CN105160418A - Charging distribution predication method based on electric vehicle application features - Google Patents
Charging distribution predication method based on electric vehicle application features Download PDFInfo
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- CN105160418A CN105160418A CN201510473999.6A CN201510473999A CN105160418A CN 105160418 A CN105160418 A CN 105160418A CN 201510473999 A CN201510473999 A CN 201510473999A CN 105160418 A CN105160418 A CN 105160418A
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Abstract
The invention relates to a method for unified management of electric vehicle charging device in discrete distribution for plug-in electric vehicles. The invention aims to ensure reliable and flexible charging of the electric vehicles and to meet power grid load adjustment feature requirements. According to the invention, electric vehicle charging distribution regularities are predicated based on the operation features of the plug-in electric vehicles. The invention provides the electric vehicle charging distribution predication method used for solving a problem of orderless electric vehicle charging distribution from aspects of charging service operation, charging facility construction and power distribution network planning, and for providing reference basis for reducing investment waste and achieving reasonable resource configuration effectively.
Description
Technical field
The invention belongs to electric passenger vehicle Charge Management technology, be specifically related to a kind of charge profile Forecasting Methodology based on electric passenger vehicle application characteristic.
Background technology
The present invention relates to the electric passenger vehicle electrically-charging equipment Explore of Unified Management Ideas of discrete distribution needed for plug-in electric passenger vehicle, ensure that electric passenger vehicle reliably, flexibly charges, meet network load control characteristic demand simultaneously.Electric passenger vehicle refers to onboard power power supply for power source, uses motor car to travel, meets the requirements vehicle of road traffic laws and regulations simultaneously completely.Due to the minimizing day by day of petroleum-based energy, the use of electric passenger vehicle becomes a focus of automobile market, will replace combustion engine powered orthodox car gradually.
Electric passenger vehicle is travelled by motor car, provided the vehicle of electric energy by vehicle-mounted energy storage device (accumulator) or hydrogen-oxygen Blast Furnace Top Gas Recovery Turbine Unit (TRT) (fuel cell).The type of electric passenger vehicle application mainly comprises electrocar, electric motor coach (bus), electronic work vehicle, electronic special-purpose vehicle, battery-operated motor cycle, electric space vehicle, Sightseeing Trolley and electric three-wheel four-wheel commercial car etc.According to developing direction or the vehicle traction principle of current technology, electric passenger vehicle mainly divides pure electric passenger vehicle, fuel battery electric passenger car and hybrid electric passenger car three kinds.
In recent years, constantly soaring along with energy prices, and the development of electric automobile correlation technique, many countries of the world establish can provide the intelligent grid of convenient charging service for electric passenger vehicle.Meanwhile, Ge great automobile vendor of the world releases electric passenger car one after another, and national governments also release every policy to support the development of electric passenger vehicle industry.According to a up-to-date address prediction of PIKEResearch, to 2016, plug-in electric passenger vehicle (the PEV in the world, containing plug-in hybrid-power automobile and pure electric passenger vehicle) overall sales volume will reach 3,200,000, wherein Chinese total sales volume will reach 88.8 ten thousand, and scale ranks first in the world.The U.S. gives priority to plug-in hybrid-power automobile and pure electric passenger vehicle, put into effect research and development and production that measures supports electrokinetic cell, key components and parts, proposition government stock planning and consumer purchase car subsidy policy, support charging infrastructure construction, plan universal 1,000,000 plug-in hybrid electric passenger vehicles in 2015.Development electric passenger vehicle is also the important component part of EU Economy rehabilitation plan, actively development and popularization electric passenger vehicle, estimates that the year two thousand twenty Europe will have 5,000,000 electric passenger vehicles to set out on a journey.Japan is in the development and popularization of electric passenger vehicle, and government subsidizes the consumer buying the environment-friendly vehicles such as pure electric passenger vehicle, plug-in hybrid-power automobile.
In the face of the pressure of the energy and environment, Development of EV, advances low-carbon type traffic, and be the focus of China and world's Main Developed Countries attention from government, various countries all increase the support on policy dynamics to electric automobile.Can estimate to have future a large amount of charging electric vehicle load to access electrical network.But, as transportable load, the unordered charging behavior over time and space of extensive electric automobile not only there will be the phenomenon at electric load " Shang Jia peak, peak ", add bulk power grid peak-valley difference, and likely cause the electrical network local problem such as overload, line congestion, bring impact to the stable operation of electrical network.The scale application of electric passenger vehicle brings new challenge by electrically-charging equipment construction and operation of power networks.Ability that electrical network does not store substantially (except part water-storage), therefore generates electricity and transmits electricity and must manage to mate the fluctuation of power load.Along with Innovation Input constantly increases the breakthrough with gordian technique, following China will enter electric passenger vehicle especially electric passenger vehicle fast development period, after forming the scale application of electric passenger vehicle, this brings new challenge by electrically-charging equipment development and operation of power networks: (1) causes new load growth, increases electrical network peak-valley difference further; (2) produce a large amount of electrically-charging equipment construction demand, requirements at the higher level are proposed to electrical network upgrading and planning construction; (3) charge requirement has randomness and dispersed feature, strengthens power distribution network operational management difficulty.The fluctuation during charging of extensive electric passenger vehicle has obvious impact to power distribution network, and therefore power distribution network can carry out load distribution and restriction to charging system or electrically-charging equipment.Under intelligent grid background, intelligent management is implemented to electric passenger vehicle charging, the adverse effect that electric passenger vehicle charge requirement causes electrical network can be avoided, and improve the operational efficiency of electrical network.Electric passenger vehicle charging controls the assignment constraint needing to consider network load.
Therefore, need a kind of new technical scheme to overcome problems of the prior art.
Summary of the invention
The present invention seeks to according to plug-in electric passenger vehicle operation characteristic prediction electric passenger vehicle charge profile rule, a kind of electric passenger vehicle charge profile Forecasting Methodology is provided, for solving electric passenger vehicle charging disorder distribution problem for charging service operation, electrically-charging equipment construction and distribution network planning, in order effectively to reduce investment outlay, waste provides reference frame with rationalization resource distribution.
For solving the problems of the technologies described above, the present invention is achieved through the following technical solutions:
Based on a charge profile Forecasting Methodology for electric passenger vehicle application characteristic, comprise the following steps:
A, analysis electric passenger vehicle application characteristic, comprise electric passenger vehicle travel behaviour feature; Average daily distance travelled; Travel Applicative time; Stop the duration of charging; Stop charging position; Electric passenger vehicle application share rate;
B, extraction electric passenger vehicle application state proper vector, and set up charge profile influence factor proportional system, total weight is 1;
C, employing particle swarm optimization algorithm are set up electric passenger vehicle application characteristic model, and are solved charging behavior temporal expression and spatial expression according to electric passenger vehicle application characteristic;
D, with its microcosmic Applicative time and space nodes for object, simulate charging behavior macroscopic law characteristic.
Compared with prior art, beneficial effect of the present invention is:
Comprehensive analysis is considered each feature of electric passenger vehicle and provides proportional system according to this each characteristic rule, and the application characteristic model prediction electric passenger vehicle charge profile characteristic by setting up, in order effectively to reduce investment outlay, waste provides reference frame with rationalization resource distribution.
Accompanying drawing explanation
Fig. 1 is electric passenger vehicle application characteristic schematic diagram in the present invention.
Fig. 2 is electric passenger vehicle charge profile influence factor coefficient weights setting principle figure in the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, illustrate the present invention further, these embodiments should be understood only be not used in for illustration of the present invention and limit the scope of the invention, after having read the present invention, the amendment of those skilled in the art to the various equivalent form of value of the present invention has all fallen within the application's claims limited range.
Shown in Fig. 1 and Fig. 2, the present invention discloses a kind of charge profile Forecasting Methodology based on electric passenger vehicle application characteristic, comprises the following steps:
A, analysis electric passenger vehicle application characteristic, comprise electric passenger vehicle travel behaviour feature; Average daily distance travelled; Travel Applicative time; Stop the duration of charging; Stop charging position; Electric passenger vehicle application share rate.
Wherein, each application characteristic is described as follows,
Electric passenger vehicle travel behaviour feature: a kind of index of daily driving passenger car trip number of times with electric passenger vehicle user, can reflect electric passenger vehicle utilization factor, and carry out border to electric passenger vehicle application characteristic;
Average daily distance travelled: in units of kilometer, in statistics monomer electric passenger vehicle one day, trip distance adds up, and reflection electric passenger vehicle user goes on a journey application demand;
Travel Applicative time: by minute in units of, statistics monomer electric passenger vehicle travels the time of sharing and adds up, in order to add up electric passenger vehicle distribution service time in one day;
Stop the duration of charging: by minute in units of, statistics electric passenger vehicle charge period, and consumption during charging, the charging behavior temporal regularity of reflection user;
Stop charging position: classify with belonging to electrically-charging equipment, as residential quarters, shopping centre, Office Area, other Public Parking etc., reflection electric passenger vehicle user charges behavioural habits and charging power load distributing feature.
Electric passenger vehicle application share rate: select the probability of use of electric passenger vehicle trip and user to expect.
B, extraction electric passenger vehicle application state proper vector, and set up charge profile influence factor proportional system, total weight is 1; And concrete, in the present embodiment, weight is divided into following a few class:
Weight (1): user's personal feature, comprising: type of vehicle, comprises classification vehicle in use (as taxi etc.), utility car, private car; Sex character, male user uses passenger car trip rate to be greater than female user; Job characteristics, as full-time working person, unemployed, individual business personnel etc.; This weight coefficient accounts for 0.3-0.6;
Weight (2): electric passenger vehicle trip share rate, namely user adopts electric passenger vehicle trip probability; This accounts for weight coefficient ratio is 0-0.1;
Weight (3): electric passenger vehicle user go on a journey the period distribution; This accounts for weight coefficient ratio is 0-0.2;
Weight (4): family's vehicles owning amount; This accounts for weight coefficient ratio is 0-0.1.
According to behavioural characteristic in steps A, extract electric passenger vehicle application state proper vector in stepb and set concrete weight classification, as shown in table 1, be an example extracted electric passenger vehicle application state proper vector and set concrete weight classification:
Table 1 electric passenger vehicle key application feature interpretation
C, employing particle swarm optimization algorithm are set up electric passenger vehicle application characteristic model, and are solved charging behavior temporal expression and spatial expression according to electric passenger vehicle application characteristic;
In step C, setting up state equation, by individual data items iteration, is one group of RANDOM SOLUTION by system initialization, by iterated search desired value; This state equation is established as:
E＝(Αx+Βy)(λ+ξ+α+β)
Wherein, E is matching bounds internal object value; A is the charging feature matrix for electric passenger vehicle, and x is its variable vector group quantity; B is that electric passenger vehicle uses eigenmatrix, and y is its variable vector group quantity; λ/ξ/α/β is respectively user characteristics weight vectors coefficient;
Through solving the population covariance matrix Σ drawing observational variable, define following expression formula:
Σ＝Σ(ω)
Wherein, ω is the Time and place parameter matrix of forecast assessment, and Σ represents population covariance matrix.
D, with its microcosmic Applicative time and space nodes for object, simulate charging behavior macroscopic law characteristic.
In step D, bring by the electric passenger vehicle application characteristic parameter through simplifying the Time and place value that algorithm computation model can draw charge profile into, by consulting method interval of definition, prediction electric passenger vehicle charge profile characteristic.
Obviously, above-described embodiment is only for clearly example being described, and the restriction not to embodiment.For those of ordinary skill in the field, can also make other changes in different forms on the basis of the above description.Here exhaustive without the need to also giving all embodiments.And thus the apparent change of extending out or variation be still among the protection domain of the invention.
Claims (5)
1., based on a charge profile Forecasting Methodology for electric passenger vehicle application characteristic, it is characterized in that, comprise the following steps:
A, analysis electric passenger vehicle application characteristic, comprise electric passenger vehicle travel behaviour feature; Average daily distance travelled; Travel Applicative time; Stop the duration of charging; Stop charging position; Electric passenger vehicle application share rate;
B, extraction electric passenger vehicle application state proper vector, and set up charge profile influence factor proportional system, total weight is 1;
C, employing particle swarm optimization algorithm are set up electric passenger vehicle application characteristic model, and are solved charging behavior temporal expression and spatial expression according to electric passenger vehicle application characteristic;
D, with its microcosmic Applicative time and space nodes for object, simulate charging behavior macroscopic law characteristic.
2. the charge profile Forecasting Methodology based on electric passenger vehicle application characteristic according to claim 1, is characterized in that, in described steps A,
Electric passenger vehicle travel behaviour feature: a kind of index of daily driving passenger car trip number of times with electric passenger vehicle user, can reflect electric passenger vehicle utilization factor, and carry out border to electric passenger vehicle application characteristic;
Average daily distance travelled: in units of kilometer, in statistics monomer electric passenger vehicle one day, trip distance adds up, and reflection electric passenger vehicle user goes on a journey application demand;
Travel Applicative time: by minute in units of, statistics monomer electric passenger vehicle travels the time of sharing and adds up, in order to add up electric passenger vehicle distribution service time in one day;
Stop the duration of charging: by minute in units of, statistics electric passenger vehicle charge period, and consumption during charging, the charging behavior temporal regularity of reflection user;
Stop charging position: classify with belonging to electrically-charging equipment, reflection electric passenger vehicle user charges behavioural habits and charging power load distributing feature.
Electric passenger vehicle application share rate: select the probability of use of electric passenger vehicle trip and user to expect.
3. the charge profile Forecasting Methodology based on electric passenger vehicle application characteristic according to claim 1, is characterized in that, in step B, weight is divided into following a few class:
Weight (1): user's personal feature, comprising: type of vehicle; Sex character; Job characteristics; This weight coefficient accounts for 0.3-0.6;
Weight (2): electric passenger vehicle trip share rate, namely user adopts electric passenger vehicle trip probability; This accounts for weight coefficient ratio is 0-0.1;
Weight (3): electric passenger vehicle user go on a journey the period distribution; This accounts for weight coefficient ratio is 0-0.2;
Weight (4): family's vehicles owning amount; This accounts for weight coefficient ratio is 0-0.1.
4. the charge profile Forecasting Methodology based on electric passenger vehicle application characteristic according to claim 1, is characterized in that:
In step C, setting up state equation, by individual data items iteration, is one group of RANDOM SOLUTION by system initialization, by iterated search desired value; This state equation is established as:
E＝(Αx+Βy)(λ+ξ+α+β)
Wherein, E is matching bounds internal object value; A is the charging feature matrix for electric passenger vehicle, and x is its variable vector group quantity; B is that electric passenger vehicle uses eigenmatrix, and y is its variable vector group quantity; λ/ξ/α/β is respectively user characteristics weight vectors coefficient;
Through solving the population covariance matrix Σ drawing observational variable, define following expression formula:
Σ＝Σ(ω)
Wherein, ω is the Time and place parameter matrix of forecast assessment, and Σ represents population covariance matrix.
5. the charge profile Forecasting Methodology based on electric passenger vehicle application characteristic according to claim 1, it is characterized in that: in step D, the Time and place value that algorithm computation model can draw charge profile is brought into by the electric passenger vehicle application characteristic parameter through simplifying, by consulting method interval of definition, prediction electric passenger vehicle charge profile characteristic.
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| CN106130110A (en) * | 2016-07-15 | 2016-11-16 | 华北电力大学 | The electric taxi charging station constant volume method on trip ground is selected based on stratified probability |
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| CN108964021A (en) * | 2018-06-25 | 2018-12-07 | 国网陕西省电力公司经济技术研究院 | A kind of control method for the frequency modulation electric car capacity spatial and temporal distributions characteristic that networks |
| CN110163460A (en) * | 2018-03-30 | 2019-08-23 | 腾讯科技（深圳）有限公司 | A kind of method and apparatus determined using score value |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106130110A (en) * | 2016-07-15 | 2016-11-16 | 华北电力大学 | The electric taxi charging station constant volume method on trip ground is selected based on stratified probability |
| CN106130110B (en) * | 2016-07-15 | 2018-12-25 | 华北电力大学 | The electric taxi charging station constant volume method on trip ground is selected based on stratified probability |
| CN106295860A (en) * | 2016-07-29 | 2017-01-04 | 国网山东省电力公司经济技术研究院 | A kind of electric automobile scale charge requirement Forecasting Methodology based on Monte Carlo Analogue Method |
| CN110163460A (en) * | 2018-03-30 | 2019-08-23 | 腾讯科技（深圳）有限公司 | A kind of method and apparatus determined using score value |
| CN110163460B (en) * | 2018-03-30 | 2023-09-19 | 腾讯科技（深圳）有限公司 | Method and equipment for determining application score |
| CN108964021A (en) * | 2018-06-25 | 2018-12-07 | 国网陕西省电力公司经济技术研究院 | A kind of control method for the frequency modulation electric car capacity spatial and temporal distributions characteristic that networks |
| CN108964021B (en) * | 2018-06-25 | 2022-07-01 | 国网陕西省电力公司经济技术研究院 | Method for controlling capacity space-time distribution characteristic of frequency-modulated electric vehicle capable of accessing network |
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