CN115438840B - Electric vehicle battery exchange station site selection optimization method with controllable average waiting time - Google Patents
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Abstract
The invention relates to an electric vehicle battery exchange station site selection optimization method with controllable average waiting time, which comprises the following steps: step S1: based on the behavior preference of the electric automobile driver for replacing the battery, taking the serviceable range and the construction budget of the battery replacement station as constraints, maximizing the battery replacement requirement of the driver as targets, and constructing a battery replacement station site selection optimization model under the background of differentiated service capability; step S2: expanding the power exchange station site selection optimization model into a power exchange station site selection optimization model by using a queuing theory method based on the battery replacement requirement of a driver and the battery replacement time of the power exchange station; step S3: and reconstructing the site selection optimization model of the power exchange station by adopting variable replacement and equivalent transformation, so that the site selection optimization model can be solved by an accurate algorithm. The method disclosed by the invention considers the randomness of the battery replacement requirement of the driver, builds a mathematical optimization model based on queuing theory and facility site selection planning theory, meets the requirement preference and waiting time threshold constraint of the driver, and has the advantages of strong practicability and high service efficiency.
Description
Technical Field
The invention relates to the field of traffic operation management, in particular to an electric vehicle battery replacement station site selection optimization method with controllable average waiting time.
Background
At present, the energy supplementing mode of the pure electric vehicle comprises two modes of charging and power changing. In the charging mode, the electric automobile needs to be parked near a charging facility, and can leave after the electric quantity of the battery reaches a satisfactory state, and the energy supplementing waiting time is more than 2 hours on average; in the power exchange mode, the battery of the electric automobile is allowed to be separated from the automobile body, and the electric automobile can leave only by replacing a full battery in the power exchange station, and the energy supplementing waiting time is not longer than 10 minutes on average. Therefore, the electric automobile in the power conversion mode can greatly shorten the energy supplementing waiting time and improve the vehicle efficiency of a driver.
In 2021, the sales of the electric vehicle in China is about 16 ten thousand, the same ratio is increased by 162%, the sales of the electric vehicle in 2025 is estimated to be 192 ten thousand, and the reserved quantity breaks through 400 ten thousand. Meanwhile, battery replacement demands of battery-replaced automobiles have been on the trend of rapid increase. It has been reported that the phenomenon of battery change-out car drivers having much longer in-line waiting times than battery change-out times in battery change-out stations has been common, with some battery change-out stations having in-line waiting times even exceeding 1 hour. Battery replacement service capability will become a bottleneck limiting the development of the battery replacement mode.
There are two ways to improve battery replacement capability: one is to update the original power change service network capacity by shortening the battery change time and the battery storage capacity in the amplification station; the other is to expand the whole capability of the urban electricity exchange service network by newly creating an electricity exchange station. In the past few years, although the battery replacement speed of a battery replacement station is continuously improved by a battery replacement service providing enterprise, the average time length of battery replacement is shortened from 10 minutes to 5 minutes, and even 3 minutes can be achieved by a part of enterprises, the phenomenon that the waiting time of a driver in a battery replacement station is too long still occurs frequently. In 2021, the holding capacity of the power exchange station in China is only about 1400, and the service capability improved by updating the original power exchange service network cannot adapt to the rapid growth of the current power exchange automobile. Therefore, expanding the overall capability of the urban power conversion service network by utilizing the newly built power conversion station becomes a key to break the bottleneck.
The construction of the power exchange station has the specificity and complexity, such as the need of a matched high-voltage power transmission network around the construction position of the power exchange station, and the like, and a plurality of alternative positions meeting the construction conditions are generally required to be defined under the discussion of government authorities and expert students. How to select the most suitable construction position from a plurality of alternative positions becomes the first problem to be solved when newly constructing a power exchange station. The existing research is generally developed based on a coverage site selection theoretical model (Covering Location Problem), namely, under the constraint that the constructed power exchange station can cover all the battery replacement demands of a driver, a site selection model for minimizing the construction cost of the power exchange station is constructed, or the construction budget of the power exchange station is taken as a model constraint, the site selection model for maximizing the coverage amount of the battery replacement demands is constructed, and finally, a certain heuristic algorithm (such as a genetic algorithm, a simulated annealing algorithm and a neighborhood search algorithm) is designed to solve the problem model, so that the optimal solution or satisfactory solution of the problem is obtained.
Although the existing research can solve the problem of selecting the address of the power exchange station in the background of a certain specific problem, the following disadvantages still exist: (1) Only whether the battery replacement requirement of the driver can be covered by the radiation range of the power exchange station is considered, the problem of the queuing waiting time of the driver after the driver arrives at the power exchange station is not considered, and the phenomenon that the queuing waiting time is too long after the driver arrives at the power exchange station is caused; (2) When the driver is covered by a plurality of stations at the same time, in order to obtain the global optimal solution of the objective function, part of the drivers can be arranged to the stations far away to replace the battery, and the fairness among the drivers is broken. At present, no research has been conducted to provide a solution based on a comprehensive consideration of the above two points.
Disclosure of Invention
In order to solve the technical problems, the invention provides an electric vehicle battery replacement station site selection optimization method with controllable average waiting time.
The technical scheme of the invention is as follows: an electric vehicle battery replacement station site selection optimization method with controllable average waiting time comprises the following steps:
step S1: based on the behavior preference of the electric automobile driver for replacing the battery, taking the serviceable range of the battery replacement station and construction precalculation as constraints, maximizing the battery replacement requirement of the driver, and constructing a battery replacement station site selection optimization model under the background of differentiated service capability;
step S2: based on the battery replacement requirement of the electric automobile driver and when the battery of the power exchange station is replaced, expanding the power exchange station site selection optimization model into a power exchange station site selection optimization model with controllable average waiting time of the driver by using a queuing theory method;
step S3: and reconstructing the power exchange station site selection optimization model by adopting variable replacement and equivalent transformation, so that the power exchange station site selection optimization model can be solved by an accurate algorithm.
Compared with the prior art, the invention has the following advantages:
1. the invention discloses an electric vehicle battery exchange station site selection optimization method with controllable average queuing time, which establishes a mathematical optimization model based on a queuing theory and a facility site selection planning theory and properly processes the randomness of service demands.
2. The method provided by the invention can simultaneously quantify the behavior preference of the driver for replacing the battery and the waiting time of the driver in the battery replacement station, thereby not only meeting the individual demands of the driver, but also reducing the service waiting time of the driver, and having the advantage of high service efficiency.
3. The method provided by the invention can convert a random programming model into a mathematical model which can be solved by an accurate algorithm, can obtain a global optimal solution in a system stable state, and has the characteristic of strong practicability.
Drawings
FIG. 1 is a flow chart of an electric vehicle battery exchange station site selection optimization method with controllable average queuing time in an embodiment of the invention;
FIG. 2 is a schematic diagram of a battery replacement request and an alternative power plant in an embodiment of the invention;
fig. 3 is a schematic diagram of an optimal construction scheme of a power exchange station facility in an embodiment of the present invention.
Detailed Description
The invention provides an electric automobile power exchange station site selection optimization method with controllable average queuing time, which solves the problem of long waiting time of a driver in a power exchange station in terms of facility planning through cross fusion of a facility site selection theory and a queuing theory, and has strong practicability and high service efficiency.
The present invention will be further described in detail below with reference to the accompanying drawings by way of specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1, the method for optimizing the address selection of the electric automobile power exchange station with controllable average queuing time provided by the embodiment of the invention comprises the following steps:
step S1: based on the behavior preference of the electric automobile driver for replacing the battery, taking the serviceable range of the battery replacement station and construction precalculation as constraints, maximizing the battery replacement requirement of the driver, and constructing a battery replacement station site selection optimization model under the background of differentiated service capability;
step S2: based on the battery replacement requirement of the electric automobile driver and when the battery of the power exchange station is replaced, expanding the power exchange station site selection optimization model into a power exchange station site selection optimization model with controllable average waiting time of the driver by using a queuing theory method;
step S3: and reconstructing the site selection optimization model of the power exchange station by adopting variable replacement and equivalent transformation, so that the site selection optimization model can be solved by an accurate algorithm.
In one embodiment, step S1 described above: based on the behavior preference of the electric automobile driver for replacing the battery, taking the service range and construction budget of the battery replacement station as constraints, maximizing the battery replacement requirement of the driver as targets, and constructing a battery replacement station site selection optimization model under the service capability differentiation background, wherein the battery replacement site selection optimization model specifically comprises an objective function (1) and constraint functions (2) - (9):
wherein, each parameter has the following meaning:
i: and (3) a potential position point set meeting the construction condition of the power exchange station in the city, wherein I is E I.
J: the set of location points in the city generated by the driver's battery replacement needs, J e J.
L: and a set of service capability types selectable when the power exchange station is constructed, L epsilon L.
r: the service radius that each station can cover.
d ij : the travel distance from the ith position to the jth position, I e I, J e J.
I j : a subset of set I, meaning
Where r is the service radius.
J i : a subset of set J, meaning
Where r is the service radius.
λ j : the digital signature of the battery change demand generated at the J-th location, J e J.
f i : and (3) constructing fixed cost required by the power exchange station at the ith position point, such as land use cost, I epsilon I.
v il : construction of the ith position pointThe variable cost of a type I power exchange station, such as the cost of construction materials and equipment, I epsilon I and L epsilon L.
b: the available budget of the power exchange station is built.
y i : and the decision variable is used for indicating whether a power exchange station is built on the ith position point, taking 1, or taking 0, and I epsilon I.
x ij : and the decision variable is used for indicating whether the battery replacement requirement service of the ith position point is provided for the battery replacement requirement service of the jth position point, and is 1, otherwise, 0 is provided, I is E I, and J is E J.
z il : and the decision variable is used for indicating whether the ith position point is built with the first type of power exchange station, 1 is taken, or 0 is taken, I epsilon I, and L epsilon L.
In the mathematical model of step S1, the behavior preference of the electric vehicle driver for battery replacement and the differentiation of the battery replacement station type are fully considered, wherein:
the formula (1) is an objective function, and consists of a calculation formula of the satisfied battery replacement requirement of the driver, wherein Max represents a scheme for solving the condition that the satisfied battery replacement requirement of the driver is maximized;
equation (2) indicates that the total investment of the current power exchange station construction scheme cannot exceed the actual budget;
equation (3) shows that a station built in one location can only choose one of a plurality of service types;
equation (4) indicates that the driver can only select one from the stations in all coverage areas to go to and receive battery replacement service;
equation (5) shows that only after the power exchange station is built in a certain location, it is possible to serve the driver nearby;
equation (6) shows that when there are a plurality of stations in the coverage area that are acceptable for service, the driver needs to go to one of the stations closest to it, and the constraint characterizes the driver's preference for performing the battery change, i.e. the driver tends to go to the station closest to it for service;
equation (7) represents the decision variable x ij The value of (2) can only be generated between two integers of 0 and 1;
equation (8) represents the decision variable y i The value of (2) can only be generated between two integers of 0 and 1;
equation (9) represents the decision variable z il The value of (2) can only be generated between two integers, 0 and 1.
In one embodiment, step S2 above: comprehensively considering the characteristics of the battery replacement requirement of a driver and the battery replacement time of the battery replacement station, expanding the mathematical optimization model in the S1 into a battery replacement station site selection optimization model with controllable average waiting time of the driver based on a queuing theory method, and specifically comprising the following steps:
step S21: constructing a digital signature Λ of battery replacement demand faced by a battery replacement station located at point i i From the basic concept of queuing theory method
Step S22: constructing a digital signature u of the mean value of the time required by a battery replacement station at point i to provide battery replacement service i From the basic knowledge of linear programming
Step S23: taking a power exchange station as a service desk, constructing a queuing system based on M/G/1, expanding the mathematical model in the step S1 into a power exchange station site selection optimization model for controlling average waiting time of a driver by adjusting a maximum waiting time threshold parameter, and specifically comprising an objective function (10) and a constraint function (11) - (22):
wherein, each parameter has the following meaning:
i: and (3) a potential position point set meeting the construction condition of the power exchange station in the city, wherein I is E I.
J: the set of location points in the city generated by the driver's battery replacement needs, J e J.
L: and a set of service capability types selectable when the power exchange station is constructed, L epsilon L.
r: the service radius that each station can cover.
d ij : the travel distance from the ith position to the jth position, I e I, J e J.
I j : a subset of set I, meaning
Where r is the service radius.
J i : a subset of set J, meaning
Where r is the service radius.
λ j : the digital signature of the battery change demand generated at the J-th location, J e J.
f i : and (3) constructing fixed cost required by the power exchange station at the ith position point, such as land use cost, I epsilon I.
v il : and constructing the variable cost of the first type of power exchange station at the ith position point, such as the cost of construction materials and equipment, I epsilon I and L epsilon L.
b: the available budget of the power exchange station is built.
y i : and the decision variable is used for indicating whether a power exchange station is built on the ith position point, taking 1, or taking 0, and I epsilon I.
x ij : and the decision variable is used for indicating whether the battery replacement requirement service of the ith position point is provided for the battery replacement requirement service of the jth position point, and is 1, otherwise, 0 is provided, I is E I, and J is E J.
z il : and the decision variable is used for indicating whether the ith position point is built with the first type of power exchange station, 1 is taken, or 0 is taken, I epsilon I, and L epsilon L.
u l : service capacity, L e L, of a first type of station.
u i : the average time required for a battery replacement service at a battery replacement station located at point I, i.e.i.
σ i : acceptance at a station located at point iVariance of time required for battery replacement service, I e I.
Λ i : the digital characteristics of the battery replacement demand faced by the power exchange station at point I, I e I.
T: the maximum waiting time threshold acceptable to the driver after reaching the battery exchange station.
M: the mathematical expression represents a meaning of a large number.
The method adopted in the step S23 fully integrates the processing technology based on the queuing theory, so that the processing technology is expanded into a power exchange station site selection optimization model capable of controlling the average waiting time of a driver by adjusting the maximum waiting time threshold T, wherein:
the formula (10) is an objective function, and consists of a calculation formula of the satisfied battery replacement requirement of the driver, wherein Max represents a scheme for solving the condition that the satisfied battery replacement requirement of the driver is maximized;
equation (11) indicates that the total investment of the current power exchange station construction scheme cannot exceed the actual budget;
equation (12) shows that a station built at one location can only choose one of a number of service types;
equation (13) indicates that the driver can only select one from all the stations in the coverage area to go to and receive battery replacement service;
equation (14) shows that only after the power exchange station is built in a certain location, a nearby driver can be serviced;
equation (15) shows that when there are a plurality of stations in coverage that are acceptable for service, the driver needs to go to the one of the stations closest to it;
equation (16) represents the digital characteristics of the battery replacement demand faced by the station located at point i, characterizing the customer arrival rate of the queuing model with the station as the service facility;
equation (17) represents the digital characteristic of the average time required by the battery replacement service provided by the battery replacement station at point i, and the service level of the battery replacement service provided by the battery replacement station is plotted;
equation (18) indicates that the service capacity of the power exchange station is higher than the total demand of the previous service, otherwise, the model is not established, and the constraint is an objective constraint condition of the queuing theory;
equation (19) shows that the average waiting time of the driver after reaching the power exchange station must be smaller than a preset threshold value T, and the constraint is an important embodiment of the invention for fusing the queuing theory and the facility site selection theory, and the purpose of controlling the average waiting time of the driver in the optimization model can be achieved by adjusting the threshold value T;
equation (20) represents the decision variable x ij The value of (2) can only be generated between two integers of 0 and 1;
equation (21) represents the decision variable y i The value of (2) can only be generated between two integers of 0 and 1;
equation (22) represents the decision variable z il The value of (2) can only be generated between two integers, 0 and 1.
In one embodiment, the step S3: reconstructing the mathematical optimization model in the step S2 by adopting an equivalent transformation and variable replacement technology so that the mathematical optimization model can be solved by an accurate algorithm, wherein the method specifically comprises the following steps:
step S31: and (3) carrying out equivalent transformation on the constraint condition (19) in the mathematical model in the step S23, wherein the equivalent transformation is specifically as follows:
wherein, each parameter has the following meaning:
m: the mathematical expression represents a meaning of a large number.
T: the maximum waiting time threshold acceptable to the driver after reaching the battery exchange station.
u i : the average time required for a battery replacement service at a battery replacement station located at point I, i.e.i.
Λ i : the digital characteristics of the battery replacement demand faced by the power exchange station at point I, I e I.
y i : and the decision variable is used for indicating whether a power exchange station is built on the ith position point, taking 1, or taking 0, and I epsilon I.
σ i : the variance of the time required to receive battery replacement service at the station located at point I, i.e.i.
The formula is the equivalent transformation formula of the constraint condition (19) in the mathematical model of the step S23, and has the same physical meaning as the equivalent transformation formula.
Step S22: three intermediate variables are newly built, specifically as follows:
the step is a specific application of a mathematical variable replacement technology, and three new intermediate variables are produced. The new intermediate variables play an auxiliary role in the model without actual physical meaning.
Step S33: the three intermediate variables of the step S32 are brought into the mathematical expression of the step S31 to obtain the constraint conditions after conversion, which are specifically as follows:
wherein, each parameter has the following meaning:
m: the mathematical expression represents a meaning of a large number.
T: the maximum waiting time threshold acceptable to the driver after reaching the battery exchange station.
u i : the average time required for a battery replacement service at a battery replacement station located at point I, i.e.i.
Λ i : the digital characteristics of the battery replacement demand faced by the power exchange station at point I, I e I.
y i : decision variable, which indicates whether a power exchange station is built at the ith position point, is 1, otherwise0，i∈I.
σ i : the variance of the time required to receive battery replacement service at the station located at point I, i.e.i.
A i : intermediate variables, the mathematical meaning of which is
i∈I
B i : intermediate variables, the mathematical meaning of which is
C i : intermediate variables, the mathematical meaning of which is
The method is a combination and application of equivalent transformation and variable replacement methods, the constraint condition (19) in the mathematical model of the step S23 is reconstructed into an equivalent form in the step, the equivalent form has the same physical meaning as the constraint condition (19) in the mathematical model of the step S23, and meanwhile, due to the transformation in the mathematical form, the constraint can be used for obtaining an optimal solution through general mathematical optimization software.
Step S34: reconstructing the mathematical optimization model in step S23 based on step S32 and step S33 so that it can be solved by an accurate algorithm, specifically including an objective function (23) and a constraint function (24) — (38):
wherein, each parameter has the following meaning:
i: and (3) a potential position point set meeting the construction condition of the power exchange station in the city, wherein I is E I.
J: the set of location points in the city generated by the driver's battery replacement needs, J e J.
L: and a set of service capability types selectable when the power exchange station is constructed, L epsilon L.
r: the service radius that each station can cover.
d ij : the travel distance from the ith position to the jth position, I e I, J e J.
I j : a subset of set I, meaning
Where r is the service radius.
J i : a subset of set J, meaning
Where r is the service radius.
λ j : the digital signature of the battery change demand generated at the J-th location, J e J.
f i : and (3) constructing fixed cost required by the power exchange station at the ith position point, such as land use cost, I epsilon I.
v il : and constructing the variable cost of the first type of power exchange station at the ith position point, such as the cost of construction materials and equipment, I epsilon I and L epsilon L.
b: the available budget of the power exchange station is built.
y i : and the decision variable is used for indicating whether a power exchange station is built on the ith position point, taking 1, or taking 0, and I epsilon I.
x ij : and the decision variable is used for indicating whether the battery replacement requirement service of the ith position point is provided for the battery replacement requirement service of the jth position point, and is 1, otherwise, 0 is provided, I is E I, and J is E J.
z il : decision variables indicating whether the first type is constructed at the ith location pointThe power exchange station takes 1, otherwise takes 0, I epsilon I, L epsilon L.
u l : service capacity, L e L, of a first type of station.
u i : the average time required for a battery replacement service at a battery replacement station located at point I, i.e.i.
σ i : the variance of the time required to receive battery replacement service at the station located at point I, i.e.i.
Λ i : the digital characteristics of the battery replacement demand faced by the power exchange station at point I, I e I.
T: the maximum waiting time threshold acceptable to the driver after reaching the battery exchange station.
M: the mathematical expression represents a meaning of a large number.
A i : intermediate variables, the mathematical meaning of which is
i∈I.
B i : intermediate variables, the mathematical meaning of which is B i ＝Λ i y i ，i∈I.
C i : intermediate variable, its mathematical meaning is C i ＝u i Λ i ，i∈I.
In step S34, the four-constraint programming model in step S23 is converted into a mixed integer quadratic programming model by new variables and constraints generated based on variable substitution, equivalent transformation, constraint reconstruction techniques, wherein:
equation (23) is an objective function, and consists of a calculation equation of the satisfied driver battery replacement demand, and Max represents a scheme for solving the case where the driver battery replacement demand is satisfied by the maximum amount;
equation (24) indicates that the total investment of the current power exchange station construction scheme cannot exceed the actual budget;
equation (25) shows that a station built at one location can only choose one of a number of service types;
equation (26) indicates that the driver is generating a message that only one of the stations in all coverage areas can be selected to go to and receive battery replacement service;
equation (27) shows that only after the power exchange station is built in a certain location, a nearby driver can be serviced;
equation (28) shows that when there are a plurality of stations in the coverage area that are acceptable for service, the driver needs to go to one of the stations closest to it, and the constraint characterizes the driver's preference for performing the battery change service, i.e., the driver tends to go to the station closest to it for service;
equation (29) represents the digital characteristics of the battery replacement demand faced by the station located at point i, characterizing the customer arrival rate of the queuing model with the station as the service facility;
equation (30) represents the digital characteristic of the average time required by the battery replacement service provided by the battery replacement station at point i, and the service level of the battery replacement service provided by the battery replacement station is plotted;
formulas (31) - (33) represent three newly added intermediate variables without actual physical meaning;
equation (34) indicates that the service capacity of the power exchange station is higher than the total demand of the previous service, otherwise, the model is not established, and the constraint is an objective constraint condition of the queuing theory;
equation (35) shows that the average waiting time of the driver after reaching the power exchange station must be less than a certain threshold value T, the constraint is an important embodiment of the invention for fusing queuing theory and facility site selection theory, and the purpose of controlling the average waiting time of the driver in the optimization model can be realized by adjusting the threshold value T;
equation (36) represents the decision variable x ij The value of (2) can only be generated between two integers of 0 and 1;
equation (37) represents the decision variable y i The value of (2) can only be generated between two integers of 0 and 1;
equation (38) represents the decision variable z il The value of (2) can only be generated between two integers, 0 and 1.
The final optimization model provided by the step enables the researched problem to be solved by an accurate algorithm, and operability of the optimization model in the practical problem application process is improved. For example, in the practical application process, the model in step S34 may be brought into commercial optimization software for solving, see below.
The existing power exchange station site selection problem as shown in fig. 2, the studied area consists of 5×5=25 square small lattices with a side length of 10, and the central position of each small lattice is the position where the battery replacement requirement is generated, namely, the position of an asterisk. The number below asterisks is the number for each demand location, i.e., j= {1,2,3, …,25}; the number above the asterisk indicates the demand produced at that location, i.e., λ 1 ＝1，λ 2 ＝1，λ 3 ＝2，λ 4 ＝4，λ 5 ＝2，λ 6 ＝4，λ 7 ＝4，λ 8 ＝5，λ 9 ＝5， λ 10 ＝2，λ 11 ＝2，λ 12 ＝3，λ 13 ＝2，λ 14 ＝5，λ 15 ＝5，λ 16 ＝3，λ 17 ＝2，λ 18 ＝4，λ 19 ＝2，λ 20 ＝3， λ 21 ＝2，λ 22 ＝3，λ 23 ＝5，λ 24 ＝4，λ 25 =5. The junctions between the grids are alternative locations for construction of the exchange station, represented in fig. 2 by solid triangles. The number at the bottom right of triangle ∈ is the number for each alternative position, i.e., i= {1,2,3, …,16}; the upper right digit of triangle ∈ indicates the fixed cost required to build the power exchange station at that location, i.e., f 1 ＝34，f 2 ＝41，f 3 ＝31，f 4 ＝45，f 5 ＝42，f 6 ＝48，f 7 ＝50，f 8 ＝49，f 9 ＝38，f 10 ＝30， f 11 ＝41，f 12 ＝34，f 13 ＝34，f 14 ＝36，f 15 ＝32，f 16 =45; corresponding sigma 1 ＝2，σ 2 ＝2，σ 3 ＝1， σ 4 ＝2，σ 5 ＝2，σ 6 ＝2，σ 7 ＝2，σ 8 ＝1，σ 9 ＝2，σ 10 ＝1，σ 11 ＝2，σ 12 ＝2，σ 13 ＝2，σ 14 ＝1， σ 15 ＝2，σ 16 =1. Two types of power exchange stations exist, namely L= {1,2}, where u 1 ＝10，u 2 =15; and v i1 ＝50，v i2 =100, I e I. Let the service radius be r=20; the maximum waiting time threshold acceptable by the driver after reaching the power exchange station is t=100; the construction budget is 40% of the total cost required to construct the most expensive power exchange station at all locations, i.e. b=892. Distance d between each position point of set I and set J ij Determined by its physical location, in this case the linear distance between the two points.
And (3) bringing the parameters into the model in the step (S34), and calling a mathematical optimization solver, such as Gurobi Version 9.5.2, so as to obtain the optimal construction scheme of the power exchange station facility meeting all constraint conditions, as shown in FIG. 3. Wherein,,
indicating that a first type of power exchange station needs to be built at that location, where i= {1,2,3,6,8,10,11}; triangles are surrounded by squares ∈9, where i= {14,15}, indicating that a second type of station needs to be built at that location; and no power exchange station is built at other positions. In fig. 3, the connection between the asterisks and triangles indicates the service relationship between the demand location and the station location, e.g., the battery replacement demand at the demand location 1,2,6,11 will be directed to station location 1 for service, the battery replacement demand at the demand location 2,5 will be directed to station location 2 for service, and so on. The example is complete. />
Claims (1)
1. An electric vehicle battery exchange station site selection optimization method with controllable average waiting time is characterized by comprising the following steps:
step S1: based on the behavior preference of the electric automobile driver for replacing the battery, taking the service range and construction budget of the battery replacement station as constraints, maximizing the battery replacement requirement of the driver as targets, and constructing a battery replacement station site selection optimization model under the service capability differentiation background, wherein the battery replacement site selection optimization model specifically comprises an objective function (1) and constraint functions (2) - (9):
wherein, each parameter has the following meaning:
i: a potential position point set meeting the construction condition of the power exchange station in the city, I epsilon I;
j: a position point set generated by the battery replacement requirement of a driver in the city, J epsilon J;
l: a set of selectable service capability types when the power exchange station is built, L epsilon L;
r: a service radius that each station can cover;
d ij : the travel distance from the ith position to the jth position, I epsilon I, J epsilon J;
I j : a subset of set I, meaning
Wherein r is the service radius;
J i : a subset of set J, meaning
Wherein r is the service radius;
λ j : the digital characteristic of the battery replacement demand generated at the jth location, J e J;
f i : the fixed cost required by the construction of the power exchange station at the ith position point is I epsilon I;
v il : the variable cost of the first type of power exchange station is built on the ith position point, I epsilon I and L epsilon L;
b: building an available budget of the power exchange station;
y i : decision variables are used for indicating whether a power exchange station is built on the ith position point, 1 is taken, and otherwise 0, I epsilon I is taken;
x ij : the decision variable is used for indicating whether the power exchange station on the ith position point is the battery replacement demand service of the jth position point, 1 is taken, or 0 is taken, I epsilon I, J epsilon J;
z il : decision variables are used for indicating whether a first type of power exchange station is built on an ith position point, 1 is taken, or 0 is taken, I epsilon I and L epsilon L;
step S2: based on the battery replacement requirement of the electric automobile driver and the battery replacement time of the power exchange station, expanding the power exchange station site selection optimization model into a power exchange station site selection optimization model with controllable average waiting time of the driver by using a queuing theory method, and specifically comprising the following steps:
step S21: constructing a digital signature Λ of battery replacement demand faced by a battery replacement station located at point i i From the basic concept of queuing theory method
Step S22: constructing a digital signature u of the mean value of the time required by a battery replacement station at point i to provide battery replacement service i From the basic knowledge of linear programming
Step S23: taking a power exchange station as a service desk, constructing a queuing system based on M/G/1, expanding the power exchange station site selection optimization model into a power exchange station site selection optimization model for controlling average waiting time of a driver by adjusting a maximum waiting time threshold parameter, and specifically comprising an objective function (10) and a constraint function (11) - (22):
wherein, each parameter has the following meaning:
i: a potential position point set meeting the construction condition of the power exchange station in the city, I epsilon I;
j: a position point set generated by the battery replacement requirement of a driver in the city, J epsilon J;
l: a set of selectable service capability types when the power exchange station is built, L epsilon L;
r: a service radius that each station can cover;
d ij : the travel distance from the ith position to the jth position, I epsilon I, J epsilon J;
I j : a subset of set I, meaning
Wherein r is the service radius;
J i : a subset of set J, meaning
Wherein r is the service radius;
λ j : the digital characteristic of the battery replacement demand generated at the jth location, J e J;
f i : the fixed cost required by the construction of the power exchange station at the ith position point is I epsilon I;
v il : the variable cost of the first type of power exchange station is built on the ith position point, I epsilon I and L epsilon L;
b: building an available budget of the power exchange station;
y i : decision variables are used for indicating whether a power exchange station is built on the ith position point, 1 is taken, and otherwise 0, I epsilon I is taken;
x ij : the decision variable is used for indicating whether the power exchange station on the ith position point is the battery replacement demand service of the jth position point, 1 is taken, or 0 is taken, I epsilon I, J epsilon J;
z il : decision variables are used for indicating whether a first type of power exchange station is built on an ith position point, 1 is taken, or 0 is taken, I epsilon I and L epsilon L;
u l : service capacity of the first type of power exchange station, L epsilon L;
u i : the average time required by the battery replacement service is accepted at the battery replacement station at the point I, I epsilon I;
σ i : time required for battery replacement service to be received at power exchange station at point iVariance, i.e.I;
Λ i : the digital characteristic of battery replacement requirements faced by a battery replacement station at point I, I epsilon I;
t: a maximum waiting time threshold acceptable to the driver after reaching the power exchange station;
m: the mathematical expression represents the meaning of a large number;
step S3: reconstructing the power exchange station site selection optimization model by adopting variable replacement and equivalent transformation so that the power exchange station site selection optimization model can be solved by an accurate algorithm, and specifically comprises the following steps:
step S31: equivalent transformation is carried out on the constraint condition (19), and the equivalent transformation is concretely as follows:
wherein, each parameter has the following meaning:
m: the mathematical expression represents the meaning of a large number;
t: a maximum waiting time threshold acceptable to the driver after reaching the power exchange station;
u i : the average time required by the battery replacement service is accepted at the battery replacement station at the point I, I epsilon I;
Λ i : the digital characteristic of battery replacement requirements faced by a battery replacement station at point I, I epsilon I;
y i : decision variables are used for indicating whether a power exchange station is built on the ith position point, 1 is taken, and otherwise 0, I epsilon I is taken;
σ i : the variance of the time required for receiving battery replacement service at the battery replacement station at the point I, I epsilon I;
step S32: three intermediate variables are newly built, specifically as follows:
step S33: bringing the three intermediate variables of step S32 into the mathematical expression of step S31 results in an equivalent constraint, specifically as follows:
wherein, each parameter has the following meaning:
m: the mathematical expression represents the meaning of a large number;
t: a maximum waiting time threshold acceptable to the driver after reaching the power exchange station;
u i : the average time required by the battery replacement service is accepted at the battery replacement station at the point I, I epsilon I;
Λ i : the digital characteristic of battery replacement requirements faced by a battery replacement station at point I, I epsilon I;
y i : decision variables are used for indicating whether a power exchange station is built on the ith position point, 1 is taken, and otherwise 0, I epsilon I is taken;
σ i : the variance of the time required for receiving battery replacement service at the battery replacement station at the point I, I epsilon I;
A i : intermediate variable, its mathematical meaning is A i ＝u i 2 ,i∈I；
B i : intermediate variables, the mathematical meaning of which is
C i : intermediate variables, the mathematical meaning of which is
Step S34: reconstructing the optimization model of the site selection of the power exchange station in the step S23 based on the step S32 and the step S33 so that the optimization model can be solved by an accurate algorithm, wherein the optimization model specifically comprises an objective function (23) and a constraint function (24) -38:
wherein, each parameter has the following meaning:
i: a potential position point set meeting the construction condition of the power exchange station in the city, I epsilon I;
j: a position point set generated by the battery replacement requirement of a driver in the city, J epsilon J;
l: a set of selectable service capability types when the power exchange station is built, L epsilon L;
r: a service radius that each station can cover;
d ij : the travel distance from the ith position to the jth position, I epsilon I, J epsilon J;
I j : a subset of set I, meaning
Wherein r is the service radius;
J i : a subset of set J, meaning
Wherein r is the service radius;
λ j : the digital characteristic of the battery replacement demand generated at the jth location, J e J;
f i : the fixed cost required by the construction of the power exchange station at the ith position point is I epsilon I;
v il : the variable cost of the first type of power exchange station is built on the ith position point, I epsilon I and L epsilon L;
b: building an available budget of the power exchange station;
y i : decision variables are used for indicating whether a power exchange station is built on the ith position point, 1 is taken, and otherwise 0, I epsilon I is taken;
x ij : the decision variable is used for indicating whether the power exchange station on the ith position point is the battery replacement demand service of the jth position point, 1 is taken, or 0 is taken, I epsilon I, J epsilon J;
zi l : decision variables are used for indicating whether a first type of power exchange station is built on an ith position point, 1 is taken, or 0 is taken, I epsilon I and L epsilon L;
u l : service capacity of the first type of power exchange station, L epsilon L;
u i : the average time required by the battery replacement service is accepted at the battery replacement station at the point I, I epsilon I;
σ i : the variance of the time required for receiving battery replacement service at the battery replacement station at the point I, I epsilon I;
Λ i : the digital characteristic of battery replacement requirements faced by a battery replacement station at point I, I epsilon I;
t: a maximum waiting time threshold acceptable to the driver after reaching the power exchange station;
m: the mathematical expression represents the meaning of a large number;
A i : intermediate variables, the mathematical meaning of which is
B i : intermediate variables, the mathematical meaning of which is B i ＝Λ i y i ，i∈I；
C i : intermediate variable, its mathematical meaning is C i ＝u i Λ i ，i∈I。
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503845A (en) * | 2016-10-21 | 2017-03-15 | 国网山东省电力公司烟台供电公司 | A kind of charging station method of allocation plan that is schemed based on V with HS algorithms |
| CN108764634A (en) * | 2018-04-24 | 2018-11-06 | 河海大学 | A kind of electric automobile charging station dynamic programming method for considering charge requirement and increasing |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102521488A (en) * | 2011-11-28 | 2012-06-27 | 山东电力集团公司济南供电公司 | Electromobile power exchanging station site selection method |
| CN105809278A (en) * | 2016-03-03 | 2016-07-27 | 华北电力大学（保定） | Queuing theory algorithm based electric vehicle power change station's location choosing and planning method |
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| CN111651899A (en) * | 2020-06-28 | 2020-09-11 | 北京理工大学 | Robust site selection and volume determination method and system for power conversion station considering user selection behavior |
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| CN108764634A (en) * | 2018-04-24 | 2018-11-06 | 河海大学 | A kind of electric automobile charging station dynamic programming method for considering charge requirement and increasing |
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