CN115034501A - Electric vehicle charging station site selection and volume fixing method - Google Patents
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Abstract
The invention discloses a site selection and volume fixing method for an electric vehicle charging station, which comprises the following steps: preliminarily setting the number N of charging stations of the electric vehicle; constructing an urban traffic flow sample space for describing spatial distribution characteristics of the urban traffic flow sample space; identifying urban traffic flow spatial distribution characteristics based on urban traffic flow sample spatial clustering; carrying out capacity configuration on the electric automobile in a service planning area based on the total charging demand of the electric automobile in the station to be selected of each charging station; accessing the station to be selected of each charging station after the location and volume fixing to a power distribution network, calculating node voltage by using a forward-backward substitution algorithm, if the node voltage deviation does not meet the constraint condition, enabling N = N +1, and performing the location and volume fixing again until the constraint condition of the operation of the power distribution network is met, thereby determining a final scheme of the location and volume fixing of the public charging station; the public charging station obtained by the locating and sizing method can fully cover the area where the urban traffic flow is located, the capacity allocation is efficient to use, the service is as much as possible, and the influence on the operation of a power grid after the public charging station is connected to an urban power distribution network is small.
Description
Technical Field
The invention belongs to the technical field of layout planning of electric vehicle charging stations, relates to a method for locating and sizing electric vehicle charging stations, and particularly relates to a method for locating and sizing electric vehicle charging stations by considering traffic flow spatial distribution.
Background
Urban electric vehicle charging station layout planning should meet the increasing charging demand of electric vehicles, and the problems of location selection and volume fixing of charging stations are increasingly concerned by the field of power distribution network planning operation, so that the spatial distribution of traffic flow needs to be analyzed from the urban traffic network to determine the proper charging station position and capacity; however, there are many influence factors on the traffic flow, and the charging station accessing to the urban distribution network also affects the operation of the power grid, so a general method for locating and sizing the electric vehicle charging station with little influence on the operation of the power grid needs to be established, so that the public charging station is reasonably planned.
Most of the current researches on public charging station planning are regarded as a multi-objective planning problem, different planning models are constructed to solve, multiple objectives are achieved under the condition of limited resources, and the minimum cost is guaranteed; the current public charging station siting and sizing research aims to focus on the following three aspects: the method comprises the following steps of taking the economic benefit of a charging station operator as a target, taking the comprehensive influence of a public charging station on a power distribution network as a target, and taking the convenience degree of the charging service of an electric vehicle user as a target; based on the three types of target researchers, the method for locating and sizing the electric vehicle charging station is improved and optimized from multiple aspects, and planning and construction of urban public charging stations are guided better.
However, most of the existing public charging station site selection and volume fixing plans are to establish a planning model with the maximum benefit of charging station operators, power grid companies and electric vehicle users as targets, and meanwhile, the existing public charging station site selection and volume fixing plans are relatively few in terms of considering urban traffic networks, simple in terms of traffic flow analysis, free of considering the influence of the charging station access to the urban power distribution network on the operation of the power grid, and have great limitation in site selection and volume fixing of electric vehicle charging stations.
In order to solve the problems, it is necessary to develop a method for locating and sizing an electric vehicle charging station by considering the spatial distribution of traffic flow.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides an electric vehicle charging station location and volume selection method considering traffic flow spatial distribution, which can realize that public charging station services fully cover the region where urban traffic flow is located, so that electric vehicles which are used efficiently and serve as much as possible are subjected to volume allocation, and simultaneously, the influence of the charging station accessing an urban power distribution network on the operation of the power grid is considered, so that the problem of low voltage caused by the charging station accessing is avoided.
The purpose of the invention is realized as follows: a location and volume fixing method for an electric vehicle charging station comprises the following steps:
step 4, capacity configuration is carried out on the electric automobile in the service planning area of the station to be selected of each charging station based on the total charging requirement of the electric automobile in the service planning area;
and 5, accessing the sites to be selected of the charging stations after the location and volume fixing in the steps 3 and 4 into the power distribution network, calculating the node voltage by using a forward-backward substitution algorithm, if the node voltage deviation does not meet the constraint condition, the charging capacity is overlarge, the charging capacity configuration is reduced by increasing the number of the cluster clusters, enabling N = N +1, repeating the steps 3 and 4, and carrying out the location and volume fixing again until the operation constraint condition of the power distribution network is met, thereby determining the final scheme of the location and volume fixing of the public charging stations.
Preferably, the step 1 specifically comprises: firstly, collecting parameters of spatial distribution characteristics of traffic flow in a planning area, wherein the parameters comprise the number of electric automobiles in the planning area, the type of land used in the planning area, road condition maps, traffic flow, the vehicle-to-pile ratio of current urban policies and the number of proposed charging piles of a single charging station in the planning area; and then dividing the number of the electric vehicles by the vehicle pile ratio and then dividing the number of the planned charging piles of the single charging station to obtain the number N of the preliminarily set electric vehicle charging stations.
Preferably, the step 2 specifically comprises: construction of urban traffic flow sample space G = { G = 1 ，g 2 ，g 3 ，…，g i ，…，g n In which g is i =（x i ，y i ，c i ) Is the ith flow cell, x i 、y i Respectively representing the abscissa and ordinate of the geometric center of the ith flow cell, c i Reflecting the traffic flow characteristics of the ith flow unit and establishing a flow characteristic parameter c of the flow unit gi i The calculation formula of (a) is as follows:
in the formula, n i Is g i The number of surrounding intersection nodes; q. q.s ik Is the ith flow unit g i Typical average daily traffic flow of the k-th intersection nodes around; s ik Is g i Area of kth right land type; h is ik Is g i The parking lot base weight of the kth right-of-land type.
Preferably, the step 3 specifically comprises:
first, urban traffic flow sample space G = { G 1 ，g 2 ，g 3 ，…，g i ，…，g n Calculating the local density rho i The calculation formula is as follows:
the local density of each sample point is determined by adopting a Gaussian kernel function in the formula; in the formula, ρ i Reflecting flow unit g i The density of local traffic flow of; c. C j Is a flow unit g i Traffic flow characteristics of (a); d ij For selecting station unit g i And a charging unit g i Calculating the distance of the geometric center point by adopting an Euclidean distance; d c Selecting a value for the truncation distance so that the average number of neighbors of each sample point is about 1-2% of the total number; and g i Is less than d c The greater the number of sample points of (b), p i The greater the value of (a);
next, the sample pitch δ is calculated i The calculation formula is as follows:
when g is i At maximum local density, δ i Represents the sum of all points of G and G i The maximum distance of (d); else delta i Denotes that all local densities in G are greater than G i Sample point of (1) and g i A minimum distance of;
finally, an objective function is calculated
Selecting the first N samples as cluster heads, using the cluster heads as clustering centers, namely the station to be selected of the charging station, and obtaining the station to be selected of the charging station z j Geographic coordinates (x) i ，y i ）。
Preferably, the step 4 specifically comprises:
firstly, an area G served by a charging station to be determined is set zj ，G zj Indicating a charging station z j A set of all service areas of (a); preliminarily setting the number N of the electric vehicle charging stations according to the step 1, wherein if N =1, all sample points belong to a cluster; if N is present>1, according to local density ρ i Sequence from large to small for the remainderTraversing the remaining non-clustering center sample points, finding out the point which has the local density larger than the traversal point and the minimum distance with the current traversal point in the completed classified sample points, classifying the current traversal point and the sample point into the same class, repeating the steps until the classification work of all data points is completed, and determining the region G served by all charging station sites zj ；
Secondly, carrying out charging station area and station division on flow units contained in the urban sample space, wherein the position of each cluster head in a clustering result is a charging station to be selected, and all samples of each cluster form a charging station address set
，z j Denotes a charging station site, where (x) j ，y j ) Denotes z j Geographic coordinates of G zj Denotes z j A set of contained traffic units;
and finally, carrying out capacity configuration on the N charging stations to be selected determined by the objective function based on the total charging demand of the electric vehicles in the service planning area, wherein the calculation formula is as follows:
in the formula, S i Indicating a public charging station z i A is a charge simultaneous coefficient, N ev Representing the total number of electric cars in the urban traffic network,
traffic flow characteristic sum C representing urban traffic flow sample space zi Representing public charging stations z i σ is the average power consumption of the electric vehicle, L is the average daily mileage, and T is the average charging duration of the electric vehicle.
Preferably, in step 5, the power distribution network operation constraint conditions include system power flow and node voltage:
the calculation formula of the system power flow equation constraint is as follows:
in the formula, P i 、Q i Active and reactive power, V, for node i i Is the voltage amplitude of node i, V j Is the voltage amplitude of node j, θ ij Is the phase angle difference of the voltage phasors of the nodes i, j, G ij 、B ij Is the admittance of the branch between nodes i and j;
the calculation formula of the node voltage deviation constraint of the power distribution network is as follows:
in the formula, V j ' represents the voltage per unit value, V, of the jth distribution network node after the charging station is connected to the distribution network oj And expressing the rated voltage per unit value of the jth power distribution network node, wherein M is the number of the nodes of the power distribution network.
Due to the adoption of the technical scheme, the invention has the beneficial effects that: the urban traffic flow sample space is constructed based on the urban traffic flow distribution unit, and the method of clustering and optimizing the flow distribution unit is adopted to realize location selection and volume fixing, so that the charging service provided by the charging station to be selected can fully cover the area where the urban traffic flow is located, and the capacity configuration is carried out by the electric vehicles which are used efficiently and serve as much as possible; the invention further considers the influence of the access of the charging station to be selected to the urban power distribution network on the operation of the power grid of the charging station to avoid the problem of low voltage caused by the access of the charging station, and adopts the iterative optimization of the site selection and volume fixing of the charging station to be selected based on the operation constraint of the power distribution network, so that the optimized site selection and volume fixing result has the advantages of fully covering the area where the urban traffic flow is located, efficiently using the volume configuration, serving as many electric vehicles as possible, having small influence on the operation of the power grid and having stable operation of the urban power distribution network.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of the distribution of land types and road traffic flow units for planning areas according to the present invention.
Fig. 3 is a schematic diagram of a planning area power distribution network structure and a power distribution area road traffic structure according to the present invention after being overlapped.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Examples
As shown in fig. 1, the invention provides a method for locating and sizing a charging station of an electric vehicle, comprising the following steps:
step 4, capacity configuration is carried out on the electric automobile in the planning area served by the station to be selected of each charging station based on the total charging requirement of the electric automobile in the planning area;
and 5, accessing the sites to be selected of the charging stations after the location and volume fixing in the steps 3 and 4 into the power distribution network, calculating the node voltage by using a forward-backward substitution algorithm, if the node voltage deviation does not meet the constraint condition, the charging capacity is overlarge, the charging capacity configuration is reduced by increasing the number of the cluster clusters, enabling N = N +1, repeating the steps 3 and 4, and carrying out the location and volume fixing again until the operation constraint condition of the power distribution network is met, thereby determining the final scheme of the location and volume fixing of the public charging stations.
In this embodiment, first, the information acquisition conditions are as follows: as shown in fig. 2, 1200 electric vehicles are arranged in the planned area, the land types in the planned area include residential land, public greenland, public parking lots and the like, the number of flow units divided at a road intersection is 34, the number of public charging piles required to be built in the area is estimated by selecting a vehicle-to-pile ratio of 16:1, 1200/16=75 and 75, with reference to the construction scale of the current urban public charging station, 20 public charging piles are averagely arranged in the preset charging stations, 75/20=3.75, 3.75 public charging stations are determined to be required to be built in the area, the minimum number Nmin of the estimated charging stations is 3, and the maximum number Nmax is 4. The parking lot base weights of different land types are shown in the following table:
then, the attribute g of each traffic flow unit is constructed by utilizing the collected information i =（x i ，y i ，c i ) Wherein x is i 、y i Respectively representing the abscissa and ordinate of the geometric center of the ith flow cell, c i Reflecting the traffic flow characteristics of the ith flow unit and establishing a flow characteristic parameter c of the flow unit gi i The calculation formula of (c) is as follows:
in the formula, n i Is g i The number of surrounding intersection nodes; q. q.s ik Is the ith flow unit g i Typical average daily traffic flow of the k-th intersection nodes around; s ik Is g i Area of kth right land type; h is ik Is g i The kth right-of-land type of parking lot basis weight.
The calculation results are shown in the following table:
34 traffic flow units calculated in the table above are used to construct city traffic flow sample space G = { G = 1 ，g 2 ，g 3 ，…，g 34 And describing the traffic flow spatial distribution characteristics of the planning area.
Then, based on the city traffic flow sampleThis space G = { G = 1 ，g 2 ，g 3 ，…，g 34 Clustering identifies urban traffic flow spatial distribution characteristics, and the characteristic extraction basis of the clustering is as follows: cluster heads of clustered clusters are surrounded by clusterers having a lower local density and are at a relatively greater distance from points having a higher density.
In this embodiment, the number of flow units is 34, and thus the traffic flow sample space G = { G = therefor 1 ，g 2 ，g 3 ，…，g 34 Calculating the local density rho i The calculation formula is as follows:
the local density of each sample point is determined by adopting a Gaussian kernel function in the formula; in the formula, ρ i Reflection flow unit g i The density of local traffic flow of; c. C j Is a flow unit g i Traffic flow characteristics of (a); d ij For selecting station unit g i And a charging unit g i Calculating the distance of the geometric center point by adopting an Euclidean distance; d is a radical of c Selecting a value for the truncation distance so that the average number of neighbors of each sample point is about 1-2% of the total number; and g i Is less than d c The greater the number of sample points of (a), p i The larger the value of (c).
At the completion of the local density p i After calculation, the sample spacing δ is calculated i The calculation formula is as follows:
when g is i At maximum local density, δ i Represents the sum of all points of G and G i The maximum distance of (d); else δ i Denotes that all local densities in G are greater than G i Sample point of (1) and g i The minimum distance of (c).
At the completion of the sample spacing δ i After calculation, the objective function is calculated
Selecting the first 3 samples as cluster heads for the first time, using the cluster heads as clustering centers, namely, stations to be selected of the charging station, and obtaining the stations to be selected of the charging station z j Geographic coordinates (x) i ，y i ）。
Determined area G served by the charging station site zj ，G zj Indicating a charging station z j A set of all service areas of (a); preliminarily setting the number N of the electric vehicle charging stations according to the step 1, wherein if N =1, all sample points belong to a cluster; if N is present>1, according to local density ρ i Traversing the residual non-clustering center sample points in a descending order, finding out a point which has the local density larger than the traversal point and the minimum distance from the current traversal point in the classified sample points, classifying the current traversal point and the sample points into the same class, repeating the steps until the classification work of all the data points is completed, and determining the region G served by all the charging station sites zj 。
Continue to choose N =3>1, according to local density ρ i Traversing the residual non-clustering center sample points in a descending order to obtain the station areas of the charging station g33, g12 and g 7.
The calculation results of the 3 cluster heads, the service areas of the 3 charging stations to be selected and related data are shown in the following table:
then, carrying out charging station area and station division on flow units contained in the city sample space, wherein the position of each cluster head in a clustering result is a charging station to be selected, and all samples of each cluster form a charging station address set
，z j Denotes a charging station site, where (x) j ，y j ) Denotes z j Geographic coordinates of G zj Denotes z j The set of contained traffic units.
In this embodiment, as shown in fig. 3, a thin solid line in the drawing is a road traffic structure diagram of a planning area, a thick dotted line in the drawing is a power distribution network structure diagram, and after a node of a crossing of the planning area is overlapped with a node of a power distribution network, a node number of the power distribution network is a node number of a charging station accessing a power grid, so as to perform capacity configuration calculation of a charging station to be selected.
Carrying out capacity configuration on 3 charging stations to be selected determined by the objective function based on the total charging demand of the electric vehicles in the service planning area, wherein the calculation formula is as follows:
in the formula, S i Indicating a public charging station z i A is a charge simultaneous coefficient, N ev Representing the total number of electric cars in the urban traffic network,
traffic flow characteristic sum, C, representing sample space of urban traffic flow zi Indicating a public charging station z i σ is the average power consumption of the electric vehicle, L is the average daily mileage, and T is the average charging duration of the electric vehicle.
The capacity allocation calculation results of the 3 charging stations to be selected are shown in the following table:
then, 3 charging stations to be selected are accessed to the urban power distribution network, and the charging load is accessed to the power distribution network, which may cause the node voltage to be out of limit, so that the operation constraint conditions of the power distribution network for locating and sizing the charging stations include the system load flow and the node voltage:
the calculation formula of the system power flow equation constraint is as follows:
in the formula, P i 、Q i Active and reactive power, V, for node i i Is the voltage amplitude of node i, V j Is the voltage amplitude of node j, θ ij Is the phase angle difference of the voltage phasors of the nodes i, j, G ij 、B ij Is the admittance of the branch between nodes i and j;
the calculation formula of the node voltage deviation constraint of the power distribution network is as follows:
in the formula, V j ' represents the voltage per unit value, V, of the jth power distribution network node after the charging station is connected to the power distribution network oj And expressing the rated voltage per unit value of the jth power distribution network node, wherein M is the number of the nodes of the power distribution network.
The voltage amplitude values of the front node and the rear node of the power distribution network accessed by the 3 charging stations to be selected are shown in the following table:
as can be seen from the above table, the voltage shifts of the node numbers 14, 15, 16, 17, and 18 do not satisfy the constraint condition and are rejected nodes.
Then, let N =3+1=4, and perform the fixed-site sizing again.
Then, the address is reselected, and N =4 is selected>1, according to local density ρ i Traversing the residual non-clustering center sample points in a descending order to obtain the station areas of the charging station, namely g33, g12, g7 and g 15.
The calculation results of the 4 cluster heads, the service areas of the 4 charging stations to be selected and related data are shown in the following table:
and then capacity is re-determined, and the capacity configuration calculation results of the 4 charging stations to be selected are shown in the following table:
and then, re-operating the constraint conditions, wherein the voltage amplitude values of the front node and the rear node of the 4 charging stations to be selected, which are accessed to the power distribution network, are shown in the following table:
as can be seen from the above table, the optimized voltage offsets of all the nodes all satisfy the constraint conditions and all satisfy the constraint conditions for operation of the power distribution network, so that the result of location determination and volume determination after N =4 is determined as the final scheme of location determination and volume determination of the public charging station.
When N =3 is found, the node voltage shifts of the failed nodes 14, 15, 16, 17, and 18 are compared after being reconfigured to N =4, as shown in the following table:
in summary, the electric vehicle charging station site selection and volume fixing method provided by the invention comprehensively considers urban traffic flow spatial distribution characteristics and power distribution network operation constraints, and the public charging station site selection and volume fixing method based on traffic flow spatial distribution is provided; further considering the influence of the urban power distribution network accessed by the charging station on the operation of the power grid of the charging station, and performing iterative optimization on the site selection and the volume determination of the charging station based on the operation constraint of the power distribution network; the method realizes that public charging station service covers the area where the urban traffic flow is located, so as to realize the capacity allocation of the electric vehicles which are efficiently used and serve as much as possible; the influence of the charging station accessing the urban distribution network on the operation of the power grid is further considered, and the problem of low voltage caused by the charging station accessing is avoided.
Finally, it should be noted that the above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same, and although the present invention is described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the specific embodiments of the present invention without departing from the spirit and scope of the present invention, and all the modifications or equivalent substitutions should be covered in the claims of the present invention.
Claims (6)
1. A location and volume selecting method for an electric vehicle charging station is characterized by comprising the following steps:
step 1, preliminarily setting the number N of charging stations of the electric vehicle;
step 2, constructing an urban traffic flow sample space for describing spatial distribution characteristics of the urban traffic flow sample space;
step 3, identifying urban traffic flow spatial distribution characteristics based on urban traffic flow sample spatial clustering, wherein the characteristic extraction basis of the clustering is as follows: clustering cluster heads of the cluster clusters are surrounded by the cluster members with lower local density, and have relatively larger distance with the points with higher density, so as to obtain the station to be selected and the geographic coordinates of the charging station;
step 4, capacity configuration is carried out on the electric automobile in the service planning area of the station to be selected of each charging station based on the total charging requirement of the electric automobile in the service planning area;
and 5, accessing the sites to be selected of the charging stations after the location and volume fixing in the steps 3 and 4 into the power distribution network, calculating the node voltage by using a forward-backward substitution algorithm, if the node voltage deviation does not meet the constraint condition, the charging capacity is overlarge, the charging capacity configuration is reduced by increasing the number of the cluster clusters, enabling N = N +1, repeating the steps 3 and 4, and carrying out the location and volume fixing again until the operation constraint condition of the power distribution network is met, thereby determining the final scheme of the location and volume fixing of the public charging stations.
2. The electric vehicle charging station site selection and sizing method according to claim 1, wherein the step 1 specifically comprises: firstly, collecting parameters of spatial distribution characteristics of traffic flow in a planning area, wherein the parameters comprise the number of electric automobiles in the planning area, the type of land used in the planning area, road condition maps, traffic flow, the vehicle-to-pile ratio of current urban policies and the number of proposed charging piles of a single charging station in the planning area; and then dividing the number of the electric vehicles by the vehicle pile ratio and then dividing the number of the planned charging piles of the single charging station to obtain the number N of the preliminarily set electric vehicle charging stations.
3. The electric vehicle charging station site selection and sizing method according to claim 2, wherein the step 2 specifically comprises: construction of urban traffic flow sample space G = { G 1 ，g 2 ，g 3 ，…，g i ，…，g n In which g is i =（x i ，y i ，c i ) Is the ith flow cell, x i 、y i Respectively representing the abscissa and ordinate of the geometric center of the ith flow cell, c i Reflecting the traffic flow characteristics of the ith flow unit and establishing a flow characteristic parameter c of the flow unit gi i The calculation formula of (a) is as follows:
in the formula, n i Is g i The number of surrounding intersection nodes; q. q.s ik Is the ith flow unit g i Typical average daily traffic flow of the k-th intersection nodes around; s ik Is g i Area of kth right land type; h is a total of ik Is g i The parking lot base weight of the kth right-of-land type.
4. The electric vehicle charging station site selection and sizing method according to claim 3, wherein the step 3 specifically comprises:
first, urban traffic flow sample space G = { G 1 ，g 2 ，g 3 ，…，g i ，…，g n Calculating the local density rho i The calculation formula is as follows:
the local density of each sample point is determined by adopting a Gaussian kernel function in the formula; in the formula, ρ i Reflecting flow unit g i The density of local traffic flow of; c. C j Is a flow unit g i Traffic flow characteristics of (a); d ij For selecting station unit g i And a charging unit g i Calculating the distance of the geometric center point by adopting an Euclidean distance; d c Selecting a value for the truncation distance so that the average number of neighbors of each sample point is about 1-2% of the total number; and g i Is less than d c The greater the number of sample points of (a), p i The greater the value of (A);
next, the sample pitch δ is calculated i The calculation formula is as follows:
when g is i At maximum local density, δ i Indicates that G is in all points with G i The maximum distance of (d); else delta i Denotes that all local densities in G are greater than G i Sample point of (1) and g i The minimum distance of (a);
finally, an objective function is calculated
Selecting the first N samples as cluster heads, using the cluster heads as a clustering center, namely a station to be selected of the charging station, and simultaneously obtaining the station to be selected z of the charging station j Geographic coordinates (x) i ，y i ）。
5. The electric vehicle charging station site selection and sizing method according to claim 4, wherein the step 4 specifically comprises:
firstly, an area G served by a charging station to be determined is set zj ，G zj Indicating a charging station z j All services ofA set of regions; preliminarily setting the number N of the electric vehicle charging stations according to the step 1, wherein if N =1, all sample points belong to a cluster; if N is present>1, according to local density ρ i Traversing the residual non-clustering center sample points in a descending order, finding out a point which has the local density larger than the traversal point and the minimum distance from the current traversal point in the classified sample points, classifying the current traversal point and the sample points into the same class, repeating the steps until the classification work of all the data points is completed, and determining the region G served by all the charging station sites zj ；
Secondly, carrying out charging station area and station division on flow units contained in the urban sample space, wherein the position of each cluster head in a clustering result is a charging station to be selected, and all samples of each cluster form a charging station address set
，z j Represents a charging station site, where (x) j ，y j ) Denotes z j Geographic coordinates of G zj Denotes z j A set of contained traffic units;
and finally, carrying out capacity configuration on the N charging stations to be selected determined by the objective function based on the total charging demand of the electric vehicles in the service planning area, wherein the calculation formula is as follows:
in the formula, S i Indicating a public charging station z i A is a charge simultaneous coefficient, N ev Representing the total number of electric cars in the urban traffic network,
traffic flow characteristic sum C representing urban traffic flow sample space zi Indicating a public charging station z i σ is the average power consumption of the electric vehicle, L is the average daily mileage, and T is the average charging duration of the electric vehicle。
6. The electric vehicle charging station site selection and sizing method as claimed in claim 5, wherein in the step 5, the operation constraint conditions of the power distribution network comprise system power flow and node voltage:
the calculation formula of the system power flow equation constraint is as follows:
in the formula, P i 、Q i Active and reactive power, V, for node i i Is the voltage amplitude, V, of node i j Is the voltage amplitude of node j, θ ij Is the phase angle difference of the voltage phasors of the nodes i, j, G ij 、B ij Is the admittance of the branch between nodes i and j;
the calculation formula of the voltage deviation constraint of the power distribution network node is as follows:
in the formula, V j ' represents the voltage per unit value, V, of the jth distribution network node after the charging station is connected to the distribution network oj And expressing the rated voltage per unit value of the jth power distribution network node, wherein M is the number of the nodes of the power distribution network.
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| CN117408498A (en) * | 2023-12-15 | 2024-01-16 | 陕西德创数字工业智能科技有限公司 | Public charging station locating, sizing and piling method based on new energy big data |
| CN117669971A (en) * | 2023-12-11 | 2024-03-08 | 重庆交通大学 | Data-driven electric bus charging station address selection method |
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| CN117669971A (en) * | 2023-12-11 | 2024-03-08 | 重庆交通大学 | Data-driven electric bus charging station address selection method |
| CN117408498A (en) * | 2023-12-15 | 2024-01-16 | 陕西德创数字工业智能科技有限公司 | Public charging station locating, sizing and piling method based on new energy big data |
| CN117408498B (en) * | 2023-12-15 | 2024-02-23 | 陕西德创数字工业智能科技有限公司 | Public charging station locating, sizing and piling method based on new energy big data |
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