CN115099462A - Optimal layout method for electric vehicle charging station - Google Patents
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Abstract
The invention discloses an optimal layout method for an electric vehicle charging station, which comprises the following steps: acquiring the charging requirement of the electric automobile in a region; initializing basic parameters of a hybrid optimization algorithm DEPSO; let the number of building stations N equal to N st,min Generating an initial population; setting the iteration time t as 0, constructing a Voronoi diagram by taking the address of the charging station as the center, dividing the service range of the charging station in the region according to the Voronoi diagram, judging whether the service range meets the preset constraint condition or not, and resetting the step if the service range does not meet the preset constraint condition; optimizing individual positions of individuals of the initial population and recording corresponding fitness; judging whether the iteration number t is equal to the maximum iteration number t max If not, let t be t + 1; if yes, judging that the station building number N isWhether or not to be equal to the maximum number of building stations N st,max If not, making N equal to N + 1; if yes, the maximum iteration number t is determined max Outputting the optimized individual positions and the corresponding fitness as an optimized layout result; the invention can realize the optimized layout of the charging station, thereby guiding the layout and construction of the charging service facility.
Description
Technical Field
The invention relates to an optimal layout method for an electric vehicle charging station, and belongs to the technical field of electric vehicles.
Background
The perfection of public facilities is the foundation for promoting the large-scale popularization of electric automobiles. The charging facility market is a market which has large early investment and is difficult to generate economic benefits in a short period. As a public service facility, the electric vehicle charging station needs to be restrained in multiple aspects when determining the position and the capacity, the charging demand distribution of the electric vehicle affected by travel time and space needs to be considered to improve the charging convenience, the traffic land guarantee construction feasibility is considered, the safety is improved by considering the power grid planning requirement, the construction cost, the operation cost and the like are considered to improve the economy, the blind construction investment is avoided, and the utilization rate and the income of the charging facility are improved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an optimal layout method for an electric vehicle charging station, which can perform optimal layout of the charging station so as to guide the layout and construction of charging service facilities.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
the invention provides an optimal layout method for an electric vehicle charging station, which comprises the following steps:
acquiring charging demand points of the electric automobile in an area and daily charging demand on each charging demand point;
initializing basic parameters of a hybrid optimization algorithm DEPSO, wherein the basic parameters comprise a population size M and a maximum iteration number t max Iteration stagnation times tau, initial inertia weight omega start Terminating the inertial weight ω end Learning factor c 1 And c 2 Scale factor F, crossover factor CR, maxNumber of stations built N st,max And the minimum number of station building N st,min ；
Let the number of building stations N equal to N st,min Randomly extracting a charging station address from a charging demand point to generate an initial population, and determining the individual position and speed of the initial population, wherein the individual position comprises the charging station address and the capacity;
setting the iteration time t as 0, constructing a Voronoi diagram by taking the address of the charging station as the center, dividing the service range of the charging station in the region according to the Voronoi diagram, judging whether the service range meets the preset constraint condition or not, and resetting the step if the service range does not meet the preset constraint condition;
carrying out optimization on individual positions of individuals of the initial population and recording corresponding fitness, wherein the optimization comprises the steps of carrying out variation, hybridization and selection operations on the individuals of the initial population, carrying out position and speed updating on the operated individuals, and carrying out variation, hybridization and selection operations on the updated individuals;
judging whether the iteration number t is equal to the maximum iteration number t max If not, making the iteration time t equal to t +1, updating the initial population according to the optimized individual position of the iteration time t, and performing iteration;
if yes, judging whether the station building number N is equal to the maximum station building number N or not st,max If not, the station building number N is made to be N +1, and the maximum iteration time t is used max Updating the initial population at the optimized individual position and performing iteration;
if yes, the maximum iteration number t is determined max And outputting the optimized individual positions and the corresponding fitness as an optimized layout result.
Optionally, the maximum number of building stations N st,max And the minimum number of station building N st,min Comprises the following steps:
where ceil (-) is an upward rounding function, P demand Is the sum of the daily charge demands, S, in the region max And S min Maximum and minimum for a single charging station capacity.
Optionally, the determining the positions of the individuals of the initial population includes generating by using a uniformly distributed random function, where the position of the individual i is a value of the dimension j
Comprises the following steps:
wherein t is 0 and rand (0,1) is [0,1 ]]Random numbers, x, obeying a uniform distribution within the interval j,max 、x j,min The upper and lower limits of the variable of the dimension j are j 1, and 2 are the address and the capacity of the charging station respectively.
Optionally, the service range of the charging station in the area is as follows:
V(k)＝{q∈R 2 |d(q,k)≤d(q,l)},k,l＝1,2,…,N；k≠l
wherein V (k) is the service range of the charging station k, d (q, k) and d (q, l) are Euclidean distances from any charging demand point q to the charging station k and the charging station l on a Voronoi diagram, N is the number of stations built, R is the number of stations built 2 Is the set of all charge demand points within the area.
Optionally, the preset constraint condition includes:
constraint of total charging demand:
in the formula, T c For the average effective running time of the charging station, P ch Is the rated power of the charging machine,
the number of chargers configured in the charging station k is divided by the capacity of the charging stationObtained by the rated power of the charger, P demand Is the sum of the daily charge demands within the area;
charging station service area constraints and coverage constraints:
max(d qk )≤R k
R k ≤d k,k+1 ≤2R k
in the formula (d) qk Distance, R, from charging demand point q to charging station k k Is the service radius of the charging station k; d k,k+1 The distance between the charging station k and the charging station k + 1;
maximum power constraint:
in the formula, P c,k The power of the distribution network is connected to the charging station k, and the value of the power is the capacity P of the charging station k c,max And allowing the maximum power accessed by the charging station for the power distribution network.
Optionally, the mutation, hybridization and selection operations comprise:
carrying out variation operation on individual positions through a DE/rand/1 variation strategy, and then taking the individual speed of the variation vector and the individual speed of the iteration times t +1
Comprises the following steps:
in the formula, r 1 ,r 2 ,r 3 E {1,2, …, M } and r 1 ≠r 2 ≠r 3 ≠i，
The position of the target individual for which the mutation operation is performed for the number of iterations t,
random individual positions for the difference operation for the number of iterations t, scaling factor F ∈ [0,2 ∈ [ ]]；
Positioning the target individual by hybridization
And the variance vector
Performing cross operation to obtain new individual position
In the formula, rand j (0,1) is [0,1 ]]Random numbers obeying uniform distribution in intervals, and a cross factor CR E [0,1 ∈]；
Target individual position through pre-constructed fitness function
And new individual positions
Performing selection operation to obtain the operated individual position
Where f (-) is a fitness function.
Optionally, the fitness function is constructed with the lowest overall social cost:
in the formula (I), the compound is shown in the specification,
respectively the fixed facility construction cost and the later operation maintenance cost of the operator,
the annual loss cost generated during and before charging of the electric automobile is respectively reduced;
fixed facility construction costs of the operator
Comprises the following steps:
in the formula, r 0 、t year Respectively for the rate of chargeback and the projected life of the charging station,
p ch the number of chargers and unit price of the charging station k are respectively,
p tr the transformer capacity and the unit capacity cost of the charging station k are respectively, and the value of the transformer capacity is the charging station capacity; a. the k 、
Land area and unit price, C, for charging station k, respectively bk 、C lk The infrastructure cost and the line investment cost of a charging station k are saved; n is the number of station building;
the operator's post-operational maintenance costs
Comprises the following steps:
in the formula,. epsilon.b r Is a conversion coefficient;
annual loss cost of electric vehicle during charging
Comprises the following steps:
in the formula, T c For average effective running time of charging station, p c For charging electricity price, C W 、C L Respectively, the loss of the charger and the charging line, C Cu 、C Fe Copper loss and iron loss of the transformer respectively;
annual loss cost of the electric automobile before charging
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
respectively the vehicle annual electric quantity loss cost, the annual running time loss cost and the annual queuing waiting time loss cost of the electric vehicle;
annual electric quantity loss cost of electric vehicle
Comprises the following steps:
in the formula, Q per Is one hundred kilometersThe power consumption,
Number of electric vehicles having a charging demand at a charging demand point q, η qk For charging decision, η qk Where 1 denotes that charging is performed at the charging demand point q by selecting the charging station k, η qk When the charging demand point q is equal to 0, the charging station k is selected not to be charged, and d qk The distance from the charging demand point q to a charging station k; r is the number of charging demand points;
the annual driving time loss cost of the electric automobile
Comprises the following steps:
in the formula, v qk The traveling speed from the charging demand point q to the charging station k, c user The unit time cost is the cost of the electric automobile user;
annual queuing waiting time loss cost of electric automobile
Comprises the following steps:
in the formula, w k The queuing waiting time of the electric vehicle at the charging station k is realized.
Optionally, the electric vehicle adopts an M/G/c queuing model, and the arrival time compliance parameter of the electric vehicle is λ k The charging time follows a normal distribution with an expectation of mu and a variance of sigma, and the queuing waiting time w of the electric vehicle at a charging station k k Comprises the following steps:
ρ k ＝λ k μ
in the formula, ρ k The number of services for the charging station k,
optionally, the position and speed updating includes:
wherein, omega is the inertia weight,
k 1 、k 2 is [0,1 ]]The intervals are subject to uniformly distributed random numbers,
the optimal solution of the individual position of the iteration times t and the optimal solution of the individual position in the population.
Optionally, in the optimization process, if the optimal solution of the individual position does not change within the iteration number equal to the iteration stagnation number τ, the individual position is randomly varied:
wherein rand (0,1) is [0,1 ]]Random numbers, x, obeying a uniform distribution within the interval j,max 、x j,min The variable of the dimension j is upper and lower limits, j is 1, and 2 is the address and capacity of the charging station respectively.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an optimal layout method of an electric vehicle charging station, which considers the convenience of charging, construction feasibility and safety of a power grid and constructs layout constraint conditions of the charging station; the station building economy is considered, and a fitness function with the lowest total social cost is established from the two aspects of a charging station operator and an electric vehicle user; a DEPSO hybrid algorithm combining a DE algorithm and a PSO algorithm is adopted to solve the optimal charging station layout problem; the charging demand data in the accessible planning district to carrying out charging station optimization overall arrangement, accurate high-efficient, have stronger commonality and practicality, have the significance to guiding the charging service facility overall arrangement.
Drawings
Fig. 1 is a flowchart of an optimized layout method for an electric vehicle charging station according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a PSO algorithm and a DEPSO algorithm optimizing process according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a location charging station address and a service range provided by an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, an embodiment of the present invention provides an optimal layout method for an electric vehicle charging station, including the following steps:
1. the charging demand points of the electric automobile in the region and the daily charging demand quantity of each charging demand point are obtained.
2. Initializing basic parameters of a hybrid optimization algorithm DEPSO, wherein the basic parameters comprise a population size M and a maximum iteration number t max Iteration stagnation frequency tau, initial inertia weight omega start Terminating the inertial weight ω end Learning factor c 1 And c 2 Scaling factor F, crossover factor CR, maximum number of stations N st,max And the minimum number of station building N st,min ；
Wherein, the maximum number of building stations is N st,max And the minimum number of station building N st,min Comprises the following steps:
in which ceil (-) is an upward rounding function, P demand Is the sum of the daily charge demands in the area, S max And S min The maximum and minimum capacity of a single charging station.
3. Let the number of building stations N equal to N st,min Randomly extracting a charging station address from a charging demand point to generate an initial population, and determining the individual position and speed of the initial population, wherein the individual position comprises the charging station address and capacity;
determining the positions of the individuals of the initial population includes generating by using a uniformly distributed random function, the position of the individual i is at the value of the dimension j
Comprises the following steps:
wherein t is 0 and rand (0,1) is [0,1 ]]Random numbers, x, obeying a uniform distribution within the interval j,max 、x j,min The upper and lower limits of the variable of the dimension j are j 1, and 2 are the address and the capacity of the charging station respectively.
4. Setting the iteration times t to be 0, constructing a Voronoi diagram by taking the address of the charging station as the center, dividing the service range of the charging station in the region according to the Voronoi diagram, judging whether the service range meets the preset constraint condition or not, and resetting the step if the service range does not meet the preset constraint condition;
4.1, the service range of the charging station in the area is as follows:
V(k)＝{q∈R 2 |d(q,k)≤d(q,l)},k,l＝1,2,…,N；k≠l
wherein V (k) is the service range of the charging station k, d (q, k) and d (q, l) are Euclidean distances from any charging demand point q to the charging station k and the charging station l on a Voronoi diagram, N is the number of stations built, R is the number of stations built 2 Is the set of all charge demand points within the area.
4.2, the preset constraint conditions comprise:
constraint of total charging demand:
in the formula, T c For average effective running time of charging station, P ch Is the rated power of the charging machine,
the number of chargers configured in the charging station k is obtained by dividing the capacity of the charging station by the rated power of the charger, P demand Is the sum of the daily charge demands within the area;
charging station service area constraints and coverage constraints:
max(d qk )≤R k
R k ≤d k,k+1 ≤2R k
in the formula (d) qk Distance, R, from charging demand point q to charging station k k Is the service radius of the charging station k; d is a radical of k,k+1 The distance between the charging station k and the charging station k + 1;
maximum power constraint:
in the formula, P c,k The power of the distribution network is connected to the charging station k, and the value of the power is the capacity P of the charging station k c,max And allowing the charging station to access the maximum power for the power distribution network.
5. Carrying out optimization on individual positions of individuals of the initial population and recording corresponding fitness, wherein the optimization comprises the steps of carrying out variation, hybridization and selection operations on the individuals of the initial population, carrying out position and speed updating on the operated individuals, and carrying out variation, hybridization and selection operations on the updated individuals;
5.1, mutation, hybridization and selection operations include:
carrying out variation operation on individual positions through a DE/rand/1 variation strategy, and then taking the individual speed of the variation vector and the individual speed of the iteration times t +1
Comprises the following steps:
in the formula, r 1 ,r 2 ,r 3 E {1,2, …, M } and r 1 ≠r 2 ≠r 3 ≠i，
The position of the target individual for which the mutation operation is performed for the number of iterations t,
random individual positions for the difference operation for the number of iterations t, scaling factor F ∈ [0,2 ∈ [ ]]；
Positioning the target individuals by hybridization
And the variance vector
Performing cross operation to obtain new individual position
In the formula, rand j (0,1) is [0,1 ]]Random numbers obeying uniform distribution in intervals, and a cross factor CR E [0,1 ∈]；
Target individual position through pre-constructed fitness function
And new individual positions
Performing selection operation to obtain the position of the operated individual
Where f (-) is the fitness function.
Specifically, the method comprises the following steps: the fitness function is constructed with the lowest overall social cost:
in the formula (I), the compound is shown in the specification,
respectively fixed facility construction cost and later operation maintenance cost of an operator,
the annual loss cost generated during and before charging of the electric automobile is respectively reduced;
fixed facility construction cost of operator
Comprises the following steps:
in the formula, r 0 、t year Respectively for the charge station discount rate and the planned service life,
p ch the number of chargers and unit price of the charging station k are respectively,
p tr the transformer capacity and the unit capacity cost of the charging station k are respectively, and in an ideal state, the transformer capacity is represented by the charging station capacity; a. the k 、
Land area and unit price, C, for charging station k, respectively bk 、C lk The infrastructure cost and the line investment cost of a charging station k are saved; n is the number of building stations;
operator's post-operational maintenance costs
Comprises the following steps:
in the formula, epsilon br Is a conversion coefficient;
annual loss cost of electric vehicle during charging
Comprises the following steps:
in the formula, T c For average effective running time of charging station, p c For charging electricity price, C W 、C L Respectively, the loss of the charger and the charging line, C Cu 、C Fe Copper loss and iron loss of the transformer respectively;
annual loss cost of electric vehicle before charging
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
respectively the vehicle annual electric quantity loss cost, the annual running time loss cost and the annual queuing waiting time loss cost of the electric vehicle;
annual electric quantity loss cost of electric automobile
Comprises the following steps:
in the formula, Q per The power consumption is hundreds of kilometers,
Number of electric vehicles having a charging demand at a charging demand point q, η qk For charging decision, η qk Where 1 denotes that charging is performed at the charging demand point q by selecting the charging station k, η qk When the charging demand point q is equal to 0, the charging station k is selected not to be charged, and d qk The distance from the charging demand point q to a charging station k; r is the number of the charging demand points;
annual driving time loss cost of electric automobile
Comprises the following steps:
in the formula, v qk The traveling speed from the charging demand point q to the charging station k, c user The unit time cost is the cost of the electric automobile user;
electric automobile annual queuing waiting time loss cost
Comprises the following steps:
in the formula, w k The queuing waiting time of the electric vehicle at the charging station k is realized.
The electric automobile adopts an M/G/c queuing model, and the arrival time obeying parameter of the electric automobile is lambda k The charging time follows normal distribution with the expectation of mu and the variance of sigma, and the queuing waiting time w of the electric vehicle at the charging station k k Comprises the following steps:
ρ k ＝λ k μ
in the formula, ρ k The number of services to be charged to the station k,
cost per unit time of electric vehicle user c user The influencing factor of (2) is the labor income of residents in unit time, and the gamma distribution obeys the parameters alpha and beta:
in the formula, x c The average value is the monthly income of residents
Variance of
Cost per unit time
Unit: a meta.
5.2, position and speed updating comprises:
in the formula, ω is the inertial weight,
k 1 、k 2 is [0,1 ]]The intervals are subject to uniformly distributed random numbers,
the optimal solution of the individual position of the iteration times t and the optimal solution of the individual position in the population are obtained.
6. Judging whether the iteration number t is equal to the maximum iteration number t max If not, making the iteration time t equal to t +1, updating the initial population according to the optimized individual position of the iteration time t, and performing iteration;
7. if yes, judging whether the station building number N is equal to the maximum station building number N or not st,max If not, the station building number N is made to be N +1, and the maximum iteration time t is used max Updating the initial population at the optimized individual position and performing iteration;
8. if yes, the maximum iteration number t is determined max And outputting the optimized individual positions and the corresponding fitness as an optimized layout result.
In the optimization process, if the optimal solution of the individual position does not change within the iteration times equal to the iteration stagnation times tau, the individual position is randomly varied:
wherein rand (0,1) is [0,1 ]]Random numbers, x, obeying a uniform distribution within the interval j,max 、x j,min The upper and lower limits of the variable of the dimension j are j 1, and 2 are the address and the capacity of the charging station respectively.
Taking a place as an example, the basic parameter situation is shown in table 1:
TABLE 1 charging station parameter values
The charging station land belongs to the social public service project land, and the charging station land requisition price is 1170 yuan/m 2 selected by referring to the urban area standard land price.
The DEPSO algorithm parameters are set as follows: the population size is 20, the maximum iteration number is 300, the iteration stagnation number is 10, the scaling factor is 0.2, and the cross factor is 0.8. The self-learning factor and the social learning factor satisfy a linear strategy, the initial value and the final value of the self-learning factor are respectively 2.5 and 0.5, and the initial value and the final value of the social learning factor are respectively 0.5 and 2.5.
And the lowest social cost can be obtained by iteration when 10 charging stations are built. The PSO algorithm and the DEPSO algorithm are respectively adopted to solve the calculation examples, and a contrast curve of the solving process is shown in figure 2. The PSO algorithm has high convergence rate at the initial stage of iteration, the convergence performance is superior to that of the DEPSO algorithm, and the optimal fitness value obtained in the 69 th iteration is 2286.75 ten thousand yuan; the DEPSO algorithm still maintains the population diversity in the middle and later stages of iteration, the obtained optimal fitness value is 2201.36 ten thousand yuan, and the final optimization result is superior to the PSO algorithm. The position coordinates of the charging stations and the configuration number of the chargers in the optimal scheme obtained after iteration are shown in table 2, and the station building positions and the service ranges are shown in fig. 3.
TABLE 2 optimal configuration results for charging stations
The method considers the convenience of charging, construction feasibility and safety of the power grid, and constructs the layout constraint condition of the charging station; the station building economy is considered, and a fitness function with the lowest total social cost is established from the two aspects of a charging station operator and an electric vehicle user; and a DEPSO hybrid algorithm combining a DE algorithm and a PSO algorithm is adopted to solve the optimal charging station layout problem. The method substitutes the electric automobile holding capacity in the planning area and the charging demand data based on travel space-time distribution into the method, accurately and efficiently solves the location and volume fixing result of the charging station, and has important significance for guiding the layout of the charging service facilities.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. An electric vehicle charging station optimization layout method is characterized by comprising the following steps:
acquiring charging demand points of the electric automobile in an area and daily charging demand on each charging demand point;
initializing basic parameters of a hybrid optimization algorithm DEPSO, wherein the basic parameters comprise a population size M and a maximum iteration number t max Iteration stagnation times tau, initial inertia weight omega start Terminating the inertial weight ω end Learning factor c 1 And c 2 Scaling factor F, crossover factor CR, maximum number of stations N st，max And the minimum number of station building N st，min ；
Let the number of building stations N equal to N st，min Randomly extracting the charging station address from the charging demand point to generate an initial population,determining individual positions and speeds of the initial population, wherein the individual positions comprise charging station addresses and capacities;
setting the iteration times t to be 0, constructing a Voronoi diagram by taking the address of the charging station as the center, dividing the service range of the charging station in the region according to the Voronoi diagram, judging whether the service range meets the preset constraint condition or not, and resetting the step if the service range does not meet the preset constraint condition;
optimizing individual positions of individuals of the initial population and recording corresponding fitness, wherein the optimizing comprises performing variation, hybridization and selection operations on the individuals of the initial population, updating the positions and the speeds of the operated individuals, and performing variation, hybridization and selection operations on the updated individuals;
judging whether the iteration number t is equal to the maximum iteration number t max If not, enabling the iteration time t to be t +1, updating the initial population according to the optimized individual position of the iteration time t, and performing iteration;
if yes, judging whether the station building number N is equal to the maximum station building number N or not st，max If not, the station building number N is made to be N +1, and the maximum iteration time t is used max Updating the initial population at the optimized individual position and performing iteration;
if yes, the maximum iteration number t is determined max And outputting the optimized individual positions and the corresponding fitness as an optimized layout result.
2. The optimal layout method for electric vehicle charging stations according to claim 1, wherein the maximum number of stations built is N st，max And the minimum number of station building N st，min Comprises the following steps:
wherein ceil (. cndot.) is rounded upwardFunction, P demand Is the sum of the daily charge demands in the area, S max And S min Maximum and minimum for a single charging station capacity.
3. The optimal layout method for electric vehicle charging stations according to claim 1, wherein the determining the positions of the individuals in the initial population comprises generating the positions of the individuals i in the dimension j by using a uniformly distributed random function
Comprises the following steps:
wherein t is 0 and rand (0,1) is [0,1 ]]Random numbers, x, obeying a uniform distribution within the interval j，max 、x j，min The upper and lower limits of the variable of the dimension j are j 1, and 2 are the address and the capacity of the charging station respectively.
4. The optimal layout method for the electric vehicle charging stations as claimed in claim 1, wherein the service range of the charging stations in the area is as follows:
V(k)＝{q∈R 2 |d(q，k)≤d(q，l)}，k，l＝1，2，…，N；k≠l
wherein V (k) is the service range of the charging station k, d (q, k) and d (q, l) are Euclidean distances from any charging demand point q to the charging station k and the charging station l on a Voronoi diagram, N is the number of stations built, R is the number of stations built 2 Is the set of all charge demand points within the area.
5. The optimal layout method for electric vehicle charging stations according to claim 1, wherein the preset constraints comprise:
constraint of total charging demand:
in the formula, T c For average effective running time of charging station, P ch Is the rated power of the charging machine,
the number of chargers configured in the charging station k is obtained by dividing the capacity of the charging station by the rated power of the charger, P demand Is the sum of the daily charge demands within the area;
charging station service area constraints and coverage constraints:
max(d qk )≤R k
R k ≤d k，k+1 ≤2R k
in the formula (d) qk Distance, R, from charging demand point q to charging station k k Is the service radius of the charging station k; d k，k+1 The distance between the charging station k and the charging station k + 1;
maximum power constraint:
in the formula, P c，k The power of the power distribution network is connected to the charging station k, and the value of the power is the capacity P of the charging station k c，max And allowing the charging station to access the maximum power for the power distribution network.
6. The method of claim 1, wherein the operations of mutation, hybridization and selection comprise:
carrying out variation operation on individual positions through a DE/rand/1 variation strategy, and then taking the individual speed of the variation vector and the individual speed of the iteration times t +1
Comprises the following steps:
in the formula, r 1 ，r 2 ，r 3 E {1,2, …, M } and r 1 ≠r 2 ≠r 3 ≠i，
The position of the target individual for performing the mutation operation for the iteration number t,
random individual positions for the difference operation for the number of iterations t, scaling factor F ∈ [0,2 ∈ [ ]]；
Positioning the target individuals by hybridization
Sum variation vector
Performing cross operation to obtain new individual position
In the formula, rand j (0,1) is [0,1 ]]Random numbers obeying uniform distribution in intervals, and a cross factor CR E [0,1 ∈]；
Target individual position through pre-constructed fitness function
And new individual positions
Performing selection operation to obtain the position of the operated individual
Where f (-) is the fitness function.
7. The optimal layout method for the electric vehicle charging station as claimed in claim 6, wherein the fitness function is constructed with the lowest social cost:
in the formula (I), the compound is shown in the specification,
respectively the fixed facility construction cost and the later operation maintenance cost of the operator,
the annual loss cost generated during and before charging of the electric automobile is respectively reduced;
fixed facility construction costs of the operator
Comprises the following steps:
in the formula, r 0 、t year Respectively for the charge station discount rate and the planned service life,
p ch the number of chargers and unit price of the charging station k are respectively,
p tr the transformer capacity and the unit capacity cost of the charging station k are respectively, and the value of the transformer capacity is the charging station capacity; a. the k 、
Land acquisition area and unit price, C, of charging station k, respectively bk 、C lk The infrastructure cost and the line investment cost of a charging station k are saved; n is the number of station building;
the operator's post-operational maintenance costs
Comprises the following steps:
in the formula, epsilon br Is a conversion coefficient;
annual loss cost of electric vehicle during charging
Comprises the following steps:
in the formula, T c For average effective running time of charging station, p c For charging electricity price, C W 、C L Respectively, the loss of the charger and the charging line, C Cu 、C Fe Copper loss and iron loss of the transformer respectively;
annual loss cost of the electric automobile before charging
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
respectively the vehicle annual electric quantity loss cost, the annual running time loss cost and the annual queuing waiting time loss cost of the electric vehicle;
annual electric quantity loss cost of electric vehicle
Comprises the following steps:
in the formula, Q per The power consumption is hundreds of kilometers,
Number of electric vehicles having a charging demand at a charging demand point q, η qk For charging decision, η qk Where 1 denotes that charging is performed at the charging demand point q by selecting the charging station k, η qk When the charging demand point q is equal to 0, the charging station k is selected not to be charged, and d qk The distance from the charging demand point q to a charging station k; r is the number of charging demand points;
the annual driving time loss cost of the electric automobile
Comprises the following steps:
in the formula, v qk The traveling speed from the charging demand point q to the charging station k, c user The unit time cost is the cost of the electric automobile user;
annual queuing waiting time loss cost of electric automobile
Comprises the following steps:
in the formula, w k The queuing waiting time of the electric vehicle at the charging station k is realized.
8. The optimal layout method for the electric vehicle charging station as claimed in claim 7, wherein the electric vehicle adopts an M/G/c queuing model, and the time compliance parameter of the electric vehicle is λ k The charging time follows a normal distribution with an expectation of mu and a variance of sigma, and the queuing waiting time w of the electric vehicle at a charging station k k Comprises the following steps:
ρ k ＝λ k μ
in the formula, ρ k The number of services to be charged to the station k,
9. the optimal layout method for electric vehicle charging stations according to claim 1, wherein the position and speed updating comprises:
in the formula, ω is the inertial weight,
k 1 、k 2 is [0,1 ]]The intervals are subject to uniformly distributed random numbers,
the optimal solution of the individual position of the iteration times t and the optimal solution of the individual position in the population.
10. The optimal layout method for the electric vehicle charging station as claimed in claim 1, wherein in the optimization process, if the optimal solution of the individual position does not change within the iteration number equal to the iteration stagnation number τ, the individual position is randomly varied:
wherein rand (0,1) is [0,1 ]]Random numbers, x, obeying a uniform distribution within the interval j，max 、x j，min The upper and lower limits of the variable of the dimension j are j 1, and 2 are the address and the capacity of the charging station respectively.
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