing cost and the construction cost of the battery changing station as a target; primarily selecting the address of the battery changing station by a geometric method, and then primarily optimizing the address of the battery changing station based on a variable-weight Voronoi diagram to obtain variable-weight Voronoi diagrams of Z battery changing stations; and then further determining the positions of the 9 battery replacement stations through a particle swarm-genetic hybrid algorithm, and further optimizing the positions of the 9 battery replacement stations to obtain an optimal site. Then, based on the optimal site selection, comparing the sum of the annual construction cost of the battery power changing station and the user power changing cost under each value of Z to obtain a comparison result shown as 2: along with the increase of the number of the battery replacing stations, the battery replacing cost of a user is gradually reduced, the investment cost of the battery replacing stations is gradually increased, and when the number of the battery replacing stations is 9, the sum of the costs is minimum. Meanwhile, the optimal solution of the positions and the service areas of the 9 battery charging stations obtained by combining the variable-weight Voronoi diagram and the particle swarm hybrid genetic algorithm is shown in fig. 3.
And then, according to the constant volume method of the battery changing station, the number of the changing service desks constructed under the planning number corresponding to different battery changing stations can be obtained. Firstly, obtaining the optimal number C of the battery replacing machines in the region according to the battery replacing machine constant volume step contained in the planning region*(ii) a And then obtaining the number N of the battery replacing machines of each battery replacing station according to the constant volume step of the battery replacing machines contained in the battery replacing stationsiSpecific data of the total service stations in the planned area obtained based on the number of battery replacement stations in the area are shown in table 3.
TABLE 3 Total number of battery replacement service stations under different number of battery replacement stations
Figure BDA0003343297570000182
The planning result of the centralized charging station is as follows:
the invention determines the number C of the battery replacement stations through the above*Determining the range of the number H of the centralized charging stations to be built; then, the sum of annual construction cost and battery distribution cost of the centralized charging stations is a minimum target, a site selection mathematical model of the centralized charging stations is established, the site selection mathematical model of the centralized charging stations is solved through a particle swarm-genetic hybrid algorithm, and the optimal sites of H centralized charging stations are obtained; then comparing the annual construction cost and the battery distribution cost of the centralized charging station under each value of H, and selecting the H value with the minimum cost sum as the optimal value of the centralized charging station, namely the H value is the optimal value of the centralized charging station*So as to achieve the minimum sum of the annual construction cost of the centralized charging station and the battery distribution cost.
As can be seen from fig. 4, as the number of concentrated charging stations increases, the sum of the concentrated charging station cost and the battery distribution cost decreases and then increases. And based on fig. 4, it is determined that the sum of the annual construction cost and the battery distribution cost of the concentrated charging stations is the minimum when the optimal number of the concentrated charging stations is 3. In the scheme, the preliminary preferred values of the 3 centralized charging stations are determined through a particle swarm hybrid genetic algorithm, and then the preliminary preferred values are further optimized through a Voronoi diagram, so that the service range of the centralized charging stations is obtained. The quantity of the batteries of each centralized charging station is obtained according to 1.6 times of the battery demand in the peak period of the battery replacement demand. The positions of the 3 centralized charging stations are shown in fig. 5, each battery replacement station can obtain battery distribution service, and the battery replacement requirements of electric vehicle users can be met.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. The planning method for the electric vehicle centralized charging station and the battery replacement station is characterized by comprising the following steps:
s1, determining the value of the planning number Z of the battery replacing stations in the planning area:
Figure FDA0003343297560000011
in the formula: smaxRepresents the maximum service area, S, of the battery replacement stationminRepresents the minimum service area, S, of the battery replacement stationtotalRepresenting the total area of the planning zone;
s2, performing primary address selection on the Z battery changing stations through a geometric method to obtain primary address selection points of the battery changing stations;
s3, taking the primarily selected address points of each battery power exchanging station as growth cores, and performing service division on the growth cores through a variable-weight Voronoi diagram to obtain Voronoi diagrams of Z battery power exchanging stations;
s4, establishing a location mathematical model of the battery changing station by taking the sum of annual construction cost of the battery changing station and electricity changing cost of a user as a minimum target, and solving the location mathematical model of the battery changing station through a particle swarm-genetic hybrid algorithm and the obtained variable weight Voronoi diagram to obtain an optimal location diagram of Z battery changing stations;
s5, comparing the sum of the annual construction cost of the battery power changing station and the power changing cost of the user under each value of Z, and selecting the Z value with the minimum sum as the optimal value of the number of the battery power changing stations, namely Z*；
S6, determining the value of the planning number H of the centralized charging stations in the planning area:
Figure FDA0003343297560000012
in the formula: mmaxRepresenting a single setMaximum number of battery replacement stations, M, serviced by the charging stationminRepresenting the minimum number of battery charging stations served by a single centralized charging station;
s7, aligning Z by a geometric method*H centralized charging stations configured in an optimal site selection diagram of each battery changing station perform initial site selection to obtain initial address points of the centralized charging stations;
s8, establishing a site selection mathematical model of the centralized charging stations by taking the sum of annual construction cost and battery distribution cost of the centralized charging stations as a minimum target, and solving the site selection mathematical model of the centralized charging stations through a particle swarm-genetic hybrid algorithm to obtain the optimal sites of H centralized charging stations;
s9, comparing the sum of annual construction cost and battery distribution cost of the centralized charging station under each value of H, and selecting the H value with the minimum sum of cost as the optimal value of the centralized charging station, namely H*；
S10, through Voronoi diagram pair H*The centralized charging station divides the service to obtain H*Voronoi diagram of a centralized charging station, namely H*The service range of the individual centralized charging stations.
2. The method for planning the electric vehicle centralized charging station and the battery replacement station according to claim 1, further comprising a step of fixing the capacity of the battery replacement station included in the planned area, which is specifically as follows:
the battery replacement behavior of the user is expressed by an M/M/c/∞/∞ mathematical model in a queuing theory, wherein a plurality of independent service desks are arranged in a queuing system, c represents the number of single parallel service desks, M represents negative exponential distribution, and the probability P of the battery replacement service of n electric vehicles in a battery replacement station in,iIs composed of
Figure FDA0003343297560000021
Figure FDA0003343297560000022
Figure FDA0003343297560000023
In the formula, ρiFor the charging service intensity of the battery charging station i, lambda is equal to nkThe/t is the number of electric vehicles arriving at the battery replacement station in unit time and obeys Poisson distribution, wherein nkThe daily battery replacement demand of the electric automobile, tbFor the time of peak period of daily battery replacement, mu is 1/tsAverage service rate for battery change machines, where tsAverage battery replacement time of each electric automobile;
according to the Ritt formula, the average value L of the length of a queue of a user waiting for battery replacement in a battery replacement station i can be obtainediIs composed of
Figure FDA0003343297560000024
The objective function of the sum of the operation cost of the battery charging station and the queuing charging time cost of the user is as follows:
minT(c)＝CRC·c+CWC·Li (7)
in the formula: cRCRepresenting the operating cost of the power change service desk in unit time, CwcThe time-consuming cost of queuing and waiting of the user to be charged in the unit time of the battery charging station is solved;
the optimal number C of the power change service stations corresponding to the lowest value of the objective function*I.e. it is constrained by equation (8):
Figure FDA0003343297560000031
the electricity changing service desks solved by the simultaneous formulas (6), (7) and (8) are the optimal number C of the battery changing machines*。
3. The method for planning the electric vehicle centralized charging station and the battery replacement station according to claim 1, further comprising a step of fixing the capacity of a battery replacement machine included in each battery replacement station, which is specifically as follows:
electric automobile daily battery replacement demand n for calculating user battery replacement demand point s by daily battery replacement demand load predictions
Figure FDA0003343297560000032
In the formula (I); i isBSFor a battery replacement station set, fceilIs an upward rounding function, S is the total number of the electric automobiles in the battery replacement service area of each battery replacement station, beta is the daily replacement probability, and P is the power supplysPredicting the daily battery replacement demand load of a battery replacement demand point s; pi,ΣPredicting the total amount of daily battery replacement demand load in a battery replacement service area of a battery replacement station i;
the number N of battery replacing machines in each battery replacing stationiThe mathematical expression of (a) is:
Figure FDA0003343297560000033
in the formula (I); r iscProbability, n, of users in each battery replacement service area simultaneously arriving at battery replacement stationqThe maximum number of queued vehicles, S, for each battery change machineUSAnd charging the user with the demand point set.
4. The method for planning the electric vehicle centralized charging station and the battery replacing station according to claim 1, wherein the annual construction cost of the battery replacing station includes facility investment cost of the battery replacing station and operation management cost of the battery replacing station;
the annual construction cost mathematical model of the battery replacement station is as follows:
Figure FDA0003343297560000041
wherein, wiThe fixed investment cost for the battery replacing station i; q is the unit price of each battery service desk; n is a radical ofiThe number of battery service desks in the battery replacement station i is counted; g is the conversion coefficient of the related investment cost of the battery replacing machine; r is0The current rate is the current rate; m isiThe operation age of the power change station i is set; sigma1Is an equivalent coefficient of the operation management cost.
5. The method for planning the electric vehicle centralized charging station and the battery replacement station according to claim 4, wherein the facility investment cost of the battery replacement station comprises land rental cost and battery replacement cost.
6. The method for planning the electric vehicle centralized charging station and the battery swapping station according to claim 1, wherein the user swapping cost mathematical model is as follows:
Figure FDA0003343297560000042
wherein d represents the number of days of the year, tbIndicating a period of peak time of daily battery change, IBSRepresenting a collection of battery change stations.
7. The method for planning the electric vehicle centralized charging station and the battery replacement station according to claim 1, wherein the annual construction cost mathematical model of the centralized charging station is as follows:
Figure FDA0003343297560000043
in the formula, ωjCapital investment cost for centralized charging stations j; e.g. of the typejThe number of sets of chargers and related facilities purchased in the centralized charging station j; c is the unit price of the charger facility; r is0The current rate is the current rate; m isjThe operation period of the centralized charging station j; sigma2The method is an equivalent coefficient of the operation management cost of the centralized charging station.
8. The method for planning the electric vehicle centralized charging station and the battery replacement station according to claim 1, wherein the mathematical model of the battery distribution cost is as follows:
Figure FDA0003343297560000044
in the formula, d is annual operation days; gamma is the equivalent coefficient of the battery distribution cost; epsilon is the nonlinear coefficient of the urban highway; m is the standby coefficient of the battery; siThe logistics vehicle number distributed for each day of the battery replacement station i; r isijThe Euclidean distance from the battery replacement station i to the centralized charging station j; i isBSIs a battery replacement station set.
9. A planning device for an electric vehicle centralized charging station and a battery replacement station, which is characterized by comprising a data input/output interface, a processor and a memory storing program instructions, wherein the processor is used for executing the program instructions stored in the memory so as to execute the planning method according to any one of claims 1 to 8.
10. A chip, characterized by comprising a processing unit and a storage unit for storing computer operation instructions, wherein the processing unit is used for executing the planning method according to any one of claims 1 to 8 by calling the computer operation instructions stored in the storage unit.
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