CN107742038A - Charging electric vehicle load forecasting method and device - Google Patents
Charging electric vehicle load forecasting method and device Download PDFInfo
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Abstract
The present invention provides a kind of charging electric vehicle load forecasting method and device, the data such as standard power consumption, charge power, initiation of charge time and daily travel by obtaining all types of electric automobiles, the random initiation of charge moment of all types of electric automobiles obtained with reference to emulation and random daily travel, duration needed for the charging of all types of electric automobiles is further obtained, to obtain the charging load prediction curve of all types of electric automobiles.By the charging load prediction curve for being superimposed all types of electric automobiles, so that the obtained charging total prediction curve of load is capable of the charge characteristic of all types of electric automobiles of concentrated expression, the charging load for obtaining prediction is more bonded the actual use situation of all types of electric automobiles and more accurate.
Description
Technical field
The present invention relates to electric automobile field of intelligent control technology, more particularly to a kind of charging electric vehicle load prediction
Method and device.
Background technology
The excess emissions of greenhouse gases, global warming trend is caused to be aggravated.Traffic of the electric automobile as a new generation
Instrument, possesses the incomparable advantage of orthodox car to the relying party face of traditional fossil energy in energy-saving and emission-reduction, the reduction mankind.With
The popularization of following electric automobile, electric automobile accesses grid charging on a large scale, and the operation of power system and planning will be produced
Very important influence.
By the research to charging electric vehicle load forecasting method, further can be sent out on a large scale for research electric automobile
The influence to power network is opened up, research electric automobile participates in power network interaction capability, so as to the collaborative planning for electrically-charging equipment and power distribution network
Establish data basis.
However, traditional charging load forecasting method does not account for electric automobile difference charge characteristic to charging load
Influence, can not Accurate Prediction electric automobile charging load.
The content of the invention
Based on this, it is necessary to exist for traditional charging load forecasting method and do not account for the different chargings of electric automobile
Characteristic to charge load influence, can not Accurate Prediction electric automobile charging load the defects of, there is provided a kind of electric automobile fills
Electric load Forecasting Methodology and device.
Technical scheme provided by the present invention is as follows：
A kind of charging electric vehicle load forecasting method, including step：
Obtain standard power consumption, charge power, initiation of charge time and the daily travel of all types of electric automobiles.
The initiation of charge time model of all types of electric automobiles is established according to the initiation of charge time.
The daily travel model of all types of electric automobiles is established according to daily travel.
Initiation of charge time model is emulated, extracts the random initiation of charge moment in initiation of charge time model.
Daily travel model is emulated, extracts the random daily travel in daily travel model.
The charging that all types of electric automobiles are obtained with reference to random daily travel, standard power consumption and charge power is taken
It is long.
The charging that duration and charge power with reference to needed for random initiation of charge moment, charging obtain all types of electric automobiles is born
Lotus prediction curve.
The charging load prediction curve of all types of electric automobiles is superimposed, obtains the total prediction curve of load that charges.
A kind of charging electric vehicle load prediction device, including：
Data acquisition module, for obtaining the standard power consumption, charge power, initiation of charge time of all types of electric automobiles
And daily travel.
Initiation of charge time model establishes module, for establishing the starting of all types of electric automobiles according to the initiation of charge time
Charging interval model.
Daily travel model building module, in the day for establishing all types of electric automobiles according to daily travel travels
Journey model.
First emulation module, for being emulated to initiation of charge time model, extract in initiation of charge time model
The random initiation of charge moment.
Second emulation module, for being emulated to daily travel model, extract random in daily travel model
Daily travel.
Duration needed for charging obtains module, is obtained for combining random daily travel, standard power consumption and charge power
Duration needed for the charging of all types of electric automobiles.
Charging load prediction curve establishes module, for duration and charging with reference to needed for random initiation of charge moment, charging
Power obtains the charging load prediction curve of all types of electric automobiles.
The charging total prediction curve of load establishes module, for being superimposed the charging load prediction curve of all types of electric automobiles,
Obtain the total prediction curve of load that charges.
A kind of computer equipment, including memory, processor and storage can be run on a memory and on a processor
Computer program, the step of realizing charging electric vehicle load forecasting method during computing device computer program program.
A kind of computer-readable recording medium, computer program is stored thereon with, when computer program is executed by processor
The step of realizing charging electric vehicle load forecasting method.
Technical scheme provided by the present invention, by obtaining the standard power consumption of all types of electric automobiles, charge power, rising
Begin the data such as charging interval and daily travel, the random initiation of charge moment of all types of electric automobiles obtained with reference to emulation and
Random daily travel, duration needed for the charging of all types of electric automobiles is further obtained, to obtain all types of electric automobiles
Charge load prediction curve.By being superimposed the charging load prediction curve of all types of electric automobiles, so that obtained charging load
Total prediction curve is capable of the charge characteristic of all types of electric automobiles of concentrated expression, and the charging load for obtaining prediction is more bonded all kinds of
The actual use situation of type electric automobile and more accurate.
Brief description of the drawings
Fig. 1 is the charging electric vehicle load forecasting method flow chart of embodiment one；
Fig. 2 is charging load prediction curve schematic diagram
Fig. 3 is the charging electric vehicle load forecasting method flow chart of embodiment two；
Fig. 4 is the charging electric vehicle load forecasting method course diagram of embodiment three；
Fig. 5 is the charging electric vehicle load forecasting method course diagram of example IV；
Fig. 6 is the charging electric vehicle load forecasting method course diagram of embodiment five；
Fig. 7 is the charging electric vehicle load prediction apparatus module figure of embodiment six；
Fig. 8 is the charging electric vehicle load prediction apparatus module figure of embodiment seven；
Fig. 9 is the charging electric vehicle load prediction apparatus module figure of embodiment eight；
Figure 10 is the charging electric vehicle load prediction apparatus module figure of embodiment nine；
Figure 11 is the charging electric vehicle load prediction apparatus module figure of embodiment ten.
Embodiment
Purpose, technical scheme and technique effect for a better understanding of the present invention, below in conjunction with drawings and examples
Further explaining illustration is carried out to the present invention.State simultaneously, embodiments described below is only used for explaining the present invention, not
For limiting the present invention.
In embodiment one, as shown in figure 1, be the charging electric vehicle load forecasting method flow chart of embodiment one, bag
Include step：
S101, obtain in the standard power consumption of all types of electric automobiles, charge power, initiation of charge time and day traveling
Journey.
Wherein, all types of electric automobiles can include a variety of different electric automobiles, be specifically including but not limited to electronic
Bus, electronic special-purpose vehicle, electronic private car, electric taxi and electronic shared automobile etc..Wherein, different types of electronic vapour
The charge characteristic of car is different, i.e., standard power consumption, charge power, initiation of charge time and the day row of different types of electric automobile
It is different to sail mileage.
Based on this, in step S101, the standard power consumption, charge power, initiation of charge of all types of electric automobiles are obtained
Time and daily travel, it is the standard power consumption, charge power, initiation of charge of the electric automobile for obtaining each type respectively
Time and daily travel.
S102, the initiation of charge time model of all types of electric automobiles is established according to the initiation of charge time.
Wherein, the type electric automobile is established according to the initiation of charge time history data of different types of electric automobile
Initiation of charge time model, the corresponding initiation of charge time model of electric automobile of each type.
S103, the daily travel model of all types of electric automobiles is established according to daily travel.
Wherein, the day of the type electric automobile is established according to the daily travel historical data of different types of electric automobile
Distance travelled model, the corresponding daily travel model of electric automobile of each type.
S104, initiation of charge time model is emulated, extract the random initiation of charge in initiation of charge time model
Moment.
Wherein, before emulation is started, the electric automobile type currently emulated is first determined, according to the determination pair of electric automobile type
The initiation of charge time model answered is emulated, and extracts the random initiation of charge moment in the initiation of charge time model.
Wherein, the method emulated to initiation of charge time model can use method of random sampling, include but is not limited to
Monte Carlo method and field test method.Preferably, in the present embodiment, emulated from Monte Carlo method, it is random to improve
Several degrees of convergence, it is easy to the foundation of follow-up charging load prediction curve.
S105, daily travel model is emulated, extract the random daily travel in daily travel model.
Wherein, before emulation is started, the electric automobile type currently emulated is first determined, according to the determination pair of electric automobile type
The daily travel model answered is emulated, and extracts the random daily travel in the daily travel model.
Wherein, the method emulated to initiation of charge time model can use method of random sampling, include but is not limited to
Monte Carlo method and field test method.Preferably, in the present embodiment, emulated from Monte Carlo method, it is random to improve
Several degrees of convergence, it is easy to the foundation of follow-up charging load prediction curve.
S106, the charging of all types of electric automobiles is obtained with reference to random daily travel, standard power consumption and charge power
Required duration.
Wherein, the charging of all types of electric automobiles is obtained with reference to random daily travel, standard power consumption and charge power
The process of required duration, such as following formula：
Wherein, t represents duration needed for charging, and E represents standard power consumption；ChRepresent the commutation factor of driving habits；D is represented
Random daily travel, ηmRepresent the charger charge efficiency of all types of electric automobiles；ηnRepresent the lithium electricity of all types of electric automobiles
Pond charge efficiency；P represents charge power.
S107, duration and charge power obtain all types of electric automobiles with reference to needed for random initiation of charge moment, charging
Charge load prediction curve.
Wherein, charge load prediction curve foundation as shown in Fig. 2 for charging load prediction curve schematic diagram, wherein, figure
The x-axis of charging load prediction curve is time shaft in 2, and 0-x direction is the developing direction of time, and y-axis is big for charge power
It is small.By taking the charging load prediction profile A in Fig. 2 as an example, the x-axis time corresponding to point a is the signal random initiation of charge moment,
The x-axis period corresponding to curve a-b is to fill for duration, y-axis size corresponding to curve a-b needed for duration signal charging needed for charging
Charge power change in electric load prediction profile A.
S108, the charging load prediction curve of all types of electric automobiles is superimposed, obtains the total prediction curve of load that charges.
The a plurality of charging load prediction curve obtained in step s 107, is superimposed a plurality of charging load prediction curve, goes forward side by side
Row planization, obtain a charging total prediction curve of load.
The technical scheme that the present embodiment is provided, by obtain the standard power consumption of all types of electric automobiles, charge power,
The data such as initiation of charge time and daily travel, the random initiation of charge moment of all types of electric automobiles obtained with reference to emulation
With random daily travel, duration needed for the charging of all types of electric automobiles is further obtained, to obtain all types of electric automobiles
Charging load prediction curve.By being superimposed the charging load prediction curve of all types of electric automobiles, so that obtained charging is born
The total prediction curve of lotus is capable of the charge characteristic of all types of electric automobiles of concentrated expression, and the charging load for obtaining prediction is more bonded respectively
The actual use situation of type electric automobile and more accurate.
In embodiment two, as shown in figure 3, be the charging electric vehicle load forecasting method flow chart of embodiment two, bag
Include step：
S201, obtain in the standard power consumption of all types of electric automobiles, charge power, initiation of charge time and day traveling
Journey.
S202, obtain the historical data of the initiation of charge time of all types of electric automobiles.
S203, it is by the method for Maximum-likelihood estimation that the historical data of the initiation of charge time of all types of electric automobiles is near
Like being normal distribution, initiation of charge time model is obtained.
The initiation of charge time is mainly influenceed by the traveling demand and trip custom of automobile user.To identified electronic
Car category, the starting that the type electric automobile is obtained according to the historical data of the initiation of charge time of the type electric automobile are filled
The mathematical distribution of electric time, the probability-distribution function of the initiation of charge time of the type electric automobile is drawn, as the type electricity
The initiation of charge time model of electrical automobile.
Wherein, the initiation of charge time of electric bus, electronic special-purpose vehicle, electronic private car and electric taxi meets just
State is distributed：
Wherein, x represents electric automobile initiation of charge time, μtRepresent the average of initiation of charge time Normal Distribution, σ
Represent the standard deviation of initiation of charge time Normal Distribution, fxRepresent the probability density function of initiation of charge time.It should be noted
The f of different type electric automobilexCorresponding average, standard deviation are different.
Wherein, because the use of electronic shared automobile is than more random, the presentation of its initiation of charge time is uniformly distributed：
Wherein, minimum value, the maximum of a, b electronic shared automobile initiation of charge time among representing respectively one day, fxTable
Show the probability density function of electronic shared automobile initiation of charge time.
According to the probability-distribution function of the initiation of charge time of the type electric automobile rising to selected electric automobile
Begin charging interval progress stochastical sampling, so as to obtain the random initiation of charge moment of selected electric automobile.
S204, the daily travel model of all types of electric automobiles is established according to daily travel.
S205, initiation of charge time model is emulated, extract the random initiation of charge in initiation of charge time model
Moment.
S206, daily travel model is emulated, extract the random daily travel in daily travel model.
S207, the charging of all types of electric automobiles is obtained with reference to random daily travel, standard power consumption and charge power
Required duration.
S208, duration and charge power obtain all types of electric automobiles with reference to needed for random initiation of charge moment, charging
Charge load prediction curve.
S209, the charging load prediction curve of all types of electric automobiles is superimposed, obtains the total prediction curve of load that charges.
The technical scheme that the present embodiment is provided, by the method for Maximum-likelihood estimation by the starting of all types of electric automobiles
The historical data in charging interval is approximately normal distribution, obtains initiation of charge time model, makes the initiation of charge time mould of foundation
Type with the initiation of charge time situation of all types of electric automobiles of concentrated expression, can improve the referential of subsequent simulation.
In embodiment three, as shown in figure 4, be the charging electric vehicle load forecasting method course diagram of embodiment three, bag
Include step：
S301, obtain in the standard power consumption of all types of electric automobiles, charge power, initiation of charge time and day traveling
Journey.
S302, the initiation of charge time model of all types of electric automobiles is established according to the initiation of charge time.
S303, obtain the historical data of the daily travel of all types of electric automobiles.
S304, it is by the method for Maximum-likelihood estimation that the historical data of the daily travel of all types of electric automobiles is approximate
For logarithm normal distribution, daily travel model is obtained.
Extract the size for being to determine charging load of daily travel.To the clear and definite type of identified electric automobile,
The mathematics of the daily travel of the type electric automobile is obtained according to the historical data of the daily travel of the type electric automobile
Distribution, the probability-distribution function of the daily travel of the type electric automobile is drawn, the day as the type electric automobile travels
Mileage model.
Wherein, the daily travel of electric bus, electronic special-purpose vehicle, electronic private car and electric taxi meets logarithm
Normal distribution, its probability density function are：
Wherein, d represents electric automobile daily travel, and μ represents that daily travel obeys the average of logarithm normal distribution, σ
Represent that daily travel obeys the standard deviation of logarithm normal distribution, fdRepresent the probability density function of daily travel.It should be noted
The f of different type electric automobiledCorresponding average, standard deviation are different.
Wherein, because the use of electronic shared automobile is than more random, its daily travel is presented and is uniformly distributed：
Wherein, minimum value, the maximum of n, m electronic shared automobile daily travel among representing respectively one day, fdRepresent
The probability density function of electronic shared automobile initiation of charge time.
Day row according to the probability-distribution function of the daily travel of the type electric automobile to identified electric automobile
Sail mileage and carry out stochastical sampling, so as to obtain the random daily travel of identified electric automobile.
S305, initiation of charge time model is emulated, extract the random initiation of charge in initiation of charge time model
Moment.
S306, daily travel model is emulated, extract the random daily travel in daily travel model.
S307, the charging of all types of electric automobiles is obtained with reference to random daily travel, standard power consumption and charge power
Required duration.
S308, duration and charge power obtain all types of electric automobiles with reference to needed for random initiation of charge moment, charging
Charge load prediction curve.
S309, the charging load prediction curve of all types of electric automobiles is superimposed, obtains the total prediction curve of load that charges.
The technical scheme that the present embodiment is provided, by the method for Maximum-likelihood estimation by the day row of all types of electric automobiles
The historical data for sailing mileage is approximately logarithm normal distribution, obtains daily travel model, makes the daily travel model of foundation
With the daily travel situation of all types of electric automobiles of concentrated expression, the referential of subsequent simulation can be improved.
In example IV, as shown in figure 5, be the charging electric vehicle load forecasting method course diagram of example IV, bag
Include step：
S401, obtain in the standard power consumption of all types of electric automobiles, charge power, initiation of charge time and day traveling
Journey.
S402, the initiation of charge time model of all types of electric automobiles is established according to the initiation of charge time.
S403, the daily travel model of all types of electric automobiles is established according to daily travel.
S404, the Monte Carlo simulation more than default simulation times is carried out to initiation of charge time model.
S405, if simulation result is restrained, obtain the convergent random initiation of charge moment.
The convergent process of simulation result, such as following formula：
Wherein, βiFor ith emulate the moment charging load variance coefficient, i=1,2 ..., 1440；For i-th
The charging load variance at secondary emulation moment；The charging load desired value at moment is emulated for ith；When being emulated for ith
The charging load criterion at quarter is poor；M is default simulation times；
If max (βi) ＜ 0.05%, then simulation result convergence.
S406, daily travel model is emulated, extract the random daily travel in daily travel model.
S407, the charging of all types of electric automobiles is obtained with reference to random daily travel, standard power consumption and charge power
Required duration.
S408, duration and charge power obtain all types of electric automobiles with reference to needed for random initiation of charge moment, charging
Charge load prediction curve.
S409, the charging load prediction curve of all types of electric automobiles is superimposed, obtains the total prediction curve of load that charges.
The technical scheme that the present embodiment is provided, initiation of charge time model is emulated by Monte Carlo method, taken out
Take the random initiation of charge moment.Based on this, the referential of random initiation of charge time data is improved, to improve charging load prediction
Accuracy rate.
In embodiment five, as shown in fig. 6, be the charging electric vehicle load forecasting method course diagram of embodiment five, bag
Include step：
S501, obtain in the standard power consumption of all types of electric automobiles, charge power, initiation of charge time and day traveling
Journey.
S502, the initiation of charge time model of all types of electric automobiles is established according to the initiation of charge time.
S503, the daily travel model of all types of electric automobiles is established according to daily travel.
S504, initiation of charge time model is emulated, extract the random initiation of charge in initiation of charge time model
Moment.
S505, the Monte Carlo simulation more than default simulation times is carried out to daily travel model.
S506, if simulation result is restrained, obtain convergent random daily travel.
The convergent process of simulation result, such as following formula：
Wherein, βiFor ith emulate the moment charging load variance coefficient, i=1,2 ..., 1440；For i-th
The charging load variance at secondary emulation moment；The charging load desired value at moment is emulated for ith；When being emulated for ith
The charging load criterion at quarter is poor；M is default simulation times；
If max (βi) ＜ 0.05%, then simulation result convergence.
S507, the charging of all types of electric automobiles is obtained with reference to random daily travel, standard power consumption and charge power
Required duration.
S508, duration and charge power obtain all types of electric automobiles with reference to needed for random initiation of charge moment, charging
Charge load prediction curve.
S509, the charging load prediction curve of all types of electric automobiles is superimposed, obtains the total prediction curve of load that charges.
The technical scheme that the present embodiment is provided, daily travel model is emulated by Monte Carlo method, extracted
Random daily travel.Based on this, the referentials of random daily travel data is improved, to improve the accurate of load prediction of charging
Rate.
In embodiment six, as shown in fig. 7, be the charging electric vehicle load prediction apparatus module figure of embodiment six, bag
Include：
Data acquisition module 601, for obtaining the standard power consumption, charge power, initiation of charge of all types of electric automobiles
Time and daily travel.
Initiation of charge time model establishes module 602, for establishing all types of electric automobiles according to the initiation of charge time
Initiation of charge time model.
Daily travel model building module 603, for establishing the day row of all types of electric automobiles according to daily travel
Sail mileage model.
First emulation module 604, for being emulated to initiation of charge time model, extract in initiation of charge time model
The random initiation of charge moment.
Second emulation module 605, for being emulated to daily travel model, extract daily travel model in
Machine daily travel.
Duration needed for charging obtains module 606, is obtained for combining random daily travel, standard power consumption and charge power
To duration needed for the charging of all types of electric automobiles.
Charging load prediction curve establishes module 607, for the duration with reference to needed for random initiation of charge moment, charging and fills
Electrical power obtains the charging load prediction curve of all types of electric automobiles.
The charging total prediction curve of load establishes module 608, and the charging load prediction for being superimposed all types of electric automobiles is bent
Line, obtain the total prediction curve of load that charges.
The technical scheme that the present embodiment is provided, by obtain the standard power consumption of all types of electric automobiles, charge power,
The data such as initiation of charge time and daily travel, the random initiation of charge moment of all types of electric automobiles obtained with reference to emulation
With random daily travel, duration needed for the charging of all types of electric automobiles is further obtained, to obtain all types of electric automobiles
Charging load prediction curve.By being superimposed the charging load prediction curve of all types of electric automobiles, so that obtained charging is born
The total prediction curve of lotus is capable of the charge characteristic of all types of electric automobiles of concentrated expression, and the charging load for obtaining prediction is more bonded respectively
The actual use situation of type electric automobile and more accurate.
Wherein, as shown in figure 8, charging electric vehicle load prediction apparatus module figure for embodiment seven, during initiation of charge
Between model building module 602 also include：
First historical data acquisition module 701, the history number of the initiation of charge time for obtaining all types of electric automobiles
According to.
First model building module 702, by the method for Maximum-likelihood estimation by the initiation of charge of all types of electric automobiles
The historical data of time is approximately normal distribution, obtains initiation of charge time model.
The technical scheme that the present embodiment is provided, by the method for Maximum-likelihood estimation by the starting of all types of electric automobiles
The historical data in charging interval is approximately normal distribution, obtains initiation of charge time model, makes the initiation of charge time mould of foundation
Type with the initiation of charge time situation of all types of electric automobiles of concentrated expression, can improve the referential of subsequent simulation.
Wherein, as shown in figure 9, charging electric vehicle load prediction apparatus module figure for embodiment eight, daily travel
Model building module 603 includes：
Second historical data acquisition module 801, the historical data of the daily travel for obtaining all types of electric automobiles.
Second model building module 802, for the method by Maximum-likelihood estimation by the day row of all types of electric automobiles
The historical data for sailing mileage is approximately logarithm normal distribution, obtains daily travel model.
The technical scheme that the present embodiment is provided, by the method for Maximum-likelihood estimation by the day row of all types of electric automobiles
The historical data for sailing mileage is approximately logarithm normal distribution, obtains daily travel model, makes the daily travel model of foundation
With the daily travel situation of all types of electric automobiles of concentrated expression, the referential of subsequent simulation can be improved.
Wherein, as shown in Figure 10, it is the charging electric vehicle load prediction apparatus module figure of embodiment nine, first emulates mould
Block 604 includes：
First emulates module 901, special more than the illiteracy of default simulation times for being carried out to initiation of charge time model
Carlow emulates；
First simulation result acquisition module 902, for when simulation result is restrained, when obtaining convergent random initiation of charge
Carve.
The technical scheme that the present embodiment is provided, initiation of charge time model is emulated by Monte Carlo method, taken out
Take the random initiation of charge moment.Based on this, the referential of random initiation of charge time data is improved, to improve charging load prediction
Accuracy rate.
Wherein, as shown in figure 11, it is the charging electric vehicle load prediction apparatus module figure of embodiment ten, second emulates mould
Block 605 includes：
Second emulates module 1001, for carrying out the Meng Teka more than default simulation times to daily travel model
Lip river emulates；
Second simulation result acquisition module 1002, for when simulation result is restrained, obtaining in convergent random day traveling
Journey.
The technical scheme that the present embodiment is provided, daily travel model is emulated by Monte Carlo method, extracted
Random daily travel.Based on this, the referentials of random daily travel data is improved, to improve the accurate of load prediction of charging
Rate.
The present invention also provides a kind of computer equipment, including memory, processor and storage on a memory and can located
The computer program that runs on reason device, above-mentioned charging electric vehicle load prediction is realized during computing device computer program program
In method the step of any one embodiment.
The computer equipment that the present embodiment is provided, by the standard power consumption, the charging work(that obtain all types of electric automobiles
The data such as rate, initiation of charge time and daily travel, the random initiation of charge of all types of electric automobiles obtained with reference to emulation
Moment and random daily travel, further obtain duration needed for the charging of all types of electric automobiles, all types of electronic to obtain
The charging load prediction curve of automobile.By being superimposed the charging load prediction curve of all types of electric automobiles, so that what is obtained fills
The total prediction curve of electric load is capable of the charge characteristic of all types of electric automobiles of concentrated expression, the charging load that prediction obtains more is pasted
Close the actual use situation of all types of electric automobiles and more accurate.
The present invention also provides a kind of computer-readable recording medium, is stored thereon with computer program, computer program quilt
The step of any one embodiment in above-mentioned charging electric vehicle load forecasting method is realized during computing device.In addition, generally
The program being stored in a storage medium by program by directly reading out storage medium or by the way that program is installed or again
Make in the storage device (such as hard disk and/or internal memory) of data processing equipment and perform.Therefore, such storage medium also constitutes
The present invention.Storage medium can use any kind of recording mode, such as paper storage medium (such as paper tape), magnetic storage to be situated between
Matter (such as floppy disk, hard disk, flash memory), optical storage media (such as CD-ROM), magnetic-optical storage medium (such as MO) etc..
The computer-readable recording medium that the present embodiment is provided, by the standard power consumption for obtaining all types of electric automobiles
The data such as amount, charge power, initiation of charge time and daily travel, with reference to emulation obtain all types of electric automobiles it is random
Initiation of charge moment and random daily travel, further obtain duration needed for the charging of all types of electric automobiles, each to obtain
The charging load prediction curve of type electric automobile.By being superimposed the charging load prediction curve of all types of electric automobiles, so that
The obtained charging total prediction curve of load is capable of the charge characteristic of all types of electric automobiles of concentrated expression, the charging for obtaining prediction
Load is more bonded the actual use situation of all types of electric automobiles and more accurate.
Each technical characteristic of above example can be combined arbitrarily, to make description succinct, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, lance is not present in the combination of these technical characteristics
Shield, all it is considered to be the scope of this specification record.
Above example only expresses the several embodiments of the present invention, and its description is more specific and detailed, but can not
Therefore it is construed as limiting the scope of the patent.It should be pointed out that for the person of ordinary skill of the art,
On the premise of not departing from present inventive concept, various modifications and improvements can be made, these belong to protection scope of the present invention.
Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (10)
1. a kind of charging electric vehicle load forecasting method, it is characterised in that including step：
Obtain standard power consumption, charge power, initiation of charge time and the daily travel of all types of electric automobiles；
The initiation of charge time model of all types of electric automobiles is established according to the initiation of charge time；
The daily travel model of all types of electric automobiles is established according to the daily travel；
The initiation of charge time model is emulated, when extracting the random initiation of charge in the initiation of charge time model
Carve；
The daily travel model is emulated, extracts the random daily travel in the daily travel model；
Filling for all types of electric automobiles is obtained with reference to random daily travel, the standard power consumption and the charge power
Duration needed for electricity；
Duration and the charge power obtain all types of electric automobiles with reference to needed for the random initiation of charge moment, the charging
Charging load prediction curve；
The charging load prediction curve of all types of electric automobiles is superimposed, obtains the total prediction curve of load that charges.
2. charging electric vehicle load forecasting method according to claim 1, it is characterised in that described according to the starting
Charging interval establishes the process of the initiation of charge time model of all types of electric automobiles, including step：
Obtain the historical data of the initiation of charge time of all types of electric automobiles；
By the historical data of the initiation of charge time of all types of electric automobiles it is approximately normal state by the method for Maximum-likelihood estimation
Distribution, obtains the initiation of charge time model.
3. charging electric vehicle load forecasting method according to claim 1, it is characterised in that described according to the day row
Sail the process that mileage establishes the daily travel model of all types of electric automobiles, including step：
Obtain the historical data of the daily travel of all types of electric automobiles；
By the method for Maximum-likelihood estimation by the historical data of the daily travel of all types of electric automobiles be approximately logarithm just
State is distributed, and obtains the daily travel model.
4. charging electric vehicle load forecasting method according to claim 1, it is characterised in that described to be filled to the starting
Electric time model is emulated, and extracts the process at the random initiation of charge moment in the initiation of charge time model, including step
Suddenly：
The Monte Carlo simulation more than default simulation times is carried out to the initiation of charge time model；
If the simulation result convergence, obtain the convergent random initiation of charge moment.
5. charging electric vehicle load forecasting method according to claim 1, it is characterised in that described to be travelled to the day
Mileage model is emulated, and extracts the process of the random daily travel in the daily travel model, including step：
The Monte Carlo simulation more than default simulation times is carried out to the daily travel model；
If the simulation result convergence, obtains convergent random daily travel.
6. the charging electric vehicle load forecasting method according to claim 4 or 5, it is characterised in that described to judge emulation
As a result convergent process, such as following formula：
<mrow>
<msub>
<mi>&beta;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<msqrt>
<mrow>
<msub>
<mi>V</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mover>
<mi>L</mi>
<mo>&OverBar;</mo>
</mover>
<mo>)</mo>
</mrow>
</mrow>
</msqrt>
<mrow>
<msqrt>
<mi>M</mi>
</msqrt>
<msub>
<mover>
<mi>L</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>&sigma;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mover>
<mi>L</mi>
<mo>&OverBar;</mo>
</mover>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msqrt>
<mi>M</mi>
</msqrt>
<msub>
<mover>
<mi>L</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
</mrow>
Wherein, βiFor ith emulate the moment charging load variance coefficient, i=1,2 ..., 1440；Imitated for ith
The charging load variance at true moment；The charging load desired value at moment is emulated for ith；The moment is emulated for ith
The load criterion that charges is poor；M is default simulation times；
If max (βi) ＜ 0.05%, then simulation result convergence.
7. charging electric vehicle load forecasting method according to claim 1, it is characterised in that described random with reference to described in
Daily travel, the standard power consumption and the charge power obtain the mistake of duration needed for the charging of all types of electric automobiles
Journey, such as following formula：
<mrow>
<mi>t</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mi>E</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>+</mo>
<msub>
<mi>C</mi>
<mi>h</mi>
</msub>
<mo>)</mo>
</mrow>
<mi>d</mi>
</mrow>
<mrow>
<msub>
<mi>&eta;</mi>
<mi>m</mi>
</msub>
<msub>
<mi>&eta;</mi>
<mi>n</mi>
</msub>
<mi>P</mi>
</mrow>
</mfrac>
</mrow>
Wherein, t represents duration needed for the charging, and E represents the standard power consumption；ChRepresent the commutation factor of driving habits；d
Represent the random daily travel, ηmRepresent the charger charge efficiency of electric automobile；ηnRepresent that the lithium battery of electric automobile fills
Electrical efficiency；P represents the charge power.
A kind of 8. charging electric vehicle load prediction device, it is characterised in that including：
Data acquisition module, for obtaining standard power consumption, charge power, initiation of charge time and the day of all types of electric automobiles
Distance travelled；
Initiation of charge time model establishes module, for establishing the starting of all types of electric automobiles according to the initiation of charge time
Charging interval model；
Daily travel model building module, in the day for establishing all types of electric automobiles according to the daily travel travels
Journey model；
First emulation module, for being emulated to the initiation of charge time model, extract the initiation of charge time model
In the random initiation of charge moment；
Second emulation module, for being emulated to the daily travel model, extract in the daily travel model
Random daily travel；
Duration needed for charging obtains module, for reference to random daily travel, the standard power consumption and the charging
Power obtains duration needed for the charging of all types of electric automobiles；
Charging load prediction curve establishes module, for the duration with reference to needed for the random initiation of charge moment, the charging and
The charge power obtains the charging load prediction curve of all types of electric automobiles；
The charging total prediction curve of load establishes module, for being superimposed the charging load prediction curve of all types of electric automobiles, obtains
Charge the total prediction curve of load.
9. a kind of computer equipment, including memory, processor and storage are on a memory and the meter that can run on a processor
Calculation machine program, it is characterised in that realize that claim 1 to 7 is any one during computer program program described in the computing device
The step of item charging electric vehicle load forecasting method.
10. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the computer program
The step of charging electric vehicle load forecasting method described in claim 1 to 7 any one is realized when being executed by processor.
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| CN108876052A (en) * | 2018-06-28 | 2018-11-23 | 中国南方电网有限责任公司 | Electric car charging load forecasting method, device and computer equipment |
| CN108846589A (en) * | 2018-07-06 | 2018-11-20 | 中国南方电网有限责任公司 | Power grid distribution planning method, apparatus, computer equipment and storage medium |
| CN109034498A (en) * | 2018-08-31 | 2018-12-18 | 国网上海市电力公司 | Consider the electric car charging load forecasting method of user's charge frequency and charge power variation |
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| CN109460853A (en) * | 2018-09-29 | 2019-03-12 | 中国电力科学研究院有限公司 | A kind of electric car charging workload demand determines method and system |
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| CN110533222A (en) * | 2019-07-29 | 2019-12-03 | 国网河南省电力公司经济技术研究院 | Electric car charging load forecasting method and device based on peak Pinggu electricity price |
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| CN111591154A (en) * | 2020-06-10 | 2020-08-28 | 湖南文理学院 | Dynamic magnetic coupling resonant array method and system supporting wireless charging of electric automobile |
| CN112613682A (en) * | 2020-12-29 | 2021-04-06 | 国网江苏省电力有限公司宜兴市供电分公司 | Electric vehicle charging load prediction method |
| CN113298298A (en) * | 2021-05-10 | 2021-08-24 | 国核电力规划设计研究院有限公司 | Charging pile short-term load prediction method and system |
| CN113298298B (en) * | 2021-05-10 | 2023-12-29 | 国核电力规划设计研究院有限公司 | Short-term load prediction method and system for charging pile |
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