CN106127339A - Charging electric vehicle load forecasting method based on probabilistic model and device - Google Patents
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Abstract
The invention provides a kind of charging electric vehicle load forecasting method based on probabilistic model and device.Wherein, the method includes: the initiation of charge moment of the electric automobile in statistics preset number of days and initiation of charge moment battery charge state；It is divided into multiple statistical time range by one day, first distribution characteristics in the initiation of charge moment that use normal function or log-normal function are respectively described on each statistical time range, the second distribution characteristics of the initiation of charge moment battery charge state that use normal function or log-normal function are respectively described on each statistical time range；Need the quantity of the electric motor car of charging according to the first distribution characteristics and the second distribution characteristics and each moment, calculate the power demand upper limit and/or the lower limit of each moment charging electric vehicle.By the present invention, the problem solving the medium-and long-term forecasting of charging electric vehicle load, it is achieved that the medium-and long-term forecasting of charging electric vehicle load.
Description
Technical field
The present invention relates to data processing field, bear in particular to a kind of charging electric vehicle based on probabilistic model
Lotus Forecasting Methodology and device.
Background technology
In the case of Development of Electric Vehicles to certain market scale, charging electric vehicle loading effects factor can be summarized
It is four aspects, is that electric automobile kind and quantity, electric automobile during traveling characteristic, electric automobile user's charging row are for practising respectively
Used, electrically-charging equipment type and layout of buildings.The prediction of charging electric vehicle load contributes to instructing electric automobile to avoid the peak hour charging, and
Foundation is provided for distribution network planning and charging electric vehicle spatial load forecasting.
In correlation technique, the charging load prediction of electric automobile typically uses load prediction based on gray theory or base
Load prediction in neutral net.Research finds, both modes have preferably performance to short-term load forecasting, but inapplicable
In Mid-long term load forecasting.
For being applicable to the medium-and long-term forecasting of charging electric vehicle load, correlation technique not yet provides effective solution party
Case.
Summary of the invention
The invention provides a kind of charging electric vehicle load forecasting method based on probabilistic model and device, at least to solve
The certainly problem of the medium-and long-term forecasting of charging electric vehicle load in correlation technique.
According to an aspect of the invention, it is provided a kind of charging electric vehicle load prediction side based on probabilistic model
Method, including: the initiation of charge moment of the electric automobile in statistics preset number of days and initiation of charge moment battery charge state；By one
It is divided into multiple statistical time range, and it is described that use normal function or log-normal function are respectively described on each statistical time range
First distribution characteristics in initiation of charge moment, uses normal function or log-normal function to be respectively described on each statistical time range
The second distribution characteristics of described initiation of charge moment battery charge state；According to described first distribution characteristics and described second point
Cloth feature and each moment need the quantity of the electric motor car of charging, calculate on the power demand of each moment charging electric vehicle
Limit and/or lower limit.
Alternatively, described the initiateing using normal function or log-normal function to be respectively described on each statistical time range is filled
First distribution characteristics in electricity moment includes: the described initiation of charge moment is carried out normal distribution-test；Lead in normal distribution-test
In the case of crossing, when using normal function or log-normal function to be respectively described the described initiation of charge on each statistical time range
Described first distribution characteristics carved.
Alternatively, described the initiateing using normal function or log-normal function to be respectively described on each statistical time range is filled
Second distribution characteristics of electricity moment battery charge state includes: described initiation of charge moment battery charge state is carried out normal state and divides
Cloth is checked；In the case of normal distribution-test is passed through, normal function or log-normal function is used to be respectively described each system
Described second distribution characteristics of the described initiation of charge moment battery charge state in timing section.
Alternatively, first distribution in the described initiation of charge moment that use normal function is respectively described on each statistical time range
Feature includes: described first distribution characteristics in the described initiation of charge moment on each statistical time range meets probability-distribution function:
Wherein, μsFor the average in this statistical time range initiation of charge moment, σsFor
The standard deviation in this statistical time range initiation of charge moment.
Alternatively, the first of the described initiation of charge moment that use log-normal function is respectively described on each statistical time range
Distribution characteristics includes: described first distribution characteristics in the described initiation of charge moment on each statistical time range meets probability distribution letter
Number:
Wherein, μsFor the initiation of charge of electric automobile in this statistical time range
The average in moment, σsFor the standard deviation in the initiation of charge moment of electric automobile in this statistical time range.
Alternatively, the described initiation of charge moment battery charge shape that normal function is respectively described on each statistical time range is used
Second distribution characteristics of state includes: described second point of the described initiation of charge moment battery charge state on each statistical time range
Cloth feature meets probability-distribution function:
Wherein, μrFor the electric automobile initiation of charge moment in this statistical time range
The average of battery charge state, σrFor the standard deviation of electric automobile initiation of charge moment battery charge state in this statistical time range.
Alternatively, the described initiation of charge moment battery lotus that log-normal function is respectively described on each statistical time range is used
Second distribution characteristics of electricity condition includes: described of the described initiation of charge moment battery charge state on each statistical time range
Two distribution characteristicss meet probability-distribution function:
Wherein, μrFor in this statistical time range during electric automobile initiation of charge
Carve the average of battery charge state, σrFor the standard of electric automobile initiation of charge moment battery charge state in this statistical time range
Difference.
Alternatively, according to described first distribution characteristics and described second distribution characteristics and each time engrave need charging
The quantity of electric motor car, the power demand upper limit and/or the lower limit that calculate each moment charging electric vehicle include: electric automobile is one
Each moment t in it0Power demand be Probability-distribution function be:
Wherein, discontinuous variableIt is that 1 expression electric automobile charges,It is that 0 expression electric automobile has filled
Get well electricity or do not start to charge up.tsFor the initiation of charge moment of electric automobile, tcFor the charging duration of electric automobile, PcFor charging merit
Rate, FS、FtcIt is respectively initiation of charge moment and the probability-distribution function of charging duration, FstcDuring for initiation of charge moment and charging
Long joint probability distribution function, Fstc=FSFtc；Under invariable power charge condition, charging duration and initiation of charge moment battery
The relation of state-of-charge SOC is: T '=Pc(1-SOC00), wherein, T ' is charging duration, and SOC% is electric automobile initiation of charge
Moment battery charge amount percentage ratio；
The power demand of the charging of each moment electric motor car is Niμc, wherein, NiThe electronic vapour of charging is needed for each moment
The quantity of car, μcFor the average of the charge power of each moment electric automobile, for normal distribution N (μ, σ), stochastic variable has
The probability distribution of 99% is in interval [μ-3 σ, μ+3 σ], thus obtains the upper limit and/or the lower limit of power demand.
According to another aspect of the present invention, a kind of charging electric vehicle load prediction based on probabilistic model is additionally provided
Device, including: statistical module, for adding up initiation of charge moment and the initiation of charge moment electricity of the electric automobile in preset number of days
Pond state-of-charge；MBM, for being divided into multiple statistical time range by one day, uses normal function or log-normal function
First distribution characteristics in the described initiation of charge moment being respectively described on each statistical time range, is just using normal function or logarithm
Second distribution characteristics of the described initiation of charge moment battery charge state that state function is respectively described on each statistical time range；Calculate
Module, for needing the electric motor car of charging according to described first distribution characteristics and described second distribution characteristics and each moment
Quantity, calculates the power demand upper limit and/or the lower limit of each moment charging electric vehicle.
Alternatively, described MBM includes: the first normal distribution-test module, for entering the described initiation of charge moment
Row normal distribution-test；First MBM, in the case of normal distribution-test is passed through, uses normal function or right
Described first distribution characteristics in the described initiation of charge moment that number normal function is respectively described on each statistical time range；And/or the
Two normal distribution-test modules, for carrying out normal distribution-test to described initiation of charge moment battery charge state；Second builds
Mould module, in the case of normal distribution-test is passed through, uses normal function or log-normal function to be respectively described respectively
Described second distribution characteristics of the described initiation of charge moment battery charge state on individual statistical time range.
By the present invention, use initiation of charge moment and the initiation of charge moment electricity of the electric automobile added up in preset number of days
Pond state-of-charge；One day is divided into multiple statistical time range, uses normal function or log-normal function to be respectively described each
First distribution characteristics in the initiation of charge moment on statistical time range, uses normal function or log-normal function to be respectively described respectively
Second distribution characteristics of the initiation of charge moment battery charge state on individual statistical time range；According to the first distribution characteristics and second point
Cloth feature and each moment need the quantity of the electric motor car of charging, calculate on the power demand of each moment charging electric vehicle
Limit and/or the mode of lower limit, the problem solving the medium-and long-term forecasting of charging electric vehicle load, it is achieved that charging electric vehicle
The medium-and long-term forecasting of load.
Accompanying drawing explanation
Accompanying drawing described herein is used for providing a further understanding of the present invention, constitutes the part of the application, this
Bright schematic description and description is used for explaining the present invention, is not intended that inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the flow process of charging electric vehicle load forecasting method based on probabilistic model according to embodiments of the present invention
Figure；
Fig. 2 is the structural frames of charging electric vehicle load prediction device based on probabilistic model according to embodiments of the present invention
Figure；
Fig. 3 be optional according to the present invention be the charging electric vehicle moment schematic diagram in 40.
Detailed description of the invention
Below with reference to accompanying drawing and describe the present invention in detail in conjunction with the embodiments.It should be noted that do not conflicting
In the case of, the embodiment in the application and the feature in embodiment can be mutually combined.
It should be noted that term " first " in description and claims of this specification and above-mentioned accompanying drawing, "
Two " it is etc. for distinguishing similar object, without being used for describing specific order or precedence.
Providing a kind of charging electric vehicle load forecasting method based on probabilistic model in the present embodiment, Fig. 1 is root
According to the flow chart of the charging electric vehicle load forecasting method based on probabilistic model of the embodiment of the present invention, as it is shown in figure 1, this stream
Journey comprises the steps:
Step S102, the initiation of charge moment of the electric automobile in statistics preset number of days and initiation of charge moment battery charge
State；
Step S104, was divided into multiple statistical time range by one day, used normal function or log-normal function to retouch respectively
State first distribution characteristics in initiation of charge moment on each statistical time range, use normal function or log-normal function respectively
Second distribution characteristics of initiation of charge moment battery charge state on each statistical time range is described；
Step S106, needs the electric motor car of charging according to the first distribution characteristics and the second distribution characteristics and each moment
Quantity, calculates the power demand upper limit and/or the lower limit of each moment charging electric vehicle.
By to predetermined number of days, be greater than 20 days, the initiation of charge moment and initiation of charge moment battery charge state
Distribution characteristics use normal function or log-normal function to be described, thus obtain one day rises on multiple statistical time ranges
Begin charging moment and the regularity of distribution of initiation of charge moment battery state of charge.When the initiation of charge time of electric automobile and charging
It is separate for carving battery charge state (SOC), if the two can be described on time dimension with the mathematical function determined
Information, then just can set up the mathematical function of the two Joint Distribution, and then ask for the load that charges.At known electric electrical automobile
Under conditions of the charge characteristic of on-vehicle battery, the charge power of single amount electric automobile and charging duration only initial with electric automobile
During charging, the state-of-charge of battery is correlated with.In embodiments of the present invention, model difficulty in order to reduce based on probabilistic model, select perseverance
Power charge model.This simplifies the power of each predicted time point and solve difficulty, and to make SOC be line with charging duration
Property inverse relation.Therefore it may only be necessary to know the value of SOC, the charging duration of determination vehicle that can be linear.Arriving of electric automobile
The SOC of time and arrival time of standing presents more stable statistical distribution rule, as long as so finding out and can describe the two
The mathematical function of distribution, resettles the probabilistic model of Joint Distribution, then just can try to achieve corresponding charging load.By above-mentioned
Step, the problem solving the medium-and long-term forecasting of charging electric vehicle load, it is achieved that charging electric vehicle load medium-term and long-term
Prediction.
Alternatively, when using normal function or log-normal function to be respectively described the initiation of charge on each statistical time range
The first distribution characteristics carved includes: the initiation of charge moment is carried out normal distribution-test；In the situation that normal distribution-test is passed through
Under, first distribution in the initiation of charge moment that use normal function or log-normal function are respectively described on each statistical time range
Feature.
Alternatively, when using normal function or log-normal function to be respectively described the initiation of charge on each statistical time range
The second distribution characteristics carving battery charge state includes: initiation of charge moment battery charge state is carried out normal distribution-test；
In the case of normal distribution-test is passed through, normal function or log-normal function is used to be respectively described on each statistical time range
The second distribution characteristics of initiation of charge moment battery charge state.
Alternatively, first distribution characteristics in the initiation of charge moment that use normal function is respectively described on each statistical time range
Probability-distribution function is met including: first distribution characteristics in the initiation of charge moment on each statistical time range:
Wherein, μsFor the average in this statistical time range initiation of charge moment, σsFor
The standard deviation in this statistical time range initiation of charge moment.
Alternatively, first distribution in the initiation of charge moment that use log-normal function is respectively described on each statistical time range
Feature includes: first distribution characteristics in the initiation of charge moment on each statistical time range meets probability-distribution function:
Wherein, μsFor the initiation of charge of electric automobile in this statistical time range
The average in moment, σsFor the standard deviation in the initiation of charge moment of electric automobile in this statistical time range.
Alternatively, the initiation of charge moment battery charge state that use normal function is respectively described on each statistical time range
Second distribution characteristics includes: the second distribution characteristics of the initiation of charge moment battery charge state on each statistical time range meets general
Rate distribution function:
Wherein, μrFor the electric automobile initiation of charge moment in this statistical time range
The average of battery charge state, σrFor the standard deviation of electric automobile initiation of charge moment battery charge state in this statistical time range.
Alternatively, the initiation of charge moment battery charge shape that log-normal function is respectively described on each statistical time range is used
Second distribution characteristics of state includes: the second distribution characteristics of the initiation of charge moment battery charge state on each statistical time range is full
Foot probability-distribution function:
Wherein, μrFor in this statistical time range during electric automobile initiation of charge
Carve the average of battery charge state, σrFor the standard of electric automobile initiation of charge moment battery charge state in this statistical time range
Difference.
Alternatively, according to the first distribution characteristics and the second distribution characteristics and each time engrave and need the electric motor car of charging
Quantity, the power demand upper limit and/or the lower limit that calculate each moment charging electric vehicle include: electric automobile in one day each
Moment t0Power demand be Probability-distribution function be:
Wherein, discontinuous variableIt is that 1 expression electric automobile charges,It is that 0 expression electric automobile has filled
Get well electricity or do not start to charge up.tsFor the initiation of charge moment of electric automobile, tcFor the charging duration of electric automobile, PcFor charging merit
Rate, FS、FtcIt is respectively initiation of charge moment and the probability-distribution function of charging duration, FstcDuring for initiation of charge moment and charging
Long joint probability distribution function, Fstc=FSFtc；Under invariable power charge condition, charging duration and initiation of charge moment battery
The relation of state-of-charge SOC is: T '=Pc(1-SOC00), wherein, T ' is charging duration, and SOC% is electric automobile initiation of charge
Moment battery charge amount percentage ratio；
The power demand of the charging of each moment electric motor car is Niμc, wherein, NiThe electronic vapour of charging is needed for each moment
The quantity of car, μcFor the average of the charge power of each moment electric automobile, for normal distribution N (μ, σ), stochastic variable has
The probability distribution of 99% is in interval [μ-3 σ, μ+3 σ], thus obtains the upper limit and/or the lower limit of power demand.
Through the above description of the embodiments, those skilled in the art is it can be understood that arrive according to above-mentioned enforcement
The method of example can add the mode of required general hardware platform by software and realize, naturally it is also possible to by hardware, but a lot
In the case of the former is more preferably embodiment.Based on such understanding, technical scheme is the most in other words to existing
The part that technology contributes can embody with the form of software product, and this computer software product is stored in a storage
In medium (such as ROM/RAM, magnetic disc, CD), including some instructions with so that a station terminal equipment (can be mobile phone, calculate
Machine, server, or the network equipment etc.) perform the method described in each embodiment of the present invention.
Additionally provide a kind of charging electric vehicle load prediction device device based on probabilistic model in the present embodiment, should
Device is used for realizing above-described embodiment and preferred implementation, has carried out repeating no more of explanation.As used below,
Term " module " can realize the software of predetermined function and/or the combination of hardware.Although the device described by following example is relatively
Realize with software goodly, but hardware, or the realization of the combination of software and hardware also may and be contemplated.
Fig. 2 is the structural frames of charging electric vehicle load prediction device based on probabilistic model according to embodiments of the present invention
Figure, as in figure 2 it is shown, this device includes: statistical module 22, MBM 24 and computing module 26, wherein, and statistical module 22, use
The initiation of charge moment of the electric automobile in statistics preset number of days and initiation of charge moment battery charge state；MBM
24, coupleding to statistical module 22, for one day being divided into multiple statistical time range, using normal function or log-normal function
First distribution characteristics in the initiation of charge moment being respectively described on each statistical time range, uses normal function or lognormal letter
Second distribution characteristics of the initiation of charge moment battery charge state that number is respectively described on each statistical time range；Computing module 26,
It coupled to MBM 24, for needing the electronic of charging according to the first distribution characteristics and the second distribution characteristics and each moment
The quantity of car, calculates the power demand upper limit and/or the lower limit of each moment charging electric vehicle.
Alternatively, MBM 24 includes: the first normal distribution-test module, for the initiation of charge moment is carried out normal state
Distribution inspection；First MBM, in the case of normal distribution-test is passed through, uses normal function or lognormal
First distribution characteristics in the initiation of charge moment that function is respectively described on each statistical time range；And/or second normal distribution-test
Module, for carrying out normal distribution-test to initiation of charge moment battery charge state；Second MBM, for dividing in normal state
In the case of cloth is upchecked, use that normal function or log-normal function be respectively described on each statistical time range initial fills
Second distribution characteristics of electricity moment battery charge state.
It should be noted that above-mentioned modules can be by software or hardware realizes, for the latter, Ke Yitong
Cross in the following manner to realize, but be not limited to this: above-mentioned module is respectively positioned in same processor；Or, above-mentioned module lays respectively at many
In individual processor.
Embodiments of the invention additionally provide a kind of software, and this software is used for performing above-described embodiment and preferred implementation
Described in technical scheme.
Embodiments of the invention additionally provide a kind of storage medium.In the present embodiment, above-mentioned storage medium can be set
It is set to storage for the program code performing following steps:
Step S102, the initiation of charge moment of the electric automobile in statistics preset number of days and initiation of charge moment battery charge
State；
Step S104, was divided into multiple statistical time range by one day, used normal function or log-normal function to retouch respectively
State first distribution characteristics in initiation of charge moment on each statistical time range, use normal function or log-normal function respectively
Second distribution characteristics of initiation of charge moment battery charge state on each statistical time range is described；
Step S106, needs the electric motor car of charging according to the first distribution characteristics and the second distribution characteristics and each moment
Quantity, calculates the power demand upper limit and/or the lower limit of each moment charging electric vehicle.
Alternatively, in the present embodiment, above-mentioned storage medium can include but not limited to: USB flash disk, read only memory (Read-
Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), mobile hard
The various media that can store program code such as dish, magnetic disc or CD.
Alternatively, the concrete example in the present embodiment is referred to described in above-described embodiment and optional embodiment
Example, the present embodiment does not repeats them here.
In order to the description making the embodiment of the present invention is clearer, it is described below in conjunction with preferred embodiment and illustrates.
Arrival time (determining the initiation of charge time) and arrival time SOC (determining charging duration) to electric passenger vehicle are made
Statistical Analysis, in q-q figure, the distribution of the two often has most presses close to normal state and the characteristic of inclined normal distribution, so warp
Normal state or log-normal function is often used to describe the distribution character of the two.But realistic situation is, electric passenger vehicle arrival time and
Although the normal distribution line that the statistical data of arrival time SOC is in q-q, but there is deviation.It is thus desirable to segmentation
The Discrete Distribution data acquisition Jarque-Bera normal distribution-test method of vehicle initiation of charge time and charging duration is just being carried out
State is checked.
Jarque-Bera normal distribution-test is whether test samples meets normal distribution, uses jbtest in Matlab
Function completes, need not when calling this function specify distribution average and variance, call format be [h, p]=jbtest (x,
Alpha), alpha is the significance level of inspection, typically takes 0.05.Output result h of function and the implication of p be: hypothesis testing
Sample x Normal Distribution, when exporting h equal to 1, represents that time refusal accepts null hypothesis in significant level α=0.05；Output h
During equal to 0, represent and time accept null hypothesis in significant level α=0.05.The inspection p value returned refers to, when p value shows less than given
Work property level, refuses null hypothesis.Under conditions of by Jarque-Bera normal distribution-test, can be by vehicle initiation of charge
Normal state and log-normal function that time and charging duration matching obtain are described, and set up the mathematical modulo of Joint Distribution
Type.
If electric passenger vehicle charging start time obeys segmentation normal distribution, the probability-distribution function of every section is
In formula, μsFor the average of start time of charging in every section of distribution, σsStandard for start time of charging in every section of distribution
Difference, represents the dispersion degree of charging start time distribution.
If electric passenger vehicle charging start time obeys segmentation logarithm normal distribution, the probability-distribution function of every section is
In formula, μsFor the average of start time of charging in every section of distribution, σsStandard for start time of charging in every section of distribution
Difference, represents the dispersion degree of charging start time distribution.
If electric passenger vehicle arrival time SOC obeys segmentation normal distribution, the probability-distribution function of every section is
In formula, μrFor the average of arrival time SOC, σ in every section of distributionrFor the standard of arrival time SOC in every section of distribution
Difference, represents the dispersion degree travelling arrival time SOC.
If electric passenger vehicle arrival time SOC obeys segmentation logarithm normal distribution, the probability-distribution function of every section is
In formula, μrFor the average of arrival time SOC, σ in every section of distributionrFor the standard of arrival time SOC in every section of distribution
Difference, represents the dispersion degree of arrival time SOC distribution.
Under invariable power charge condition, the relation of charging duration and SOC is
T ＇=Pc(1-SOC%)
In formula, T ＇ is charging duration, PcFor charging electric vehicle rate, SOC% is charging initial time vehicle carrying capacity hundred
Proportion by subtraction.
According to the law of large numbers: if stochastic variable X1, X2, X3 ..., Xn ... separate, obey same distribution, and mathematics
Expect E (Xk)=μ (k=1,2 ...), then for arbitrary integer ε, have
Assume that electric motor car charging start time and charging duration the two variable are independent, time series be analyzed,
The charged state that whether is in a certain moment electric automobile depends on when whether current time electric motor car has begun to charging and charging
Long.
Fig. 3 be optional according to the present invention be the charging electric vehicle moment schematic diagram in 40, as it is shown on figure 3, analyze electricity
Motor-car at a time t0Charge and terminated or situation about not starting to charge up, wherein tsFor charging start time, tcDuring for charging
Long.The joint probability distribution of charge initial time and charging duration according to electric motor car sets up the segmentation possibilities mould of day charging load
Type.
Electric automobile is certain moment t in one day0Power demand be Probability distribution be
In formula, discontinuous variableIt is that 1 expression vehicle charges,It is that 0 expression is the most charged or do not start
Charging.tsFor charging start time, tcFor charging duration, PcFor charge power, FS、FtcIt is respectively charging start time and charging
The probability-distribution function of duration.FstcFor charging start time and the joint probability distribution function of charging duration, Fstc=FSFtc。
Simultaneous formula 1～formula 3, obtain the segmentation possibilities model of charging of each separate unit electric passenger vehicle day in moment load in a day.
Assume that in one day, there is N each time periodiCar needs charging, central limit theorem know, the charging of a certain moment taxi needs
Ask as Niμc, wherein μcIt it is the average of the charge power of each moment separate unit passenger car in 1 day.For normal distribution N (μ, σ), at random
Variable has the probability distribution of 99% in interval [μ-3 σ, μ+3 σ], the upper and lower bound of available power demand.
Obviously, those skilled in the art should be understood that each module of the above-mentioned present invention or each step can be with general
Calculating device realize, they can concentrate on single calculating device, or be distributed in multiple calculating device and formed
Network on, alternatively, they can with calculate the executable program code of device realize, it is thus possible to by they store
Performed by calculating device in the storage device, and in some cases, can perform with the order being different from herein shown
The step gone out or describe, or they are fabricated to respectively each integrated circuit modules, or by the multiple modules in them or
Step is fabricated to single integrated circuit module and realizes.So, the present invention is not restricted to the combination of any specific hardware and software.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.All within the spirit and principles in the present invention, that is made any repaiies
Change, equivalent, improvement etc., should be included within the scope of the present invention.
Claims (10)
1. a charging electric vehicle load forecasting method based on probabilistic model, it is characterised in that including:
The initiation of charge moment of the electric automobile in statistics preset number of days and initiation of charge moment battery charge state；
One day is divided into multiple statistical time range, uses normal function or log-normal function to be respectively described each statistical time range
On first distribution characteristics in described initiation of charge moment, use normal function or log-normal function to be respectively described each system
Second distribution characteristics of the described initiation of charge moment battery charge state in timing section；
The quantity of the electric motor car of charging is needed according to described first distribution characteristics and described second distribution characteristics and each moment,
Calculate the power demand upper limit and/or the lower limit of each moment charging electric vehicle.
Method the most according to claim 1, it is characterised in that use normal function or log-normal function to be respectively described
First distribution characteristics in the described initiation of charge moment on each statistical time range includes:
The described initiation of charge moment is carried out normal distribution-test；
In the case of normal distribution-test is passed through, when using normal function or log-normal function to be respectively described each statistics
Described first distribution characteristics in the described initiation of charge moment in section.
Method the most according to claim 1, it is characterised in that use normal function or log-normal function to be respectively described
Second distribution characteristics of the described initiation of charge moment battery charge state on each statistical time range includes:
Described initiation of charge moment battery charge state is carried out normal distribution-test；
In the case of normal distribution-test is passed through, when using normal function or log-normal function to be respectively described each statistics
Described second distribution characteristics of the described initiation of charge moment battery charge state in section.
Method the most according to claim 1, it is characterised in that use normal function to be respectively described on each statistical time range
First distribution characteristics in described initiation of charge moment includes:
Described first distribution characteristics in the described initiation of charge moment on each statistical time range meets probability-distribution function:
Wherein, μsFor the average in this statistical time range initiation of charge moment, σsStandard deviation for this statistical time range initiation of charge moment.
Method the most according to claim 1, it is characterised in that use log-normal function to be respectively described each statistical time range
On first distribution characteristics in described initiation of charge moment include:
Described first distribution characteristics in the described initiation of charge moment on each statistical time range meets probability-distribution function:
Wherein, μsFor the average in the initiation of charge moment of electric automobile, σ in this statistical time rangesFor electric automobile in this statistical time range
The standard deviation in initiation of charge moment.
Method the most according to claim 1, it is characterised in that use normal function to be respectively described on each statistical time range
Second distribution characteristics of described initiation of charge moment battery charge state includes:
Described second distribution characteristics of the described initiation of charge moment battery charge state on each statistical time range meets probability and divides
Cloth function:
Wherein, μrFor the average of electric automobile initiation of charge moment battery charge state, σ in this statistical time rangerFor this statistical time range
The standard deviation of middle electric automobile initiation of charge moment battery charge state.
Method the most according to claim 1, it is characterised in that use log-normal function to be respectively described each statistical time range
On the second distribution characteristics of described initiation of charge moment battery charge state include:
Described second distribution characteristics of the described initiation of charge moment battery charge state on each statistical time range meets probability and divides
Cloth function:
Wherein, μrFor the average of electric automobile initiation of charge moment battery charge state, σ in this statistical time rangerFor this statistical time range
The standard deviation of middle electric automobile initiation of charge moment battery charge state.
Method the most according to any one of claim 1 to 7, it is characterised in that according to described first distribution characteristics and institute
State the second distribution characteristics and each time engrave the quantity of electric motor car needing charging, calculate each moment charging electric vehicle
The power demand upper limit and/or lower limit include:
Electric automobile is each moment t in one day0Power demand be Probability-distribution function be:
Wherein, discontinuous variableIt is that 1 expression electric automobile charges,It is that 0 expression electric automobile is the most charged
Or do not start to charge up.tsFor the initiation of charge moment of electric automobile, tcFor the charging duration of electric automobile, PcFor charge power,
FS、FtcIt is respectively initiation of charge moment and the probability-distribution function of charging duration, FstcFor initiation of charge moment and charging duration
Joint probability distribution function, Fstc=FSFtc；Under invariable power charge condition, charging duration and initiation of charge moment battery lotus
The relation of electricity condition SOC is: T '=Pc(1-SOC%), wherein, T ' is charging duration, when SOC% is electric automobile initiation of charge
Carve battery charge amount percentage ratio；
The power demand of the charging of each moment electric motor car is Niμc, wherein, NiThe electric automobile of charging is needed for each moment
Quantity, μcFor the average of the charge power of each moment electric automobile, for normal distribution N (μ, σ), stochastic variable has 99%
Probability distribution is in interval [μ-3 σ, μ+3 σ], thus obtains the upper limit and/or the lower limit of power demand.
9. a charging electric vehicle load prediction device based on probabilistic model, it is characterised in that including:
Statistical module, for adding up initiation of charge moment and the initiation of charge moment battery charge of the electric automobile in preset number of days
State；
MBM, for being divided into multiple statistical time range by one day, uses normal function or log-normal function to retouch respectively
State first distribution characteristics in described initiation of charge moment on each statistical time range, use normal function or log-normal function
Second distribution characteristics of the described initiation of charge moment battery charge state being respectively described on each statistical time range；
Computing module, for needing charging according to described first distribution characteristics and described second distribution characteristics and each moment
The quantity of electric motor car, calculates the power demand upper limit and/or the lower limit of each moment charging electric vehicle.
Device the most according to claim 9, it is characterised in that described MBM includes:
First normal distribution-test module, for carrying out normal distribution-test to the described initiation of charge moment；First MBM,
In the case of passing through in normal distribution-test, when using normal function or log-normal function to be respectively described each statistics
Described first distribution characteristics in the described initiation of charge moment in section；And/or
Second normal distribution-test module, for carrying out normal distribution-test to described initiation of charge moment battery charge state；
Second MBM, in the case of normal distribution-test is passed through, uses normal function or log-normal function respectively
Described second distribution characteristics of described initiation of charge moment battery charge state on each statistical time range is described.
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| CN112036624A (en) * | 2020-08-21 | 2020-12-04 | 上海电力大学 | Power grid dispatching method based on charging load prediction of electric vehicles in region |
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