CN109858676A - Electric car electric charging demand for services model prediction method based on clustering algorithm - Google Patents

Electric car electric charging demand for services model prediction method based on clustering algorithm Download PDF

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Publication number
CN109858676A
CN109858676A CN201811640079.9A CN201811640079A CN109858676A CN 109858676 A CN109858676 A CN 109858676A CN 201811640079 A CN201811640079 A CN 201811640079A CN 109858676 A CN109858676 A CN 109858676A
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China
Prior art keywords
electric car
electric
charging demand
sample
electric charging
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Pending
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CN201811640079.9A
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Chinese (zh)
Inventor
刘亚丽
李树鹏
吕金炳
李国栋
王旭东
王天昊
崇志强
马世乾
王峥
于光耀
李树青
胡晓辉
刘云
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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Application filed by State Grid Corp of China SGCC, State Grid Tianjin Electric Power Co Ltd, Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN201811640079.9A priority Critical patent/CN109858676A/en
Publication of CN109858676A publication Critical patent/CN109858676A/en
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/14—Plug-in electric vehicles

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  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The electric car electric charging demand for services model prediction method based on clustering algorithm that the present invention relates to a kind of, technical characterstic are: the following steps are included: step 1, inquiry electric car information, regional planning information and it is built, building the element with built in advance charge station information as electric car electric charging demand model；Step 2, the element results inquired according to step 1, establish electric car electric charging demand model.The present invention forms the demand model of the electric car charging of meter and power grid bearing capacity by research automobile user electricity consumption behavioral trait, provides reliable reference for the addressing constant volume planning of electric car charging and conversion electric facility.

Description

Electric car electric charging demand for services model prediction method based on clustering algorithm
Technical field
The invention belongs to electric power facility planning technology fields, are related to electric car electric charging demand for services model prediction side Method is based especially on the electric car electric charging demand for services model prediction method of clustering algorithm.
Background technique
Currently, new-energy automobile has the features such as environmental-friendlyization, the energy is using high efficiency as new industry.New energy Source automobile is a significant industry of the New Economy in industrial circle.China for the support that new-energy automobile develops will be just like The past, the encouragement of electric car charging and conversion electric facility development is only increased, the prospect to electric car overall development is Good for a long time.For the charge requirement for meeting electric car, each province is all in the construction for carrying forward vigorously charging station and charging pile.
Want scientific and reasonable planning and build the needs that electric car electric charging service network meets masses, just needs first Have the accurate Demand Forecast Model of science, as guiding plan construction electric car electric charging service network it is important according to The influence of numerous practical factors such as electric car is considered according to, the foundation of Demand Forecast Model, the influence in region, built filled The influence etc. in power station, the influences such as there are also the trips of numerous uncontrollable factors such as user to influence, the electric charging rule of user.It can The Demand Forecast Model that these factors are integrally formed a set of science there is into great meaning to the development of new-energy automobile industry.
In conclusion being badly in need of forming the Demand Forecast Model of a set of science, it can assist or instruct optimization planning electronic The addressing constant volume of automobile charging and conversion electric service network pushes new-energy automobile industry actively to advance.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, proposes that a kind of electric car based on clustering algorithm fills and change Electric demand for services model prediction method forms meter and power grid bearing capacity by analyzing automobile user electricity consumption behavioral trait Electric car charging demand model, for electric car charging and conversion electric facility addressing constant volume plan reliable reference is provided.
The present invention solves its realistic problem and adopts the following technical solutions to achieve:
A kind of electric car electric charging demand for services model prediction method based on clustering algorithm, comprising the following steps:
Step 1, inquiry electric car information, regional planning information and it is built, building and built in advance charge station information conduct The element of electric car electric charging demand model；
Step 2, the element results inquired according to step 1, establish electric car electric charging demand model.
Moreover, the step 2 method particularly includes: according to the element results that step 1 is inquired, established by K clustering algorithm Electric car electric charging demand model, specific steps include:
(1) planning region is divided into several net regions, by the electric car electric charging demand of each net region Property is expressed as n-dimensional spaceOn vector x (a1,a2,a3……an), wherein each component of vector x is being somebody's turn to do for quantization means The electric car electric charging demand property information of net region；
(2) several vector x are chosen as training sample { x(1),x(2)……x(m), sample size m；
(3) k cluster center of mass point is randomly selected from sample
(4) for each sample i, its class that should belong to is calculated:
For each class j, such mass center is recalculated:
(5) step 4 is repeated until canonical measure function convergence.
Moreover, the step 2 method particularly includes: according to the element results that step 1 is inquired, pass through K central cluster algorithm Establish electric car electric charging demand model；
Its specific steps includes:
(1) planning region is divided into several net regions, by the electric car electric charging demand of each net region Property is expressed as n-dimensional spaceOn vector x (a1,a2,a3……an), wherein each component of vector x is being somebody's turn to do for quantization means The electric car electric charging demand property information of net region；
(2) several vector x are chosen as training sample { x(1),x(2)……x(m), sample size m；
(3) k center of mass point is randomly selected from sample
(4) point for concentrating sample point to be clustered is assigned to nearest center of mass point, forms k race；
(5) calculate all sample points in each race to one of sample point manhatton distance with；
(6) selecting makes in the race manhatton distance and the smallest sample point as mass center；
(7) step 4 is repeated to step 6 until meeting the number of iterations or error range.
The advantages of the present invention:
The present invention assigns its attribute by dividing to region, and by each region with actual conditions, based on trip Chain theory determines automobile passage situation in region, the letter such as ownership of space-time characterisation and electric car referring again to user's charging Breath, (including electric car information data, electric charging station information data and planning region information data), and based on scale The background for changing electric car access ultra-large type urban distribution network, studies automobile user electricity consumption behavioral trait, while considering power grid Bearing capacity finally show that optimal electric car fills by successive ignition calculating using K central cluster algorithm (K-medoids) Electric demand for services model is changed, Accurate Prediction scientifically is carried out to the electric charging demand of electric car charging and conversion electric service network, thus Reliable reference and effectively guidance can be provided the addressing constant volume of subsequent optimization planning electric car charging and conversion electric service network, in turn New-energy automobile industry is pushed actively to advance.
Detailed description of the invention
Fig. 1 is that the present invention establishes electric car electric charging demand model flow chart；
Fig. 2 is that the present invention uses K clustering algorithm to establish electric car electric charging demand model flow chart；
Fig. 3 is that the present invention uses K central cluster algorithm to establish electric car electric charging demand model flow chart.
Specific embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing:
A kind of electric car electric charging demand for services model prediction method based on clustering algorithm, as shown in Figure 1, include with Lower step:
Step 1, inquiry electric car information, regional planning information and it is built, building and built in advance charge station information conduct The element of electric car electric charging demand model；
Key element is as shown in table 1:
1 electric car electric charging demand model element table of table
Embodiment one: step 2, the element results inquired according to step 1 are established electronic by K clustering algorithm (K-means) Automobile electric charging demand model；
As shown in Fig. 2, the specific steps of the step 2 include:
(1) planning region is divided into several net regions, by the electric car electric charging demand of each net region Property is expressed as n-dimensional spaceOn vector x (a1,a2,a3……an), wherein each component of vector x is being somebody's turn to do for quantization means The electric car electric charging demand property information of net region；
(2) several vector x are chosen as training sample { x(1),x(2)……x(m), sample size m；
(3) k cluster center of mass point is randomly selected from sample
(4) for each sample i, its class that should belong to is calculated:
For each class j, such mass center is recalculated:
(5) step S4 is repeated until canonical measure function convergence.
In the present embodiment, canonical measure function is mean square deviation.
Using k clustering algorithm, preferable Clustering Effect can be reached within the shorter calculating time, and is easy to explain；But It is more sensitive to abnormal data, and needs artificial determining k value, to determine that k value needs many experiments.
Embodiment two: step 2, the element results inquired according to step 1 are built by K central cluster algorithm (K-medoids) Vertical electric car electric charging demand model；
The specific steps of the step 2 include:
(1) planning region is divided into several net regions, by the electric car electric charging demand of each net region Property is expressed as n-dimensional spaceOn vector x (a1,a2,a3……an), wherein each component of vector x is being somebody's turn to do for quantization means The electric car electric charging demand property information of net region；
(2) several vector x are chosen as training sample { x(1),x(2)……x(m), sample size m；
(3) k center of mass point is randomly selected from sample
(4) point for concentrating sample point to be clustered is assigned to nearest center of mass point, forms k race；
(5) calculate all sample points in each race to one of sample point manhatton distance with；
(6) selecting makes in the race manhatton distance and the smallest sample point as mass center；
(7) step 4 is repeated to step 6 until meeting the number of iterations or error range.
It is less sensitive to abnormal data using K central cluster algorithm compared with K clustering algorithm, but due to being in The mode of heart point selection is calculated, and the time complexity of algorithm also rises O (n) than K clustering algorithm.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore the present invention includes It is not limited to embodiment described in specific embodiment, it is all to be obtained according to the technique and scheme of the present invention by those skilled in the art Other embodiments, also belong to the scope of protection of the invention.

Claims (3)

1. a kind of electric car electric charging demand for services model prediction method based on clustering algorithm, it is characterised in that: including with Lower step:
Step 1, inquiry electric car information, regional planning information and it is built, building with built in advance charge station information as electronic The element of automobile electric charging demand model；
Step 2, the element results inquired according to step 1, establish electric car electric charging demand model.
2. a kind of electric car electric charging demand for services model prediction side based on clustering algorithm according to claim 1 Method, it is characterised in that: the step 2 method particularly includes: according to the element results that step 1 is inquired, established by K clustering algorithm Electric car electric charging demand model, specific steps include:
(1) planning region is divided into several net regions, by the electric car electric charging demand property of each net region It is expressed as n-dimensional spaceOn vector x (a1,a2,a3……an), wherein each component of vector x is the grid of quantization means The electric car electric charging demand property information in region；
(2) several vector x are chosen as training sample { x(1),x(2)……x(m), sample size m；
(3) k cluster center of mass point is randomly selected from sample
(4) for each sample i, its class that should belong to is calculated:
For each class j, such mass center is recalculated:
(5) step 4 is repeated until canonical measure function convergence.
3. a kind of electric car electric charging demand for services model prediction side based on clustering algorithm according to claim 1 Method, it is characterised in that: the step 2 method particularly includes: according to the element results that step 1 is inquired, pass through K central cluster algorithm Electric car electric charging demand model is established, specific steps include:
(1) planning region is divided into several net regions, by the electric car electric charging demand property of each net region It is expressed as n-dimensional spaceOn vector x (a1,a2,a3……an), wherein each component of vector x is the grid of quantization means The electric car electric charging demand property information in region；
(2) several vector x are chosen as training sample { x(1),x(2)……x(m), sample size m；
(3) k center of mass point is randomly selected from sample
(4) point for concentrating sample point to be clustered is assigned to nearest center of mass point, forms k race；
(5) calculate all sample points in each race to one of sample point manhatton distance with；
(6) selecting makes in the race manhatton distance and the smallest sample point as mass center；
(7) step 4 is repeated to step 6 until meeting the number of iterations or error range.
CN201811640079.9A 2018-12-29 2018-12-29 Electric car electric charging demand for services model prediction method based on clustering algorithm Pending CN109858676A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111591152A (en) * 2020-05-19 2020-08-28 浙江秦欧控股集团有限公司 Battery pack power change decision method, device and system in charge and change separation mode

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CN102903037A (en) * 2011-07-28 2013-01-30 上海拉手信息技术有限公司 Siting method of distribution centers
CN103793750A (en) * 2012-10-30 2014-05-14 国际商业机器公司 Method and apparatus for disposing charging/replacing station in area
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
CN107169605A (en) * 2017-05-18 2017-09-15 东南大学 City electric car charging station site selecting method based on vehicle location information
CN109066663A (en) * 2018-08-31 2018-12-21 国网上海市电力公司 Consider the price competing method of electric car cluster grouping

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Cited By (2)

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Publication number Priority date Publication date Assignee Title
CN111591152A (en) * 2020-05-19 2020-08-28 浙江秦欧控股集团有限公司 Battery pack power change decision method, device and system in charge and change separation mode
CN111591152B (en) * 2020-05-19 2021-11-09 浙江秦欧控股集团有限公司 Battery pack power change decision method, device and system in charge and change separation mode

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