CN107871184A - A kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment - Google Patents
A kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment Download PDFInfo
- Publication number
- CN107871184A CN107871184A CN201711138250.1A CN201711138250A CN107871184A CN 107871184 A CN107871184 A CN 107871184A CN 201711138250 A CN201711138250 A CN 201711138250A CN 107871184 A CN107871184 A CN 107871184A
- Authority
- CN
- China
- Prior art keywords
- mrow
- charge requirement
- msub
- cluster
- charging station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000004364 calculation method Methods 0.000 claims abstract description 6
- 238000000342 Monte Carlo simulation Methods 0.000 claims abstract description 4
- 239000012141 concentrate Substances 0.000 claims abstract description 4
- 230000005611 electricity Effects 0.000 claims description 7
- IOYNQIMAUDJVEI-BMVIKAAMSA-N Tepraloxydim Chemical compound C1C(=O)C(C(=N/OC\C=C\Cl)/CC)=C(O)CC1C1CCOCC1 IOYNQIMAUDJVEI-BMVIKAAMSA-N 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims description 3
- 238000012986 modification Methods 0.000 claims description 3
- 238000013439 planning Methods 0.000 description 4
- 238000012913 prioritisation Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment, comprise the following steps：Step 1: obtain charge requirement point set；Step 1, according to existing electric automobile on highway running data, obtain electric automobile all charge requirement dot position informations on a highway using monte carlo method；Step 2, charging station, acquisition service radius are built in Expressway Service；Step 3, calculation procedure 2 charging station particular location service radius neighborhood in charge requirement point, by the charge requirement point outside service radius neighborhood be added to charge requirement point concentrate, and using existing charging station as charge requirement point be added to charge requirement point concentration；Step 2: the charge requirement point concentrated to charge requirement point is clustered using improved K means algorithms so that cluster object function is minimum, so as to obtain final charging station location.The present invention can cover more charge requirement points, so as to meet the charge requirement of more automobile users.
Description
Technical field
The present invention relates to distribution technique field, particularly a kind of choosing of the electric automobile charging station of facing area electrically-charging equipment
Location method.
Background technology
Today's society, electric automobile replace fuel automobile to have become the inexorable trend of China Automobile Industry, and this will be complete
The world is rapidly developed.Electric automobile can not only improve energy utilization rate, the discharge to reduce environmental pollution with greenhouse gases, also
The security of Operation of Electric Systems and economy can be improved by the synergy to be generated electricity with the intermittent renewable energy, but
Be electric automobile charging station addressing it is improper, may influence urban traffic network programming and distribution, electric automobile optimization trip
Convenience, and then have influence on the extensive use of electric automobile, it is also possible to cause power consumption to increase.
So far, charging station is built in service area mostly, does not fully take into account electric automobile layout scenarios, yet
There is no complete system to be studied for the specific position of charging station.The existing document about addressing, it is electronic vapour mostly
The estimation charging station of car course continuation mileage and transportation network roughly needs the position established, and some is simply on existing charging station basis
On get rid of the charge requirement point that existing charging station is covered, build charging station on remaining charge requirement point, the above is ground
Study carefully be likely to result in charging station occur overcrowding and some charge requirement points be not covered with to the problem of.
In view of the urban transportation public service attribute of electric automobile charging station and its be linked into power network for distribution
The influence of net, a kind of (the electric automobile charging station addressing constant volume side based on two benches optimization of patent application 201510627316.8
Method) using charging electric vehicle logic as constraint, it is proposed that a kind of multistage charging addressing constant volume scheme based on capacitance grade.This side
Case but does not account for the charge requirement of electric automobile itself although it is contemplated that the economy of electric grid investment operation, may
The electric automobile to be charged such as cause some electric automobile charging stations and need excessively cause serious queuing phenomena, some fill in addition
Power station is likely to result in the very few phenomenon of electric automobile of charging.
A kind of (the electric automobile charging station siteselecting planning side based on queueing theory algorithm of patent application 201610121901.5
Method) optimal facility number in electrical changing station is calculated, using the estimated medium-sized charging station (transformer station) of electrical changing station as holistic approach object, structure
The optimal models that target is minimised as with operation cost and cost of transportation is built, finally utilizes the model realization electric automobile charging station
Siteselecting planning.This patent application take into full account electrical changing station construction cost, transport energy consumption and user power utilization demand on the basis of,
Plan meets the site selection model of urban construction requirement.Although this patent application considers to transport from automobile user angle
Energy consumption problem, then the optimal candidate point of addressing turns into the charging station for needing to build from existing charging station candidate point, still
The particular location (i.e. charge requirement point) that actual shipment circuit gets on to consider that electric automobile needs charge is not bound with to fill to plan
Power station.
Patent application 201510515806.9 (a kind of planing method of electric automobile on highway quick charge station), the party
Method obtains the charge requirement of electric automobile point by gathering on daily highway electric automobile during traveling data, calculates electronic vapour
Car reaches service radius of the distance that can be travelled during predetermined electricity as charging station, and service area first is established into charging station, excludes
Charge requirement point, recycles shared k-nearest neighbor to determine the position of charging station afterwards in built charging station service radius, this
Charging electric vehicle demand point particular location is considered in patent application, and the position than traditional electric automobile charging station can more meet
The actual charge requirement of electric automobile.This method does not consider that built charging station is lined up situation, is so likely to result in charging
The charging pressure stood, in addition this method can also cause the situation that more charge requirement point can not charge, that is, the charging that covers needs
Ask a little less.
The content of the invention
The technical problems to be solved by the invention are overcome the deficiencies in the prior art and provide a kind of facing area charging and set
The site selecting method for the electric automobile charging station applied, the present invention can cover more charge requirement points, more electronic so as to meet
The charge requirement of user vehicle.
The present invention uses following technical scheme to solve above-mentioned technical problem：
According to a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment proposed by the present invention, including with
Lower step：
Step 1: obtain charge requirement point set；It is specific as follows：
Step 1, according to existing electric automobile on highway running data, utilize monte carlo method to obtain electric automobile
All charge requirement dot position informations on a highway；
Step 2, charging station, acquisition service radius are built in Expressway Service；The service radius is：Statistics is all
The maximum distance that can be travelled after charging electric vehicle, using central-limit theorem, electric automobile is obtained by the normal distribution being fitted
The distance that can be travelled in dump energy, by the service radius that the distance definition is charging station；
Step 3, calculation procedure 2 charging station particular location service radius neighborhood in charge requirement point, by service radius
Charge requirement point outside neighborhood is added to charge requirement point concentration, and is added to charging using existing charging station as charge requirement point
Demand point is concentrated；
Step 2: the charge requirement point concentrated to charge requirement point is clustered using improved K-means algorithms so that
It is minimum to cluster object function, so as to obtain final charging station location；It is specific as follows：
Step I, according to charge requirement point set, the charge requirement point number for making charge requirement point concentrate is m, makes iterations
For R, it is determined that needing increased charging electric vehicle number k；
Step II, according to acquired charge requirement point set, be randomly divided into k cluster, randomly select k charge requirement point minute
Cluster centre not as this k cluster, f-th of cluster z of the r times iterationfCenter be Mf(r), wherein f=1,2 ..., k, r=
1,2,…R；
Step III, calculate charge requirement point concentration charge requirement point NeWith the minimum range D (N of each cluster centree,Mf
(r)), e=1,2 ..., m, if charge requirement point NeTo cluster zfDistance it is minimum, then charge requirement point belongs to cluster zf, count again
Cluster centre is calculated, has thus repartitioned cluster；
Step IV, the cluster repartitioned according to step III calculate cluster object function, if cluster target function value is less than
The knots modification that default first threshold or cluster target function value clustered target function value relative to last time is less than default second
Threshold value, then algorithm stopping are final to obtain charging station particular location；Otherwise cluster centre is recalculated, goes to step III.
It is further as a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment of the present invention
Prioritization scheme, the electric automobile on highway running data described in step 1 include the residue that electric automobile enters highway
Electricity, electric automobile need the distance that the electricity to charge and electric automobile needs arrive at.
It is further as a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment of the present invention
Prioritization scheme, D (N in step IIIe,Mf(r)) calculation formula is as follows：
D(Ne,Mf(r))=min { D (N1,Mf(r)),D(N2,Mf(r)),…,D(Nm,Mf(r))} (1)
Then Ne∈Mf(r)。
It is further as a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment of the present invention
Prioritization scheme, cluster object function in step IV and recalculate cluster centre formula it is as follows：
Wherein, cjRepresent the positional information of j-th of cluster centre, uijRepresent whether i-th of charge requirement point reaches j-th
The mark of cluster centre charging,Represent xiTo place cjSquare of distance, xiRepresent cluster zfIn i-th of charge requirement
The positional information of point, SUMjRepresent the charge requirement point total number in j-th of cluster.
It is further as a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment of the present invention
Prioritization scheme, the distance that 99% electric automobile can travel in dump energy is obtained by the normal distribution being fitted in step 2.
The present invention compared with prior art, has following technique effect using above technical scheme：
(1) present invention is mainly from two angles of electric automobile charging station, according to the electric automobile charging station of reality
Queuing situation and the situation of the existing charge requirement point not covered cluster to carry out one, finally electric automobile are filled
Congested conditions are lined up in power station to be eased, and can also cover more charge requirement points, it is more used for electric vehicle so as to meet
The charge requirement at family；
(2) compared to a kind of (planning of electric automobile on highway quick charge station of patent application 201510515806.9
Method) service area established into charging station and then the method for being clustered remaining unlapped charge requirement point, our side
Method fully alleviates the situation that charging station in service area waits in line charging；
(3) the inventive method can meet the charge requirement of charge requirement point in the radius neighborhood of built charging station place, enter
And alleviate the charging pressure of built charging station；
(4) existing charging station is built in service area, as the increase of electric automobile quantity is likely to result in charging station
Situation about inside waiting in line, then we are using built charging station as charge requirement point, by these charge requirement points progress K-
Means is clustered, and wherein k is the number that charging station increases charging station newly, and specifically newly-increased charging station location is obtained finally by cluster；
The method can cover more charge requirement points.
Brief description of the drawings
Fig. 1 is the acquisition flow chart of charge requirement point.
Fig. 2 is improved K-means algorithm flow charts.
Embodiment
In order that the object, technical solutions and advantages of the present invention are clearer, below in conjunction with the accompanying drawings and the specific embodiments
The present invention will be described in detail.
Step 1: the acquisition of charge requirement point
A kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment, accompanying method are directed to existing charging station
It is lined up in excessive, causes crowded situation, using built electric automobile charging station as charge requirement point, and this demand point is added
Enter charge requirement point Candidate Set, calculate charging station currently without the charge requirement point covered, these points are added to Candidate Set,
As shown in Figure 1.
Wherein, the process of specific each step is as follows：
Step 1, according to existing electric automobile on highway running data, electronic vapour is obtained using monte carlo method
Car all charge requirement dot position informations on a highway；
Step 2, charging station, acquisition service radius, service herein are built in Expressway Service according to existing information
Radius is：The maximum distance that can be travelled after all charging electric vehicles is counted, using central-limit theorem, by the normal state point being fitted
Cloth obtains the distance that 99% electric automobile can travel in dump energy, the service half by the distance definition for charging station
Footpath；
Step 3, calculation procedure 2 charging station particular location service radius neighborhood in charge requirement point, will service half
Charge requirement point outside footpath is added to charge requirement point concentration, and is added to charging need using existing charging station as charge requirement point
Seek a concentration；
Step 2: improved K-means algorithms cluster to charge requirement point
The charge requirement point set of above-mentioned acquisition is clustered using improved K-means clustering algorithms, it is determined that last increase
The charging station particular location added, K-means algorithms are i.e. m charge requirement dot position information xi(i=1,2 ..., m) it is divided into k
Cluster, and seek the cluster centre of each cluster so that cluster object function reaches minimum value.
Fig. 2 be improved K-means algorithm flow charts wherein, the process of specific each step is as follows：
Step 1, according to charge requirement point set, the charge requirement point number for making charge requirement point concentrate is m, makes iterations
For R, it is determined that needing increased charging electric vehicle number k；
Step 2, according to acquired charge requirement point set, be randomly divided into k cluster, randomly select k charge requirement point minute
Cluster centre not as this k cluster, the center of f-th of cluster of the r times iteration is Mf(r), wherein f=1,2 ..., k, r=1,
2,…R；
Step 3, calculate charge requirement point concentration charge requirement point Ne(e=1,2 ..., m) and each cluster centre minimum
Distance D (Ne,Mf(r)), if charge requirement point NeTo cluster zfDistance it is minimum, then charge requirement point belongs to cluster zf, recalculate
Cluster centre, cluster is thus repartitioned；I.e.
D(Ne,Mf(r))=min { D (N1,Mf(r)),D(N2,Mf(r)),…,D(Nm,Mf(r))} (1)
Then Ne∈Mf(r)。
Step 4, cluster object function is calculated according to formula (2), if cluster target function value is less than the threshold value of some determination
Or it is less than some threshold value relative to the knots modification of last time cluster target function value, then algorithm stops, final to obtain charging station tool
Body position；Otherwise cluster centre is recalculated according to formula (5), goes to step 3.
Wherein, cjRepresent the positional information of j-th of cluster centre, uijRepresent whether i-th of charge requirement point reaches j-th
The mark of cluster centre charging,Represent xiTo place cjSquare of distance, xiRepresent cluster zfIn i-th of charge requirement
The positional information of point, SUMjRepresent the charge requirement point total number in j-th of cluster.
Step 3: the final object function of the present invention
Cluster is carried out by step 2 and obtains newly-built charging station, according to charging station operation cost and electric automobile in charging station
The aspect of delay cost two generate final object function.Final object function herein includes two parts charging station operation
The delay cost of cost and electric automobile in charging station.
C=C1+C2 (6)
C2=mCwWq (8)
Wherein, C is totle drilling cost, C1For operation cost；e1(equipment is included for the operation cost of an electric automobile charging station
Maintenance cost, depreciable cost, human cost)；For the cost in soil shared by electric automobile charging station；C2For electric automobile
In the charging station stand-by period cost distributed；CwWaited by each electric automobile in the charging station distributed one hour
Time cost；WqThe time waited by each electric automobile required for the electric automobile charging station distributed；When λ represents unit
Between enter charging station electric automobile quantity.
Step 4: analysis of experimental results
The present invention is tested by charging electric vehicle data actual on actual annular highway, first by
Build charging station to add in charging Candidate Set as charge requirement point, the charge requirement point for being then not covered with former charging station adds
Enter into Candidate Set, finally charge requirement point is clustered, obtained last experimental result and patent application
(201510515806.9 a kind of planing method of electric automobile on highway quick charge station) is contrasted.
Table 1 shows a kind of (rule of electric automobile on highway quick charge station of patent application 201510515806.9
The method of drawing) actual charging station covering charge requirement point number, each charging station waits in line situation, and table 2 shows this
The actual charging station covering charge requirement point number of invention, each charging station wait in line situation.
A kind of (the planning side of electric automobile on highway quick charge station of 1 patent application of table 201510515806.9
Method) experimental result
The experimental result of the present invention of table 2
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, the change or replacement that can readily occur in, all should
Cover within the scope of the present invention.
Claims (5)
1. a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment, it is characterised in that comprise the following steps：
Step 1: obtain charge requirement point set；It is specific as follows：
Step 1, according to existing electric automobile on highway running data, obtain electric automobile in height using monte carlo method
All charge requirement dot position informations on fast highway；
Step 2, charging station, acquisition service radius are built in Expressway Service；The service radius is：Count all electronic
The maximum distance that can be travelled after automobile charging, using central-limit theorem, electric automobile is obtained surplus by the normal distribution being fitted
The distance that can be travelled during remaining electricity, by the service radius that the distance definition is charging station；
Step 3, calculation procedure 2 charging station particular location service radius neighborhood in charge requirement point, by service radius neighborhood
Outer charge requirement point is added to charge requirement point concentration, and is added to charge requirement using existing charging station as charge requirement point
Point is concentrated；
Step 2: the charge requirement point concentrated to charge requirement point is clustered using improved K-means algorithms so that cluster
Object function is minimum, so as to obtain final charging station location；It is specific as follows：
Step I, according to charge requirement point set, the charge requirement point number for making charge requirement point concentrate is m, and it is R to make iterations,
It is determined that need increased charging electric vehicle number k；
Step II, according to acquired charge requirement point set, be randomly divided into k cluster, randomly select k charge requirement point and make respectively
For the cluster centre of this k cluster, f-th of cluster z of the r times iterationfCenter be Mf(r), wherein f=1,2 ..., k, r=1,
2,…R；
Step III, calculate charge requirement point concentration charge requirement point NeWith the minimum range D (N of each cluster centree,Mf(r)), e
=1,2 ..., m, if charge requirement point NeTo cluster zfDistance it is minimum, then charge requirement point belongs to cluster zf, recalculate cluster
Center, cluster is thus repartitioned；
Step IV, the cluster repartitioned according to step III calculate cluster object function, are preset if cluster target function value is less than
First threshold or cluster target function value relative to last time cluster target function value knots modification be less than default Second Threshold,
Then algorithm stops, final to obtain charging station particular location；Otherwise cluster centre is recalculated, goes to step III.
2. a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment according to claim 1, it is special
Sign is that the electric automobile on highway running data described in step 1 includes the residue electricity that electric automobile enters highway
Amount, electric automobile need the distance that the electricity to charge and electric automobile needs arrive at.
3. a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment according to claim 1, it is special
Sign is, D (N in step IIIe,Mf(r)) calculation formula is as follows：
D(Ne,Mf(r))=min { D (N1,Mf(r)),D(N2,Mf(r)),…,D(Nm,Mf(r))} (1)
Then Ne∈Mf(r)。
4. a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment according to claim 1, it is special
Sign is that the cluster object function in step IV and the formula for recalculating cluster centre are as follows：
<mrow>
<mi>Z</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>c</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<msubsup>
<mi>d</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>c</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>d</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>c</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>|</mo>
<mo>|</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>c</mi>
<mi>j</mi>
</msub>
<mo>|</mo>
<mo>|</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mn>1</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>c</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<msub>
<mi>SUM</mi>
<mi>j</mi>
</msub>
</mrow>
</msubsup>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
</mrow>
<mrow>
<msub>
<mi>SUM</mi>
<mi>j</mi>
</msub>
</mrow>
</mfrac>
<mo>,</mo>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, cjRepresent the positional information of j-th of cluster centre, uijRepresent whether i-th of charge requirement point reaches j-th of cluster
The mark of center charging,Represent xiTo place cjSquare of distance, xiRepresent cluster zfIn i-th charge requirement point
Positional information, SUMjRepresent the charge requirement point total number in j-th of cluster.
5. a kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment according to claim 1, it is special
Sign is, the distance that 99% electric automobile can travel in dump energy is obtained by the normal distribution being fitted in step 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711138250.1A CN107871184A (en) | 2017-11-16 | 2017-11-16 | A kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711138250.1A CN107871184A (en) | 2017-11-16 | 2017-11-16 | A kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107871184A true CN107871184A (en) | 2018-04-03 |
Family
ID=61754192
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711138250.1A Pending CN107871184A (en) | 2017-11-16 | 2017-11-16 | A kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107871184A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108764555A (en) * | 2018-05-22 | 2018-11-06 | 浙江大学城市学院 | A kind of shared bicycle based on Hadoop parks a site selecting method |
| CN108973750A (en) * | 2018-08-13 | 2018-12-11 | 青岛特锐德电气股份有限公司 | A kind of Vehicular charging method and apparatus |
| CN109145989A (en) * | 2018-08-22 | 2019-01-04 | 深圳市东部公共交通有限公司 | Bus station distribution method, device and terminal |
| CN109840635A (en) * | 2019-01-29 | 2019-06-04 | 三峡大学 | Electric automobile charging station planing method based on voltage stability and charging service quality |
| CN110543976A (en) * | 2019-08-14 | 2019-12-06 | 河海大学常州校区 | Charging station layout optimization method based on genetic algorithm |
| CN110728421A (en) * | 2019-08-30 | 2020-01-24 | 山东理工大学 | Road network charging optimization method based on charging demand big data |
| CN110852547A (en) * | 2019-08-19 | 2020-02-28 | 上海元卓信息科技有限公司 | Public service facility grading method based on position data and clustering algorithm |
| CN111861527A (en) * | 2019-04-30 | 2020-10-30 | 中移(苏州)软件技术有限公司 | Method and device for determining electric vehicle charging station and storage medium |
| CN113077113A (en) * | 2021-05-10 | 2021-07-06 | 成都特来电新能源有限公司 | Intelligent planning and design method for charging infrastructure |
| CN113971484A (en) * | 2021-10-20 | 2022-01-25 | 朱珈汛 | Planning method of electric vehicle charging station based on CRITIC method and non-cooperative game |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951978A (en) * | 2017-02-20 | 2017-07-14 | 国网天津市电力公司 | A kind of city concentrated charging station planing method based on improvement K means algorithms |
-
2017
- 2017-11-16 CN CN201711138250.1A patent/CN107871184A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951978A (en) * | 2017-02-20 | 2017-07-14 | 国网天津市电力公司 | A kind of city concentrated charging station planing method based on improvement K means algorithms |
Non-Patent Citations (3)
| Title |
|---|
| AKANKSHA SHUKLA等: "Consumer Perspective based Placement of Electric Vehicle Charging Stations by Clustering Techniques", 《2016 NATIONAL POWER SYSTEMS CONFERENCE (NPSC)》 * |
| XIAOHONG DONG等: "Planning of Fast EV Charging Stations on a Round Freeway", 《IEEE TRANSACTIONS ON SUSTAINABLE ENERGY》 * |
| 高建平: "电动汽车充电站网络规划优化研究", 《中国优秀硕士学位论文全文数据库-工程科技Ⅱ辑》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108764555A (en) * | 2018-05-22 | 2018-11-06 | 浙江大学城市学院 | A kind of shared bicycle based on Hadoop parks a site selecting method |
| CN108764555B (en) * | 2018-05-22 | 2021-08-31 | 浙江大学城市学院 | Shared bicycle parking point site selection method based on Hadoop |
| CN108973750A (en) * | 2018-08-13 | 2018-12-11 | 青岛特锐德电气股份有限公司 | A kind of Vehicular charging method and apparatus |
| CN108973750B (en) * | 2018-08-13 | 2020-07-17 | 青岛特来电新能源科技有限公司 | Vehicle charging method and device |
| CN109145989A (en) * | 2018-08-22 | 2019-01-04 | 深圳市东部公共交通有限公司 | Bus station distribution method, device and terminal |
| CN109145989B (en) * | 2018-08-22 | 2021-07-06 | 深圳市东部公共交通有限公司 | Bus stop layout method and device and computer terminal |
| CN109840635A (en) * | 2019-01-29 | 2019-06-04 | 三峡大学 | Electric automobile charging station planing method based on voltage stability and charging service quality |
| CN111861527A (en) * | 2019-04-30 | 2020-10-30 | 中移(苏州)软件技术有限公司 | Method and device for determining electric vehicle charging station and storage medium |
| CN110543976A (en) * | 2019-08-14 | 2019-12-06 | 河海大学常州校区 | Charging station layout optimization method based on genetic algorithm |
| CN110543976B (en) * | 2019-08-14 | 2022-08-16 | 河海大学常州校区 | Charging station layout optimization method based on genetic algorithm |
| CN110852547A (en) * | 2019-08-19 | 2020-02-28 | 上海元卓信息科技有限公司 | Public service facility grading method based on position data and clustering algorithm |
| CN110728421A (en) * | 2019-08-30 | 2020-01-24 | 山东理工大学 | Road network charging optimization method based on charging demand big data |
| CN110728421B (en) * | 2019-08-30 | 2024-04-19 | 山东理工大学 | Road network charging optimization method based on charging demand big data |
| CN113077113A (en) * | 2021-05-10 | 2021-07-06 | 成都特来电新能源有限公司 | Intelligent planning and design method for charging infrastructure |
| CN113077113B (en) * | 2021-05-10 | 2023-12-12 | 成都特来电新能源有限公司 | Intelligent planning and designing method for charging infrastructure |
| CN113971484A (en) * | 2021-10-20 | 2022-01-25 | 朱珈汛 | Planning method of electric vehicle charging station based on CRITIC method and non-cooperative game |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107871184A (en) | A kind of site selecting method of the electric automobile charging station of facing area electrically-charging equipment | |
| CN102880921B (en) | A kind of electric automobile charging station Optimization Method for Location-Selection | |
| CN106951978B (en) | Urban concentrated charging station planning method based on improved K-means algorithm | |
| CN106651059B (en) | Optimal configuration method for electric vehicle charging station | |
| CN102722767B (en) | Electromobile charging and exchanging power station stationing and planning system and method | |
| CN103915869B (en) | A kind of Intelligent charging system of electric automobile based on mobile device and method | |
| WO2020199558A1 (en) | Method for planning optimal construction quantity and site selection scheme for electric vehicle charging stations | |
| CN108805321A (en) | A kind of electric automobile charging station planing method | |
| CN103208034B (en) | A kind of track traffic for passenger flow forecast of distribution model is set up and Forecasting Methodology | |
| CN107187442A (en) | The plug-in hybrid electric automobile EMS predicted based on operating mode | |
| CN107153893A (en) | A kind of electric automobile charging station Site planning method based on city parking data | |
| CN106965688A (en) | A kind of charging electric vehicle method under power network and the network of communication lines cooperative surroundings | |
| CN107169605A (en) | City electric car charging station site selecting method based on vehicle location information | |
| CN106875075A (en) | A kind of electric automobile charging station points distributing method based on travel behaviour | |
| CN111881939B (en) | Shared single-vehicle parking area layout method based on clustering algorithm | |
| CN104778832B (en) | A kind of multimode Urban Road Cross-section resource coordination collocation method | |
| CN108932561A (en) | A kind of electric car charge path selection method considering non-linear charge function | |
| Fathollahi et al. | Optimal siting and sizing of wireless EV charging infrastructures considering traffic network and power distribution system | |
| CN107730049A (en) | Electric vehicle rapid charging optimal location system of selection | |
| CN103714257A (en) | Public transportation problematic line recognition technology | |
| CN114186754A (en) | Electric vehicle charging station site selection and volume fixing optimization method containing different types of charging piles | |
| CN106373384A (en) | Remote area passenger transport regular bus route real-time generation method | |
| Yin et al. | Planning of electric vehicle charging station based on real time traffic flow | |
| CN106130110A (en) | The electric taxi charging station constant volume method on trip ground is selected based on stratified probability | |
| CN109389310A (en) | Electric car electrically-charging equipment maturity assessment method based on Monte Carlo simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180403 |
|
| RJ01 | Rejection of invention patent application after publication |