CN109872070B - Static charging pile deployment method based on cluster division - Google Patents

Static charging pile deployment method based on cluster division Download PDF

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CN109872070B
CN109872070B CN201910132418.0A CN201910132418A CN109872070B CN 109872070 B CN109872070 B CN 109872070B CN 201910132418 A CN201910132418 A CN 201910132418A CN 109872070 B CN109872070 B CN 109872070B
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钟萍
徐爱昆
奎晓燕
张艺雯
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Central South University
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Abstract

The invention discloses a static charging pile deployment method based on cluster division, which comprises the steps of establishing a network model aiming at SEB and SCP; dividing the SEB distribution maps into a relatively dense area and a relatively sparse area, and determining the intersection area of the relatively dense areas in the SEB distribution maps as an SCP charging range; constructing a minimum SCP quantity optimization function which can cover all SEBs in all target areas; solving the problem of an optimization function to obtain a preliminary deployment position of an SCP; and optimizing the initial deployment position of the SCP to obtain the final deployment position of the SCP. According to the method, the deployment position of the static charging pile is determined through the processes of constructing the optimization model, solving the optimization model and optimizing the optimization model, so that the method can be better suitable for the SEB distribution condition with dynamic change, the SCP deployment quantity can be minimized as much as possible, the reliability of the method is high, and the SCP deployment effect is good.

Description

Static charging pile deployment method based on cluster division
Technical Field
The invention particularly relates to a static charging pile deployment method based on cluster division.
Background
With the development of economic technology, the Shared economy is rapidly developed in an emerging economic model, and has also widely penetrated a plurality of fields of people's lives, wherein a series of Shared travel tools such as a Shared Electric Bicycle (SEB), a Shared automobile and the like are derived from a Shared Bicycle. Compared with the shared bicycle, the energy problem is a main influence factor for limiting the development of the SEB, so that the solution of the energy problem of the SEB is an important link for promoting the further development of the SEB.
At present, main charging modes of SEB brands mainly existing in the market are divided into three types, namely a) a mobile charging vehicle is adopted for charging the SEB, but the SEB charging vehicle has the problems of traffic jam, overhigh cost of the mobile charging vehicle and the like, and few operating companies adopting the mode are available; b) The manual energy supplement is carried out by manually carrying the storage battery, so that the method has low efficiency, needs a large amount of manpower and material resource investment, and is not beneficial to the long-term development of operation companies; c) Through deploying Static Charging Pile (SCP), the electric quantity is supplemented through returning the SCP at a fixed point. At present, the mode of using SCP to supplement electric quantity is common in the market, but the current SCP deployment is often determined according to the initial release condition of the SEB, the SEB has the characteristic of high liquidity, the SCP deployment is not suitable only according to the initial release condition, meanwhile, the SCP has higher land occupancy rate and higher cost, which leads to higher early investment cost of an operation company, so that an optimized deployment method for deploying the minimum number of SCPs to adapt to the dynamic change of the SEB is found, and becomes an urgent task.
Disclosure of Invention
The invention aims to provide a static charging pile deployment method based on cluster division, which has high reliability and good effect.
The invention provides a static charging pile deployment method based on cluster division, which comprises the following steps:
s1, establishing a network model aiming at a shared electric bicycle SEB and a static charging pile SCP;
s2, aiming at the network model established in the step S1, dividing a plurality of SEB distribution graphs into a relatively dense area and a relatively sparse area, and determining that an intersection area of the relatively dense areas in the SEB distribution graphs is an SCP charging range, wherein the relatively dense area is defined as an area containing the most SEBs, for example, the SEB distribution graphs are divided into 2 parts, an area A contains 65 SEBs, an area B contains 34 SEBs, and the area A contains more SEBs relative to the area B, the area A is the relatively dense area, and correspondingly, the area B is the relatively sparse area, namely the area containing less SEBs;
s3, constructing a minimum SCP quantity optimization function which can cover all SEBs in all target areas;
s4, solving the problem of the optimization function constructed in the step S3 based on the group division problem to obtain the initial deployment position of the SCP;
and S5, optimizing the initial deployment position of the SCP obtained in the step S4 so as to obtain the final deployment position of the SCP.
In step S2, the SEB distribution maps are divided into relatively dense areas and relatively sparse areas, specifically, the SEB distribution maps are divided into relatively dense areas and relatively sparse areas by using a k-means algorithm.
Step S2, determining an intersection area of the relatively dense areas in the SEB distribution maps as an SCP charging range, specifically determining the SCP charging range by using the following rules:
r1. If the area is relatively dense b 1 ,b 2 ,...,b e If no unique intersection exists, calculating the average value of the SEB number in all the intersections, and regarding the intersection area containing the SEB number higher than the average value as the SCP charging range P important ；
R2. If the area is relatively dense b 1 ,b 2 ,...,b e If only intersection exists, the intersection area is regarded as the SCP charging range P important ；
R3. If the relatively dense region b 1 ,b 2 ,...,b e If two regions are not intersected with each other, a relatively dense region b is calculated 1 ,b 2 ,...,b e Average value of the number of the inner SEB, and regarding a relatively dense area containing the SEB with the number not less than the average value as an SCP charging range P important 。
The construction in step S3 satisfies the minimum SCP number optimization function capable of covering all SEBs in all target areas, and specifically, the construction is performed by the steps of:
A. calculating a to-be-selected deployment area D of the SCP by adopting the following formula:
D＝D ∩ ∪D al
in the formula D ∩ ＝{D ∩1 ,D ∩2 ,...,D ∪s Represents SCP charging range P important Intersection of the belonged ranges of all SEB in the region;
Figure GDA0003985729670000031
represents the range of all SEBs that do not intersect any of the remaining SEBs;
B. calculating a deployment area D to be selected by adopting the following formula j SEB set G within j ：
Figure GDA0003985729670000032
In the formula, the | N | is the number of SEB; d i Is the belonged range of the ith SEB;
C. the following model was used as the minimum SCP number optimization function:
Figure GDA0003985729670000033
constraint conditions are as follows:
Figure GDA0003985729670000034
where i is within {1, 2., | G | }, x j ∈{0,1}，c ij ∈{0,1}，
Figure GDA0003985729670000035
D is the number of the deployment areas to be selected; />
Figure GDA0003985729670000041
And S4, solving the problem of the optimization function constructed in the step S3 based on the group partition problem, specifically solving the problem of the group partition by adopting a heuristic algorithm, thereby solving the problem of the optimization function constructed in the step S3.
Step S5, optimizing the preliminary deployment location of the SCP obtained in step S4, specifically, optimizing by using the following rule:
if the SEB in the same cluster has a unique public area, the geometric center position of the public area is the deployment position of the SCP;
and r2, if the SEB in the same group does not have the unique common area, sequentially isolating the area to which any SEB belongs in the group, and judging whether the rest SEBs have the unique common area: if the unique common area exists, taking the geometric center position of the unique common area and the geometric center position of the isolated SEB area as the deployment position of the SCP; and if no unique public area is found in the area to which any SEB belongs by sequentially isolating the cluster, deploying the SCP at the geometric center position of the generated intersection area.
r3, if the range to which 3 SEBs belong forms 3 common areas in the same cluster, and the 3 common areas are compared with 1 point, the common point is taken as the deployment position of the SCP.
According to the static charging pile deployment method based on cluster division, the deployment position of the static charging pile is determined through the processes of constructing the optimization model, solving the optimization model and optimizing, so that the method can be better suitable for the dynamically changed SEB distribution condition, the SCP deployment quantity can be minimized as much as possible, the reliability of the method is high, and the SCP deployment effect is good.
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FIG. 1 is a process flow diagram of the process of the present invention.
FIG. 2 is a schematic diagram of a network model of the method of the present invention.
FIG. 3 is a schematic diagram of an SEB undirected graph constructed by the method of the present invention.
Fig. 4 is a schematic diagram illustrating an application of a first rule for optimizing a preliminary deployment location of an SCP in the method of the present invention.
Fig. 5 is a schematic diagram illustrating an application of a second rule for optimizing a preliminary deployment location of an SCP in the method of the present invention.
Fig. 6 is a schematic diagram illustrating an application of a third rule for optimizing a preliminary deployment location of an SCP according to the method of the present invention.
Fig. 7 is a schematic diagram illustrating an effect of a range of SEBs on a required number of SCPs according to an embodiment of the method of the present invention.
Fig. 8 is a P-SEB distribution situation diagram provided by an embodiment of the method of the present invention important And the number of SEB in the area and the number of needed SCP are shown in the diagram.
Detailed Description
FIG. 1 shows a method flowchart of the method of the present invention: the invention provides a static charging pile deployment method based on cluster division, which comprises the following steps:
s1, establishing a network model aiming at a shared electric bicycle SEB and a static charging pile SCP;
according to the invention, the SEB is taken as a sensor node in a wireless chargeable sensor network, and the SCP is taken as a static energy source; assuming that a plurality of SCPs with the same specification are deployed in a two-dimensional area, and not considering the existence of obstacles; the SCP has a charging coverage of radius r, which means that SEBs present in the SCP charging coverage will be charged by the SCP (as shown in fig. 2);
assuming N SEBs (N is more than 1) with the same specification, N is used i Representing each SEB, the SEB set is N = { N = 1 ,N 2 ,...,N N }; regardless of the presence of obstacles, a one-to-one charging scheme is used, i.e. one SEB can only be charged by one SCP, and therefore each SEB has its own charging range d i ；
S2, aiming at the network model established in the step S1, dividing the SEB distribution maps into relatively dense areas and relatively sparse areas, and determining intersection areas of the relatively dense areas in the SEB distribution maps as SCP charging ranges; the relatively dense area is defined as an area containing the most SEBs, for example, the SEB distribution map is divided into 2 parts, the area a contains 65 SEBs, the area B contains 34 SEBs, and the area a contains more SEBs than the area B, so the area a is a relatively dense area, and correspondingly, the area B is a relatively sparse area, namely, an area containing less SEBs;
in specific implementation, a k-means algorithm is adopted to divide a plurality of SEB distribution graphs into a relatively dense area and a relatively sparse area;
for a k SEB distribution situation graph, dividing the k SEB distribution situation graph into theta regions by adopting a k-means algorithm, and dividing the k SEB distribution situation graph into relatively dense regions R = { b = according to the number of SEBs 1 ,b 2 ,...,b e And a relatively sparse region B = { c = } 1 ,c 2 ,...,c l And expressed by N = R ═ B, the relatively dense region and the relatively sparse region contain all SEBs within the two-dimensional region;
in a relatively dense region R, respectively finding k SEB distribution situation graphs through P important ＝b 1 ∩b 2 ∩...∩b e Find the intersection of these e relatively dense regions as the keyRegion P important The critical area P important I.e., indicating that there is a significant amount of SEB distributed over the area;
meanwhile, the SCP charging range is determined by the following rules:
r1. If the area is relatively dense b 1 ,b 2 ,...,b e If no unique intersection exists, calculating the average value of the SEB numbers in all intersections, and regarding the intersection area containing the SEB numbers higher than the average value as the SCP charging range P important ；
R2. If the area is relatively dense b 1 ,b 2 ,...,b e If there is a unique intersection, the intersection area is regarded as the SCP charging range P important ；
R3. If the relatively dense region b 1 ,b 2 ,...,b e Two by two are not intersected with each other, then a relatively dense area b is calculated 1 ,b 2 ,...,b e Average value of the number of the inner SEB, and regarding a relatively dense area containing the SEB with the number not less than the average value as an SCP charging range P important
S3, constructing a minimum SCP quantity optimization function capable of covering all SEBs in all target areas; specifically, the method comprises the following steps:
A. calculating a to-be-selected deployment area D of the SCP by adopting the following formula:
D＝D ∩ ∪D al
in the formula D ∩ ＝{D ∩1 ,D ∩2 ,...,D ∩s Denotes SCP charging range P important Intersecting the belonged ranges of all SEB in the region;
Figure GDA0003985729670000071
represents the range of all SEBs that do not intersect any of the remaining SEBs;
B. calculating the deployment area D to be selected by adopting the following formula j SEB set G within j ：
Figure GDA0003985729670000072
In the formula, the | N | is the number of SEB; d i Is the belonged range of the ith SEB;
C. the following model was used as the minimum SCP number optimization function:
Figure GDA0003985729670000073
constraint conditions are as follows:
Figure GDA0003985729670000074
where i belongs to {1,2,. And G | }, x j ∈{0,1}，c ij ∈{0,1}，
Figure GDA0003985729670000075
D is the number of the deployment areas to be selected; />
Figure GDA0003985729670000076
S4, solving the problem of cluster division by adopting a heuristic algorithm, solving the problem of the optimization function constructed in the step S3, and obtaining the initial deployment position of the SCP;
the problem of optimizing the minimum SCP quantity is an NP-HARD problem, the problem is similar to a group division problem with low complexity, the group division problem is to find the minimum group quantity capable of dividing a graph, each group is a certain vertex set, and any two vertexes in one group are connected through edges;
to convert the original problem into a clique solution, the present invention first aims at the final P important All SEB's within a region construct an undirected graph G (V, E) (as shown in FIG. 3), where each vertex represents the final P important Each SEB of the region, if the belonged ranges of the two SEBs are intersected, connecting an edge between the two points; with clique partitioning, the original problem can be converted into a less complex final P important Finding the minimum cluster number capable of dividing the undirected graph on the undirected graph formed by the SEB of the region;
there are many heuristic algorithms to solve the clustering problem, and the present invention uses the algorithm proposed by Tseng C J et al, "Tseng C J, siewiorek D P. Automated Synthesis of Data Paths in Digital Systems. IEEE Transactions on Computer-air designed Design of Integrated Circuits and Systems,1986,5 (3): 379-395", to solve the problem, the core concept of which is as follows:
(1) On undirected graph G (V, E), the node pairs (w) with the most common neighbors are chosen 1 ,w 2 ) If the graph has a plurality of node pairs with the same maximum number of public neighbors, selecting the node pair with the maximum degree, and if the degrees of the node pairs are also the same, randomly selecting any node pair;
(2) The selected node pair (w) 1 ,w 2 ) Are merged into one node w 1 ；
(3) Deletion is required for the following class 3 edges:
1)w 1 and w 2 The edge therebetween;
2)w 1 、w 2 the index number of the edge connected with the public neighbor is smaller;
3) And node pair (w) 1 ,w 2 ) An independent vertex connected with one node;
(4) Repeating the first three steps on the new undirected graph until there are no edges in the graph;
s5, optimizing the initial deployment position of the SCP obtained in the step S4 to obtain the final deployment position of the SCP; specifically, the following rules are adopted for optimization:
if the SEB in the same cluster has a unique public area, the geometric center position of the public area is the deployment position of the SCP; as shown in particular in fig. 4;
and r2, if the SEB in the same group does not have the unique common area, sequentially isolating the area to which any SEB belongs in the group, and judging whether the rest SEBs have the unique common area: if the unique common area exists, taking the geometric center position of the unique common area and the geometric center position of the isolated SEB area as the deployment position of the SCP; if no unique public area is found in the area to which any SEB belongs by sequentially isolating the cluster, deploying SCP at the geometric center of the generated intersection area;
as shown in fig. 5 in particular, the ranges of four SEBs a, b, c, d intersect each other two by two, a, b, c have a unique public area, and SEB d does not intersect the public area, in which case 3 SEBs are needed if the SCPs are still deployed in the public area, but the present invention considers that the SCPs are deployed in the public areas α and γ, and the SCP has a greater effect on SEB d, which is not necessary for SEBs a, b, c, and therefore, for this case, the present invention considers that one SEB is isolated from the cluster, and then the remaining SEBs are observed whether there is a unique public intersection, and if so, the SCP is deployed at the geometric center of the public intersection and at the center of the isolated SEB's range; as shown in fig. 5, an SEB d is isolated, and if it is found that a unique common area β exists in the remaining a, b, and c, SCPs are deployed at a geometric center position of β and a center position of a range to which the SEB d belongs, so that 3 SCPs originally required can be simplified into 2 SCPs; if the unique public area is not found after isolating one SEB, continuing to select another SEB for isolation processing to find the unique public area again; if no unique public area is found in the area to which any SEB belongs by sequentially isolating the cluster, deploying SCP at the geometric center of the generated intersection area;
r3, if 3 public areas are formed in the range to which the 3 SEBs belong in the same cluster, and the 3 public areas are compared with 1 point, taking the public point as the deployment position of the SCP;
specifically, as shown in fig. 6, the belonging range of 3 SEBs forms 3 common areas, and the 3 common areas intersect at 1 point, if the previous method is continued, 2 SCPs are deployed, but for such a special case, the SCPs only need to be deployed at the intersection point Q of the 3 common areas, and 1 SCP is used to replace the original 2 SCPs, thereby reducing the number of needed SCPs.
The method for optimizing and deploying the number of the minimized static charging piles based on the cluster division is used for dividing the number of clusters, and the number of SCPs required after optimization is used for experimental verification.
The number of SCP required is influenced by the range of SEB (see the attached figure 7):
in particular, the present invention contemplates SEB's falling within the scope
Figure GDA0003985729670000101
To>
Figure GDA0003985729670000102
And changing, with the increase of the range of the SEB, the number of the clusters obtained through cluster division is gradually reduced, and the required number of the SCPs obtained through optimization is also gradually reduced, which shows that the range of the SEB has a large influence on the number of the finally required SCPs. Meanwhile, the SEB can be divided into a plurality of groups with strong internal relevance by simply utilizing a group division algorithm, the number of the needed SCPs cannot be determined, and the specific deployment position and the number of the SCPs need to be found out after optimization.
P under different SEB distribution situation chart important The number of SEB and the number of required SCP in the region (see the attached figure 8):
specifically, the invention proposes that P found by finding a relatively dense area of different SEB distribution diagrams as an area charged by SCP, obviously, by finding 10 SEB distribution diagrams important The SEB number covered by the areas in different distribution condition graphs is more uniform and the fluctuation is smaller, which shows that the P determined by the invention is important The area can always cover more SEBs with higher density, and further shows that the utilization rate of SCP can be improved as much as possible by charging the SEBs in an SCP mode in the area. On the other hand, by observing the required SCP number under different SEB distribution charts, the required SCP number is found to vary from 53 to 65, and the difference is small, which means that when the final P is found important After the area, no matter which group of data is selected to determine SCP deployment, the gap is not too large, and the SEB distribution condition of dynamic change can be better adapted.
The experiments show that the technical method disclosed by the invention can be more suitable for the dynamically changed SEB distribution condition, and can simultaneously minimize the deployment quantity of SCPs, so that the investment required by an SEB operator to deploy SCPs in the early stage is reduced, and the problem that the SCPs are difficult to move once deployed is well solved.

Claims (4)

1. A static charging pile deployment method based on cluster division comprises the following steps:
s1, establishing a network model for a shared electric bicycle SEB and a static charging pile SCP;
s2, aiming at the network model established in the step S1, dividing a plurality of SEB distribution graphs into relatively dense areas and relatively sparse areas, and determining the intersection area of the relatively dense areas in the SEB distribution graphs as an SCP charging range; the relatively dense area is defined as an area containing the most SEB; particularly, a plurality of SEB distribution graphs are divided into a relatively dense area and a relatively sparse area by adopting a k-means algorithm;
in specific implementation, the following rules are adopted to determine the SCP charging range:
r1. If relatively dense region b 1 ,b 2 ,...,b e If no unique intersection exists, calculating the average value of the SEB numbers in all intersections, and regarding the intersection area containing the SEB numbers higher than the average value as the SCP charging range P important ；
R2. If the area is relatively dense b 1 ,b 2 ,...,b e If there is a unique intersection, the intersection area is regarded as the SCP charging range P important ；
R3. If the area is relatively dense b 1 ,b 2 ,...,b e Two by two are not intersected with each other, then a relatively dense area b is calculated 1 ,b 2 ,...,b e Average value of the inner SEB number, and a relatively dense region containing SEB numbers not less than the average value is regarded as the SCP charging range P important ；
S3, constructing a minimum SCP quantity optimization function capable of covering all SEBs in all target areas;
s4, solving the problem of the optimization function constructed in the step S3 based on the group division problem to obtain the initial deployment position of the SCP;
and S5, optimizing the initial deployment position of the SCP obtained in the step S4 so as to obtain the final deployment position of the SCP.
2. The method for deploying the static charging piles based on the clique partition according to claim 1, wherein the step S3 of constructing a minimum SCP number optimization function capable of covering all SEBs in all target areas is specifically constructed by adopting the following steps:
A. calculating a to-be-selected deployment area D of the SCP by adopting the following formula:
D＝D ∩ ∪D al
in the formula D ∩ ＝{D ∩1 ,D ∩2 ,...,D ∩s Denotes SCP charging range P important Intersection of the belonged ranges of all SEB in the region;
Figure FDA0003985729660000021
indicating all the belonged ranges of SEBs for which there is no intersection with any remaining SEBs;
B. calculating the deployment area D to be selected by adopting the following formula j SEB set G within j ：
Figure FDA0003985729660000022
In the formula, the | N | is the number of SEB; d i Is the belonged range of the ith SEB;
C. the following model was used as the minimum SCP number optimization function:
Figure FDA0003985729660000023
constraint conditions are as follows:
Figure FDA0003985729660000024
where i belongs to {1,2,. And G | }, x j ∈{0,1}，c ij ∈{0,1}，
Figure FDA0003985729660000025
D is the number of the deployment areas to be selected; />
Figure FDA0003985729660000026
3. The method for deploying static charging piles based on clique partition according to claim 2, wherein the problem of the optimization function constructed in the step S3 is solved by the problem of the clique partition in the step S4, specifically, the problem of the optimization function constructed in the step S3 is solved by a heuristic algorithm.
4. The method for deploying the static charging piles based on the cluster division according to claim 3, wherein the preliminary deployment position of the SCP obtained in the step S4 is optimized in the step S5 by adopting the following rules:
r1, if the SEBs in the same cluster have a unique common area, the geometric center position of the common area is the deployment position of the SCP;
and r2, if the SEB in the same group does not have the unique common area, sequentially isolating the area to which any SEB belongs in the group, and judging whether the rest SEBs have the unique common area: if the unique common area exists, taking the geometric center position of the unique common area and the geometric center position of the area to which the isolated SEB belongs as the deployment position of the SCP; if no unique public area is found in the area to which any SEB belongs by sequentially isolating the cluster, deploying SCP at the geometric center of the generated intersection area;
r3, if the range to which 3 SEBs belong forms 3 public areas in the same cluster, and the 3 public areas are compared with 1 point, the public point is taken as the deployment position of the SCP.
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