CN113780659A - Electric vehicle charging station layout method based on double-layer planning model and electronic equipment - Google Patents

Electric vehicle charging station layout method based on double-layer planning model and electronic equipment Download PDF

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CN113780659A
CN113780659A CN202111066707.9A CN202111066707A CN113780659A CN 113780659 A CN113780659 A CN 113780659A CN 202111066707 A CN202111066707 A CN 202111066707A CN 113780659 A CN113780659 A CN 113780659A
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曾凯文
刘嘉宁
王可
林斌
杜斌
苏卓
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Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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Abstract

The application discloses an electric vehicle charging station layout method based on a double-layer planning model, electronic equipment and a storage medium, wherein the method comprises the following steps: establishing a preference model selected by the user charging station according to the geographical position of the user and the distribution condition of the arranged charging stations; a calculation method for obtaining user satisfaction based on a preference model is used for establishing a first objective function, a first constraint condition and a first penalty function of an upper layer model; calculating 24-moment charging demand distribution of each charging station based on the preference model, and establishing a second objective function, a second constraint condition and a second penalty function of the lower layer model; solving the double-layer planning model based on a particle swarm optimization algorithm, and outputting a layout scheme of the electric vehicle charging station according to a solving result; the double-layer planning model comprises an upper layer model and a lower layer model. This application can evaluate the utilization ratio, the recovery period, traffic attraction ability, the service level etc. of charging station to in the planning of conveniently considering the charging station from many.

Description

Electric vehicle charging station layout method based on double-layer planning model and electronic equipment
Technical Field
The application relates to the technical field of electric vehicle charging station planning, in particular to an electric vehicle charging station layout method and electronic equipment based on a double-layer planning model.
Background
In recent years, electric vehicles powered by clean energy have become important in research and market direction to replace traditional fossil-powered vehicles for the purpose of alleviating energy crisis and reducing carbon emission. By 2030, the global market share of the electric vehicle is estimated to be up to 30%, and at this popularity, whether the supporting facilities of the electric vehicle, such as a charging station, are perfect will directly affect the use experience of the electric vehicle users.
At present, a great deal of literature is available for researching electric vehicle charging station planning, and the research includes site selection and volume fixing of the electric vehicle charging station, orderly charging of the electric vehicle, influence of electric vehicle access on a large power grid and the like. Currently, most of charging station plans related to electric vehicles are single-layer plans, and only interests of a single group (such as governments) are considered, however, in practical plans, as most of charging stations are private, operators want their own charging stations to obtain maximum interests, and at the same time, the governments want to maximize social interests, namely, the problem of balancing multi-layer multi-interest subjects exists that government leadership plans and interests of a plurality of operators on the lower layer are mutually restricted.
At present, research on the problems is less, and under the condition that the market share of the electric automobiles in the future is increased rapidly, a feasible solution model needs to be provided for the conditions.
Disclosure of Invention
The application provides an electric vehicle charging station layout method based on a double-layer planning model, electronic equipment and a storage medium, and aims to solve the problem that in the prior art, the planning of a charging station can only meet the benefit of a single group.
In order to solve the technical problem, the application provides an electric vehicle charging station layout method based on a double-layer planning model, which includes: establishing a preference model selected by the user charging station according to the geographical position of the user and the distribution condition of the arranged charging stations; a calculation method for obtaining user satisfaction based on a preference model, and establishing a first objective function, a first constraint condition and a first penalty function of an upper model; calculating 24-moment charging demand distribution of each charging station based on the preference model, and establishing a second objective function, a second constraint condition and a second penalty function of the lower layer model; solving the double-layer planning model based on a particle swarm optimization algorithm, and outputting a layout scheme of the electric vehicle charging station according to a solving result; the double-layer planning model comprises an upper layer model and a lower layer model.
In order to solve the above technical problem, the present application provides an electronic device, which includes a memory and a processor, where the memory is connected to the processor, and the memory stores a computer program, and the computer program is executed by the processor to implement the above electric vehicle charging station layout method based on a dual-layer planning model.
In order to solve the above technical problem, the present application provides a computer-readable storage medium, and a computer program is executed to implement the above electric vehicle charging station layout method based on a dual-layer planning model.
The application provides an electric vehicle charging station layout method based on a double-layer planning model, electronic equipment and a storage medium, the double-layer planning model is established based on a preference model, user satisfaction and 24-moment charging demand distribution of each charging station, and benefits of governments and electric vehicle charging station operators are fully considered in the planning process. On the basis, the application also provides an improved particle swarm algorithm based on the penalty function to solve the nonlinear and non-convex double-layer programming model. In summary, the method and the device can be applied to single-center small and medium-sized cities, and the device utilization rate, the recovery period, the traffic attraction capacity, the service level and the like of the cities are evaluated under three scenes, so that planning of charging stations can be conveniently considered from multiple aspects.
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In order to more clearly illustrate the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a method for arranging an electric vehicle charging station based on a two-tier planning model according to the present application;
FIG. 2 is a flowchart illustrating an embodiment of step S140 in FIG. 1;
FIG. 3 is a schematic structural diagram of an embodiment of an electronic device of the present application;
FIG. 4 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.
Detailed Description
In order to enable those skilled in the art to better understand the technical solution of the present application, the electric vehicle charging station layout method, the electronic device, and the storage medium based on the two-tier planning model provided in the present application are further described in detail below with reference to the accompanying drawings and the detailed description.
Referring to fig. 1-2, fig. 1 is a schematic flow chart of an embodiment of a method for arranging electric vehicle charging stations based on a double-layer planning model according to the present application, the method for arranging electric vehicle charging stations based on a double-layer planning model may include steps S110 to S140, and fig. 2 is a schematic flow chart of an embodiment of step S140 in fig. 1.
S110: and establishing a preference model selected by the user charging station according to the geographical position of the user and the distribution condition of the arranged charging stations.
Alternatively, the geographical location of the user and the distribution of the deployed charging stations may be represented using a logistic model.
Suppose user n has geographic coordinates of (x) when it generates a charging demandn,yn) Then the possibility that he is selecting charging station i for charging is:
Figure BDA0003256488810000031
wherein the geographical coordinates (x) of the charging stationi,yi) I is the total number of all charging stations; wi nProbability of selecting the i-th charging station for user n,
Figure BDA0003256488810000032
for the utility of the user n in selecting the charging station i, it is assumed that the selection of the user is based on the utility of the charging station, i.e. the user's travel cost, charging cost (expected queuing time and charging price), and perceived cost (anxiety degree), so the formula of the calibration function is as follows:
Figure BDA0003256488810000033
in formula (2):
Figure BDA0003256488810000034
it may be understood that the total cost of selecting the ith charging station for user n; thetai(i ═ 1,2,3,4,5) are preference parameters that influence the user's choice; λ is a travel distance cost conversion coefficient, d ((x)n，yn),(xi，yi) Is the shortest distance, S, from the user to the charging station i, determined according to the Dijkstra algorithmi,tFor the size of the charging station i, mu is the estimated queuing time and sizeEstimating the coefficient, Pi,tFor charging station i, SOCnV is a conversion coefficient for estimating a user anxiety value by using the residual electric quantity for the current vehicle residual electric quantity; epsilonnFor other costs, it is simplified here to ignore some unimportant costs.
S120: and obtaining a calculation method of user satisfaction based on the preference model, and establishing a first objective function, a first constraint condition and a first penalty function of the upper layer model.
User satisfaction is closely related to selection cost, and the user desires to select the largest
Figure BDA0003256488810000035
The charging stations of value, and thus the charging station number selected by user n, are:
Figure BDA0003256488810000036
in the formula (3), the function argmax (f (x)) is a value at which x is obtained when f (x) takes a maximum value, in which case f (x) ═ Wi n，
Figure BDA0003256488810000037
Is the possibility of the user n selecting the charging station i.
The user satisfaction degree is composed of a plurality of factors such as travel cost, queuing time cost, charging cost and the like, wherein the travel cost is directly related to the distance between the user and the CS, the queuing cost is simplified to be a function of the CS scale, and the charging cost is related to the SOC and the CS charging price. The mathematical equation describing the user satisfaction at time t is:
Figure BDA0003256488810000038
in the formula (4), the reaction mixture is,
Figure BDA0003256488810000039
is the total user satisfaction at time t, QtTotal number of vehicles having a charging demand at time t, δi(i ═ 1,2,3) is a weighting parameter that affects user satisfaction, λ2And mu2Are all conversion factors
Figure BDA00032564888100000310
Refers to the movement from the vehicle to the selected charging station
Figure BDA00032564888100000311
The distance of (a) to (b),
Figure BDA00032564888100000312
refers to the selected charging station
Figure BDA00032564888100000313
The scale of (a) is,
Figure BDA00032564888100000314
refers to the selected charging station
Figure BDA00032564888100000315
Charging price of, SOCnIs the battery state of the vehicle n,
Figure BDA00032564888100000316
and
Figure BDA00032564888100000317
are weighing parameters that affect the cost of charging.
The upper level of the double-layer planning model is the leader of the government, and the target of the model is determined by the requirements of the government. From the government perspective, it is expected that newly-built charging stations can bring maximum benefits to the society, and the objective of the upper-layer optimization model is to optimize the charge price, so as to improve the user satisfaction to the maximum extent, reduce the price difference among the charging stations to the maximum extent, reduce the overall charge price of the charging stations to the maximum extent, reduce the construction cost of the charging stations to the maximum extent, and improve the equipment utilization rate to the maximum extent. The guidance of the upper-level government to the lower-level operator in the model is determined by the value of the electric power sale price guide, the lower-level operator automatically selects the charging exhibition scale after obtaining the electric power sale price given by the government and feeds the scale back to the upper-level government, and a government decision maker determines the optimized electric power sale price according to the fed-back capacity, so that iteration is repeated until the convergence condition is met.
Due to the charging station capacity c fed back at the lower layerjAfter the charging pile is fixed, the utilization rate and the construction cost of the charging pile are determined, so that the upper layer part of the double-layer optimization model problem finally used for solving comprises an upper layer model, and the aims of maximizing the user satisfaction, minimizing the price difference among the charging stations and minimizing the total charging price of all the charging stations are fulfilled.
Figure BDA0003256488810000041
In the formula (5), ci,tDenotes the size of the charging station i, p, during ti,tIndicating the direct charge price of the charging station i during the time t,
Figure BDA0003256488810000042
is the total user satisfaction at time t;
Figure BDA0003256488810000043
variance, CP, representing the price charged by a plurality of charging stations during time ttRepresenting the sum of the prices of the charging stations, alpha, during time t1，α2，α3Is the weight of the objective function.
The variance of the charge prices of all charging stations is used to reflect the discreteness of the charge prices. The second part of the upper model object is:
Figure BDA0003256488810000044
further, the sum of the charged prices of the charging stations is:
CPt＝SUM(Pi,t) … … type (7)
Thus, the first objective function of the upper model is:
Figure BDA0003256488810000045
since the overall goal of the method is to find a charged price vector and a charging station capacity vector that satisfy the objective function. Price and capacity are obviously non-negative. If the optimization function extends over the entire space, there may be local poles outside the target space, which will cause some heuristics to find the optimization results outside the target space during the calculation. Therefore, if the particle swarm optimization algorithm is used as the optimization method, it is necessary to add a penalty term to the optimization function to penalize those particles that "escape" to the negative limit. The penalty function Pen is as follows:
Figure BDA0003256488810000046
wherein p isiIndicating the lead electricity selling price of the ith charging station.
The penalty function is used to control the price of each charging station to be non-negative. When the price of a certain charging station is positive, the value of the term is less than 1, and almost no penalty is given to the first objective function value, and when the price of the charging station is negative, the term becomes very large, and the larger the absolute value of the negative number is, the larger the penalty term is, so that the particle swarm is guided to tend to be the positive dividend.
In summary, the optimization objective function of the upper model is
Figure BDA0003256488810000051
The first constraint of the upper layer model is as follows:
Pi,min≤Pi,t≤Pi,max… … type (11)
VAR(Pi,t)≤Vmax… … type (12)
Wherein, Pi,minIs the lowest price of the charging station; pi,maxIs the highest price of the charging station; vmaxIs the maximum variance of the charge price; the first constraint will beThe charged price is limited in the range of the lowest price and the highest price; the maximum variance of the charging station charge price is also defined.
S130: and calculating the 24-time charging demand distribution of each charging station based on the preference model, and establishing a second objective function, a second constraint condition and a second penalty function of the lower layer model.
The lower-layer model of the double-layer planning model takes operators as decision makers, and each operator hopes that construction cost is minimum and charging station profit is maximum. The construction cost comprises the ground cost and the equipment cost of the charging station, and can be calculated by the following formula:
Figure BDA0003256488810000052
in formula (13), Mi,tBecause the construction cost, chi, is land rental expense, and the land rental expense is actually related to the land type and the land scale, chi ═ etac can also be usediIs represented by ci,tFor charging station capacity, γ is the cost per additional charge pile, c0Is a fixed cost and is independent of capacity. Due to c0Fixed, so it can be left out at optimization, reducing the amount of computation. Namely the following formula:
Mi,t＝(η+γ)ci,t… … type (14)
In view of the service life of the charging station equipment, the daily average cost
Figure BDA0003256488810000053
Is composed of
Figure BDA0003256488810000054
Wherein eta is the land lease cost per unit area; and T is the total life cycle of the equipment.
The daily profit of the charging station is related to the customer volume, and first, the number of vehicles arriving at charging station i at time t needs to be calculated. Suppose that the total number of vehicles waiting for charging at time t is QtThen select at time tThe number of users at the ith charging station is determined by:
Figure BDA0003256488810000061
in the formula (16), Di,tIs the number of vehicles driving to charging station i, Wi nIs the possibility of the user n selecting the charging station i.
The hourly income of the charging station is obtained by subtracting the electricity purchase cost from the turnover, and for the operator i, the income R at the time ti,tComprises the following steps:
Figure BDA0003256488810000062
in summary, the second objective function of the lower model is:
Figure BDA0003256488810000063
similarly, the second objective function, after adding the second penalty function, is:
Figure BDA0003256488810000064
the second constraint conditions of the lower layer model are:
Si,min≤Si,t≤Si,max… … type (20)
Figure BDA0003256488810000065
Figure BDA0003256488810000066
Wherein S isi,minIs the minimum size of the charging station, Si,maxIs the maximum size of the charging station, RiIs a charging stationThe average day of income.
S140: solving the double-layer planning model based on a particle swarm optimization algorithm, and outputting a layout scheme of the electric vehicle charging station according to a solving result; the double-layer planning model comprises an upper layer model and a lower layer model.
Preferably, the weight factors in the upper and lower layer objective functions are assigned, and after multiple times of manual debugging, the [ alpha ] is found1,α2,α3]＝[0.002,20,1]、[β1,β2,β3]＝[1,0.1,0.1]In time, the result orders of magnitude of each sub-target in the target function are similar, and the function response is sensitive.
Step S140 may further include step S141 to step S143, which are specifically as follows:
s141: initializing parameters of the particle swarm algorithm.
An initial solution of a bottom layer planning model is established, because a double-layer planning model is more complex, the number of particle swarms is set to be 100, a learning factor is 2 (obtained by multiple debugging), a termination condition is set to be 800, and an upper layer solution space (price interval) is set to be [40,100 ]]The lower solution space (scale interval) is [50,200%]. Randomly initializing the position X of the particle in the particle swarm after the above parameters are setjVelocity Vj，j∈[1,1000]Let PjIs the current position of the jth particle, PgIs the optimal position of the particles in the initial population.
S142: and updating the position and the speed of the particle swarm according to the parameters.
1) The position and velocity of the particles are updated according to the following equations (23-25).
Figure BDA0003256488810000071
Figure BDA0003256488810000072
Figure BDA0003256488810000073
In the above formula: r is1And r2Random numbers between 0 and 1, commonly referred to as learning factors; obtaining the learning factor c after multiple times of debugging1＝c22 is optimal; omega is an inertia weight and is used for balancing the local optimization capacity and the global optimization capacity, and the value is generally between 0.1 and 0.9, and the value is reduced along with the increase of the iteration times; omegamaxThe largest weight; omegaminIs the smallest weight. k is the current iteration number of the algorithm, itermaxIs the maximum value of the iteration number of the algorithm.
2) The position X of the jth particle in the upper layer planning modeljSubstituting the optimal scale matrix into the lower-layer planning model, and obtaining the optimal scale matrix y of each charging station of the lower-layer planning model by adopting a traditional optimization methodj*。
3) Substituting the obtained preliminary pricing strategy matrix and the scale matrix into an upper layer objective function and a lower layer objective function, and calculating an adaptive value F (X) of the jth particlej,yj*),j∈[1,m](ii) a If the adaptation value F (X) of the ith particlej,yj*) Better than the current position P of the particlejUpdating the position of the jth particle to XjAnd use y in combinationj*In place of PjOptimal solution yP obtained in lower-layer planning modelj(ii) a Calculating PgIf the fitness of P is less than the fitness value of the jth particle, P is addedgIs updated to Xj，yPgIs updated to yj*。
S143: and judging whether the position and the speed of the particle swarm meet preset conditions or not.
Judging whether the absolute value of the two objective function values is less than 10-8If yes, go to step S144, otherwise, update P according to equation (26) by using the conventional optimization methodgAnd calculate yPg：
Figure BDA0003256488810000074
S144: and outputting the pricing matrix and the scale matrix as a solution of the double-layer planning model.
Output PgPricing matrices derived for the upper model, and yPgAnd obtaining a scale matrix for the lower layer model, and calculating objective function values corresponding to the upper layer planning model and the lower layer planning model.
Through verification, the particle swarm based double-layer programming model solving method can be converged to a solution through 100-300 times of iteration numbers.
The method adopts a double-layer planning model form, and fully considers the benefits of governments and operators of electric vehicle charging stations in the planning process. From the perspective of social welfare maximization, the government serves as an upper-layer decision maker to optimize a charge price matrix, and the charge price matrix is used as a transfer variable to indirectly influence the decision of a lower-layer operator. And then, the lower-layer operator determines the scale according to the goal of maximizing the self income and feeds back the scale matrix to the upper-layer operator. The Logit model is applied to predict the preference of the user when selecting the charging station. On the basis, an improved particle swarm algorithm based on a penalty function is provided for solving the nonlinear non-convex double-layer programming model. The two-stage planning model provided by the invention is applied to a single-center small and medium-sized city, the equipment utilization rate, the recovery period, the traffic suction capacity, the service level and the like of the single-center small and medium-sized city are evaluated under three scenes, and the result shows that the model runs well under a typical CSs distribution scene, the station recovery period is reasonable (average 6.5 years), the equipment utilization rate is high and reaches 44.32%.
Based on the above electric vehicle charging station layout method based on the double-layer planning model, the present application also provides an electronic device, as shown in fig. 3, and fig. 3 is a schematic structural diagram of an embodiment of the electronic device of the present application. The electronic device 300 may comprise a memory 31 and a processor 32, the memory 31 being connected to the processor 32, the memory 31 having stored therein a computer program, the computer program implementing the method of any of the above embodiments when executed by the processor 32. The steps and principles thereof have been described in detail in the above method and will not be described in detail herein.
In the present embodiment, the processor 32 may also be referred to as a Central Processing Unit (CPU). The processor 32 may be an integrated circuit chip having signal processing capabilities. The processor 32 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Based on the electric vehicle charging station layout method based on the double-layer planning model, the application also provides a computer readable storage medium. Referring to fig. 4, fig. 4 is a schematic structural diagram of an embodiment of a computer-readable storage medium according to the present application. The computer-readable storage medium 400 has stored thereon a computer program 41, the computer program 41 implementing the method of any of the above embodiments when executed by a processor. The steps and principles thereof have been described in detail in the above method and will not be described in detail herein.
Further, the computer-readable storage medium 400 may be various media that can store program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic tape, or an optical disk.
It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. In addition, for convenience of description, only a part of structures related to the present application, not all of the structures, are shown in the drawings. The step numbers used herein are also for convenience of description only and are not intended as limitations on the order in which the steps are performed. 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 application.
The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.

Claims (10)

1. An electric vehicle charging station layout method based on a double-layer planning model is characterized by comprising the following steps:
establishing a preference model selected by the user charging station according to the geographical position of the user and the distribution condition of the arranged charging stations;
a calculation method for obtaining user satisfaction based on the preference model, and establishing a first objective function, a first constraint condition and a first penalty function of an upper layer model;
calculating 24-moment charging demand distribution of each charging station based on the preference model, and establishing a second objective function, a second constraint condition and a second penalty function of the lower layer model;
solving the double-layer planning model based on a particle swarm optimization algorithm, and outputting a layout scheme of the electric vehicle charging station according to a solving result; wherein the two-tier planning model includes the upper tier model and the lower tier model.
2. The double-layer planning model-based electric vehicle charging station layout method according to claim 1, wherein solving the double-layer planning model based on the particle swarm optimization algorithm comprises:
initializing parameters of a particle swarm algorithm;
updating the position and the speed of the particle swarm according to the parameters;
and judging whether the position and the speed of the particle swarm meet preset conditions, if so, outputting a pricing matrix and a scale matrix as the solution of the double-layer planning model.
3. The electric vehicle charging station layout method based on the double-layer planning model according to claim 2, wherein the establishing of the preference model of the user charging station selection according to the geographical location of the user and the distribution of the arranged charging stations comprises:
if the geographic coordinate of the user n when generating the charging demand is (x)n，yn) Then, the possibility that the user n is selecting the charging station i for charging is:
Figure FDA0003256488800000011
wherein the geographic coordinate of the charging station is (x)i，yi) (ii) a I is the total number of all charging stations;
Figure FDA0003256488800000012
probability of selecting the i-th charging station for user n,
Figure FDA0003256488800000013
selecting a total cost for the ith charging station for user n;
Figure FDA0003256488800000014
θi(i ═ 1,2,3,4,5) is a preference parameter that affects user n's choice; λ is a travel distance cost conversion coefficient, d ((x)n，yn)，(xi，yi) Is the shortest distance, S, from user n to charging station i, determined according to the Dijkstra algorithmi，tFor the scale of the charging station i, μ is the estimated coefficient of the budget queuing time and scale, Pi，tFor charging station i, SOCnV is a conversion coefficient for estimating a user anxiety value by using the residual electric quantity for the current vehicle residual electric quantity; epsilonnFor other costs.
4. The electric vehicle charging station layout method based on the double-layer planning model according to claim 3, wherein the calculation method for obtaining the user satisfaction degree based on the preference model and establishing the first objective function, the first constraint condition and the first penalty function of the upper layer model comprises:
the first objective function max f (c)i，t，pi，t) Comprises the following steps:
Figure FDA0003256488800000021
wherein alpha is1，α2，α3Is the weight of the first objective function;
Figure FDA0003256488800000022
is the total user satisfaction at time t;
Figure FDA0003256488800000023
variance, CP, representing the price charged by a plurality of charging stations during time ttRepresenting the sum of prices of all charging stations in the time t;
the first constraint condition is as follows: pi，min≤Pi，t≤Pi，max；
Figure FDA0003256488800000024
Wherein, Pi，minIs the lowest price of the charging station; pi，maxIs the highest price of the charging station; vmaxIs the maximum variance of the charge price;
the first penalty function Pen is:
Figure FDA0003256488800000025
wherein p isiIndicating the lead electricity selling price of the ith charging station.
5. The electric vehicle charging station layout method based on the two-tier planning model according to claim 4,
Figure FDA0003256488800000026
wherein, deltai(i ═ 1,2,3) is a weight parameter that affects user satisfaction, QtTotal number of vehicles with charging demand at time t, λ2And mu2Are all conversion factors
Figure FDA0003256488800000027
Refers to the movement from the vehicle to the selected charging station
Figure FDA0003256488800000028
The distance of (a) to (b),
Figure FDA0003256488800000029
refers to the selected charging station
Figure FDA00032564888000000210
The scale of (a).
Figure FDA00032564888000000211
Refers to the selected charging station
Figure FDA00032564888000000212
Charging price of, SOCnIs the battery state of the vehicle n,
Figure FDA00032564888000000213
and
Figure FDA00032564888000000214
are weighing parameters that affect the cost of charging.
6. The electric vehicle charging station layout method based on the two-tier planning model according to claim 5, wherein the calculating a 24-time charging demand distribution of each charging station based on the preference model and establishing a second objective function, a second constraint condition and a second penalty function of a lower-tier model comprises:
said second objective function max gi，t(ci，t，pi，t) Comprises the following steps:
Figure FDA00032564888000000215
wherein R isi，tIs the t hour income for charging station i;
Figure FDA00032564888000000216
is the daily average cost of the charging station;
the second objective function added with the second penalty function is:
Figure FDA00032564888000000217
wherein, beta1，β2，β3Is a weight of the second objective function;
the second constraint condition is as follows: si，min≤Si，t≤Si，max；
Figure FDA00032564888000000218
Wherein S isi，minIs the minimum size of the charging station, Si，maxIs the maximum size of the charging station, RiIs the daily average income of the charging station.
7. The electric vehicle charging station layout method based on the two-tier planning model according to claim 6,
Figure FDA0003256488800000031
wherein M isi，tThe construction cost is reduced; x is land rental fee, and X is eta ci(ii) a Gamma is the cost of one more charging pile; c. Ci，tIs the charging station capacity; c. C0For fixed costs; eta is the land rental cost per unit area; and T is the total life cycle of the equipment.
8. The electric vehicle charging station layout method based on the two-tier planning model according to claim 7,
setting a weight [ alpha ] of a first objective function1，α2，α3]＝[0.002，20，1]；
Setting a weight [ beta ] of a second objective function1，β2，β3]＝[1，0.1，0.1]。
9. An electronic device, comprising a memory and a processor, wherein the memory is connected to the processor, and the memory stores a computer program, and the computer program is executed by the processor to implement the electric vehicle charging station layout method based on the two-tier planning model according to any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that a computer program is stored, which when executed implements the double-deck planning model-based electric vehicle charging station layout method according to any of claims 1 to 8.
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