CN108805322A - A kind of charging load spatial and temporal distributions prediction technique of private car - Google Patents
A kind of charging load spatial and temporal distributions prediction technique of private car Download PDFInfo
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Abstract
The invention discloses a kind of charging load spatial and temporal distributions prediction techniques of private car.Include the following steps：Step 1, monitoring vehicle flow, the anti-road traffic simulation amount for pushing away cell, the parking probability and newly-increased vehicle number of dynamic prediction different location；Step 2, when selecting charging modes, according to fast charge, trickle charge feature, formulate user psychology to the transformation rule between fast charge probability, and introduce cloud model in rule and determine user's fast charge probability, the randomness and ambiguity of embodiment user's decision；Step 3, the duration of load application curve that different charging places are calculated using Monte Carlo method analysis, the spatial and temporal distributions characteristic of prediction electric vehicle charging load.The method of the present invention can conveniently and effectively calculate the spatial and temporal distributions situation of each cell charging load, and prediction result provides foundation for researchs such as charging station planning, load schedulings.
Description
Technical field
The invention belongs to intelligent grid field, more particularly to a kind of private car charging load spatial and temporal distributions prediction technique.
Background technology
In recent years, urban automobile quantity rapid growth so that environment, energy problem become increasingly conspicuous, and electric vehicle is because of it
Good environmental protection and energy conservation characteristic become the most promising vehicles.However, electric vehicle access power grid on a large scale must
Can so impact be formed to the operation and planning of existing electric system, have scholar and numerous studies, content have been carried out to these problems
It is related to many aspects such as pressure drop, circuit network loss, voltage stability margin, percent harmonic distortion；Meanwhile in order to eliminate or inhibit electronic vapour
The negative effect that the unordered charge-carrying belt of vehicle comes, the orderly charging of electric vehicle and optimization charging strategy are also the hot spot of research.Make
For the basis of these researchs, the spatial and temporal distributions of electric vehicle charging load how are accurately predicted, to the accuracy of follow-up study
And the reasonability of conclusion has far-reaching influence.But previous load forecasting model mostly uses fixed parking probability distribution,
And lack the rational foundation for calculating fast charge load.
Invention content
Technical problem solved by the invention passes through there is provided a kind of private car charging load spatial and temporal distributions prediction technique
Traffic trip matrix calculates the parking area distribution probability of different moments, determines user's fast charge probability by cloud model, is used in combination
Monte carlo method carries out simulation and forecast to load.
Realize that the technical solution of the object of the invention is：A kind of private car charging load spatial and temporal distributions prediction technique, packet
Include following steps：
Step 1: monitoring vehicle flow, the anti-road traffic simulation amount for pushing away cell, the parking probability of dynamic prediction different location with
And newly-increased vehicle number.Enough traffic informations are acquired, it is counter to push away acquisition by PSO Algorithm link counting optimal models
Traffic trip matrix (OD matrixes), and then calculate the parking area distribution probability of different moments.Pass through gauss hybrid models meter
Charging time started distribution probability is calculated, when electrically-charging equipment is enough, it is believed that newly-increased charging vehicle distributed areas probability and parking area
Domain distribution probability is equal, calculates cell and increases vehicle fleet size newly.
Step 2: when selecting charging modes, according to fast charge, trickle charge feature, user psychology is formulated between fast charge probability
Transformation rule, and introduce cloud model in rule and determine user's fast charge probability, embody the ambiguity of user's decision.In order to fixed
Can amount analysis trickle charge mode meet charge requirement, construct the distribution of consideration charging duration, initial SOC distributions, trip distance point
The trickle charge failure degree Dem of the information such as cloth, and then it is converted into the rule that user selects charging modes.
When Dem is in interlude, user has very strong ambiguity to the decision of charging modes.Cloud model is introduced it is expected
Ex, entropy En and tri- numbers of super entropy He carry out the uncertain concept of general token one, using one-dimensional half liter of cloud of lognormal X conditions
The probability of fast charge is selected to estimate single unit vehicle.
Step 3: calculating the duration of load application curve in different charging places using Monte Carlo method analysis, electronic vapour is predicted
The spatial and temporal distributions characteristic of vehicle charging load.
According to data such as the electric vehicle number of each cell, the distribution of charging duration, vehicle ratios, acquisition traffic is believed in real time
Breath, extracts that vehicle, charging duration, initial soc, trip distance etc. predicts electric automobile load again by Monte Carlo simulation
Spatial and temporal distributions.
For selecting the vehicle of fast charge, set charge target as the 80% of full capacity, needs to recalculate its charging duration,
And to the vehicle of all selection trickle charges, the charging duration that will be extracted before calculates its charging finishing time.
Traffic information is resurveyed every 15min, calculates and increases charging vehicle quantity, charge power newly, realizes charging load
The dynamic prediction of spatial and temporal distributions.
Compared with prior art, the present invention its remarkable advantage is：1) method dynamic of the invention calculates charging place
Stop probability, and the spatial and temporal distributions of charging load can be more accurately predicted；2) charging modes of fuzzy prediction user of the present invention,
So as to avoid previous drawback；3) method of the invention can conveniently and effectively calculate the space-time of each cell charging load
Distribution situation, prediction result provide foundation for researchs such as charging station planning, load schedulings；4) of the invention to be gone out based on traffic
The charging load spatial and temporal distributions prediction technique of row matrix and cloud model.Each cell charging load can conveniently and effectively be calculated
Spatial and temporal distributions situation, prediction result provides foundation for researchs such as charging station planning, load schedulings.
Description of the drawings
Fig. 1 is a kind of private car charging load spatial and temporal distributions prediction technique flow chart.
Fig. 2 is electric vehicle charging load spatial and temporal distributions prediction structure chart.
Fig. 3 is charging duration distribution curve.
Fig. 4 is the electric vehicle charging load spatial and temporal distributions prediction flow chart based on Monte Carlo simulation.
Representative meaning is numbered in figure is：1 increases newly and fills for the parking probability of dynamic prediction different location and each region
Electric vehicle fleet size, 2 determine user's fast charge probability to establish cloud model, and 3 is negative to obtain electric vehicle charging using Monte Carlo method
The spatial and temporal distributions of lotus.
Specific implementation mode
The present invention proposes a kind of private car charging load spatial and temporal distributions prediction technique, includes the following steps：
Step 1: the traffic information that acquisition is enough, obtains traffic trip matrix, in turn using the anti-method pushed away of vehicle flow
Calculate the parking area distribution probability P of different momentsi′(t)。
Step 2: according to charging time started distribution probability, total electric vehicle ownership and Pi' (t) calculates cell
I increases charging vehicle quantity N ' newly in special time periodi(t)。
Step 3: calculating trickle charge failure degree according to information such as charging duration distribution, initial SOC distributions, trip distance distributions
Dem, and then calculate the probability that single unit vehicle selects fast charge using cloud model method
Step 4: obtaining the spatial and temporal distributions of electric vehicle charging load using Monte Carlo method.
Further, in step 1, region is totally divided into n cell, and several will be divided into working day and be divided into
The period of Δ t.Parking area distribution probability Pi' (t) refers to that vehicle reaches the flat of cell i area parking during [t- Δs t, t]
Equal probability can be acquired by traffic trip matrix (OD matrixes).So-called OD matrixes refer to all starting points in transportation network
(Origin) it goes on a journey between terminal (Destination) and exchanges the table of quantity, describe a traffic in special time period
The traffic trip amount of all origin-to-destinations in network.OD matrixes are estimated up to dot matrix for dynamic origins-in transportation network
A kind of practical algorithm.Its form is shown in Table 1.
1 OD matrixes of table
In table 1：TijFor the volume of traffic from the areas i to the areas j；OiIt is the generation volume of traffic in the areas i；DjIt is the attraction volume of traffic in the areas j；
T is the total wheel traffic in the area.Parking area distribution probability Pi' (t) can be calculated as follows：
The present invention obtains OD matrixes by the anti-method pushed away of link counting, is specially sought to following using particle cluster algorithm
Excellent model seeks approximate solution.
In formula, Pa_ijProbability is chosen for section, refers to reaching the vehicle in the areas j by the probability in the sections a from the areas i；QaFor
The road Traffic Volume in the sections a, pcu/h；M is section number；E is all link counting estimated bias quadratic sums；TijTo wait asking change
Amount.As E minimums, the T acquiredijClosest to truth.
Further, in step 2, private car is mainly used for short distance in city and drives, therefore its time started of charging has week
Phase property.According to statistics, the probability density of electric vehicle charging start time can be with gauss hybrid models approximate representation
In formula, αiFor the weight coefficient of each distribution；βiFor the expectation of distribution；γiFor the variance of distribution.
If electric vehicle sum in region to be measured is Nu, the daily average charge number of private car be ξ, then cell i [t- Δ t,
T] newly-increased charging vehicle number is in the period
In formula, Pi(t) probability that charging vehicle is distributed in the areas i is increased during being [t- Δs t, t] newly.When electrically-charging equipment is enough,
Think Pi(t)=Pi′(t)。
Further, in step 3, from the angle of user psychology, when user's charge requirement is not " urgent ", preferentially
Consider the charging modes of trickle charge, and assumes disposably to be full of；Conversely, then paying the utmost attention to fast charge.The present invention constructs trickle charge failure
Can degree Dem carrys out quantitatively characterizing trickle charge mode meet user's charge requirement：
In formula, d is expected trip distance again, km；W is vehicle per 100 km power consumption；C is battery capacity；Soc is to fill
Private car residue state-of-charge before electricity；PW is trickle charge power；Δ t is expected charging duration, min.Wherein, soc, Δ t, d are phase
Mutual independent stochastic variable, value can be obtained according to ASSOCIATE STATISTICS curve.
The electric energy that the characterization of molecules expection of Dem will need, the electric energy that can be obtained after this expected charging of denominator characterization.
Therefore, Dem values are bigger, and situation gets over " urgent ", and user selects the possibility of trickle charge mode lower, change with Dem values, user's charging
The urgency level of demand also changes therewith, can be divided into following three phases：
1) as 0≤Dem≤DEMtheWhen, user's charge requirement degree is " not urgent ", wherein DEMtheIt is normal for threshold value undetermined
Number.In this stage, trickle charge mode can fully meet user's trip requirements next time, and user preferentially selects trickle charge, individual difference
The decision of charging modes is not influenced.
2) work as DEMthe<Dem<When 1, user's charge requirement degree is " relatively more urgent ".In this stage, trickle charge mode is still
Trip requirements next time can be met, but enough psychological security surpluses cannot be reserved.As Dem values increase, the heart promptly to charge
Reason demand is more and more stronger.In this stage, influence of the individual difference of user to charging modes decision is very big.And identical Dem
Value, different users may make opposite decision, therefore have very strong ambiguity.
3) as Dem >=1, user's charge requirement degree is " very urgent ".In this stage, trickle charge mode cannot expire
Trip requirements of foot, with selecting fast charge per family.Individual difference nor affects on the decision of charging modes at this time.
As seen from the above analysis, in " relatively more urgent " stage, the influence of family subjective factor is benefited from, user selects fast charge
Probability there is very strong mould, and in other two stage, user's charging modes are influenced very little by individual difference, can be used normal
It counts to characterize the probability that user selects fast charge.Consider that the above feature, individual consumer select the probability of fast charge that can use following segmentation
Function representation：
In formula,For the fast charge probability density function after cloud model, value range is (0,1), with Dem values
The relationship being positively correlated, but under conditions of identical Dem values, value result is not unique, the randomness of analog subscriber decision and
Ambiguity.
In view of the feature of fast charge probability monotone increasing and the broad applicability of logarithm normal distribution, the present invention uses one
Half liter of cloud of lognormal X conditions is tieed up to estimateValue.Known Dem, algorithm for estimating are as follows：
Input：Numerical characteristic it is expected Ex, entropy En, super entropy He, X conditions Dem.
Output：Water dust
1) it is expectation, He to generate with En2For a normal random number En ' of variance.
2) it enablesDomain is mapped in section (DEMthe, 1)
On.
3) it calculates
4) water dust is generated
5) step 1)~step 4) is repeated, until the Dem of all inputs generates water dust.
Further, in step 4, prediction load needs following data：Charge initial soc, charging duration tc, again go on a journey
Distance d.According to investigations, there is 82% charging behavior to be happened in family, have 18% charging behavior be happened at it is outgoing during.Cause
This, is respectively fitted the initial soc of " in family ", " outgoing ", obtains following probability density.
For tc, by the distribution of charging room operating range Δ d twice, calculate the electricity that vehicle is consumed in charging room twice
Energy Δ soc, to speculate the charging duration distribution of different automobile types indirectly." vehicle one " charging room operating range Δ d is fitted twice
Probability density is：
The electric energy Δ soc that vehicle is consumed in charging room twice is
In formula, W1、C1Respectively " vehicle one " per 100 km power consumption and battery capacity.It is required to supplement these electric energy
Time is set as tc, calculation formula is
In formula, PW1For the trickle charge power of " vehicle one ".According to formula (10), " vehicle one " single charge duration can be calculated
Distribution situation.Furthermore, it is contemplated that Δ soc is mainly influenced by user's use habit, it is little with vehicle relationship.It is assumed herein that no
Δ soc distributions with vehicle are essentially identical, and the single charge duration of other vehicles in this way can also calculate.Four kinds of vehicles
The result of calculation of charging duration distribution curve is as shown in Figure 3.
Trip distance again refers to being calculated since current departure place, until the spacing in next chargeable place, is passed through
Fitting, obtained probability density function are
F (d)=0.3549e-0.07276d+0.4085e-0.3317d (11)
Emulation is with t0Moment acquires Traffic Information as starting point, using Traffic monitoring platform, calculates stopping for each cell
Vehicle spatial distribution probability.Further according to the distribution of charging time started, region electric vehicle sum, calculates and increase charging vehicle quantity newly.
Vehicle, charging duration, initial soc, the again trip distance for extracting these vehicles, calculate its trickle charge failure degree Dem, judge vehicle
The state of emergency.The vehicle of " not urgent ", directly selects trickle charge；The vehicle of " urgent ", directly selects fast charge；" relatively more urgent "
Vehicle then calculates the fast charge probability after cloud model using above-mentioned algorithm.It, will be according to taking out before to the vehicles of all selection trickle charges
The charging duration taken, calculates its charging finishing time；For selecting the vehicle of fast charge, it assumes that its charge target is full capacity
80%, charging duration is recalculated according to fast charge power, determines its charging finishing time.Finally, statistics is just in charging vehicle
Vehicle, charging modes add up and obtain the charge power of each cell at this time.
Hereafter, traffic information is resurveyed every 15min, calculates and increases charging vehicle quantity, charge power newly.And for it
It is preceding in the vehicle of charging, if reaching its charging finishing time, terminate charging；Otherwise, continue to charge.Update it is all just
Vehicle, charging modes in charging vehicle calculate each sub-district charge power, until emulation terminates.
The method dynamic of the present invention calculates the parking probability in charging place, and charging load can be more accurately predicted
Spatial and temporal distributions.
Claims (4)
1. a kind of charging load spatial and temporal distributions prediction technique of private car, which is characterized in that include the following steps：
Step 1, monitoring vehicle flow, the road traffic simulation amount of cell, the parking of dynamic prediction different location are determined by the flow
Probability and newly-increased vehicle number；
Step 2, according to fast charge, the characteristic of trickle charge, formulate user psychology to the transformation rule between fast charge probability, and in rule
It introduces cloud model and determines user's fast charge probability；
Step 3 calculates trickle charge failure degree Dem according to information such as charging duration distribution, initial SOC distributions, trip distance distributions, into
And determine that single unit vehicle selects the probability of fast charge using cloud model method
Step 4, the duration of load application curve that different charging places are determined using Monte Carlo method, electric vehicle is determined by the curve
The spatial and temporal distributions of charging load.
2. the charging load spatial and temporal distributions prediction technique of private car as described in claim 1, which is characterized in that moved in step 1
State predicts that the parking probability of different location and newly-increased vehicle number are specially：
Region is totally divided into n cell, and working day is divided into several periods for being divided into △ t；Parking area
Distribution probability Pi' (t) refers to the average probability that vehicle reaches the parking of cell i area during [t- △ t, t], passes through traffic trip square
Battle array is that OD Matrix Calculatings obtain；The OD matrixes refer to that trip exchanges the table of quantity between all Origin And Destinations in transportation network,
Describe the traffic trip amount of all origin-to-destinations in a transportation network in special time period；The form of OD matrixes is shown in Table 1：
1 OD matrixes of table
In table, TijFor the volume of traffic from the areas i to the areas j；OiIt is the generation volume of traffic in the areas i；DjIt is the attraction volume of traffic in the areas j；T is this
The total wheel traffic in area；Parking area distribution probability Pi' (t) is calculated as follows：
OD matrixes are obtained by the anti-pushing manipulation of link counting, approximation is specifically sought to following optimizing models using particle cluster algorithm
Solution：
In formula, Pa_ijProbability is chosen for section, refers to reaching the vehicle in the areas j by the probability in the sections a from the areas i；QaFor the roads a
The road Traffic Volume of section, pcu/h；M is section number；E is all link counting estimated bias quadratic sums；TijFor unknown variable,
As E minimums, the T acquiredijClosest to truth.
3. the charging load spatial and temporal distributions prediction technique of private car as described in claim 1, which is characterized in that electric vehicle fills
The probability density of electric start time is expressed as with gauss hybrid models
In formula, αiFor the weight coefficient of each distribution；βiFor the expectation of distribution；γiFor the variance of distribution；
If electric vehicle sum in region to be measured is Nu, the daily average charge number of private car is ξ, then cell i is at [t- △ t, t]
Between increase charging vehicle number in section newly and be
In formula, Pi(t) probability that charging vehicle is distributed in the areas i is increased during being [t- △ t, t] newly；When electrically-charging equipment is enough, it is believed that Pi
(t)=Pi′(t)。
4. the charging load spatial and temporal distributions prediction technique method of private car as described in claim 1, which is characterized in that in step 3
The formula of trickle charge failure degree Dem is：
In formula, d is expected trip distance again, km；W is vehicle per 100 km power consumption；C is battery capacity；Soc is before charging
Private car residue state-of-charge；PW is trickle charge power；△ t are expected charging duration, min；Wherein, soc, △ t, d are mutually solely
Vertical stochastic variable；
Trickle charge failure degree Dem meets：
1) as 0≤Dem≤DEMtheWhen, user's charge requirement degree is " not urgent ", wherein DEMtheFor threshold constant undetermined；?
This stage, trickle charge mode can fully meet user's trip requirements next time, and user preferentially selects trickle charge, individual difference not to influence
The decision of charging modes；
2) work as DEMthe<Dem<When 1, user's charge requirement degree is " relatively more urgent "；At this stage, trickle charge mode remains to meet
Trip requirements next time, but enough psychological security surpluses cannot be reserved；
3) as Dem >=1, user's charge requirement degree is " very urgent "；At this stage, trickle charge mode has been unable to meet next
Secondary trip requirements, with selecting fast charge per family；
Individual consumer selects the probability of fast charge that can be indicated with following piecewise function：
In formula,For the fast charge probability density function after cloud model, value range is (0,1).
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109711630A (en) * | 2018-12-28 | 2019-05-03 | 郑州大学 | A kind of electric car fast charge station addressing constant volume method based on trip probability matrix |
| CN110516372A (en) * | 2019-08-29 | 2019-11-29 | 重庆大学 | Meter and the electric car state-of-charge spatial and temporal distributions analogy method of Quasi dynamic traffic flow |
| CN111199320A (en) * | 2020-01-07 | 2020-05-26 | 国家电网有限公司 | Electric vehicle charging load space-time distribution prediction method based on travel probability matrix |
| US11498450B2 (en) * | 2019-05-21 | 2022-11-15 | Rolls-Royce Plc | Forecast of electric vehicle state of charge and energy storage capacity |
| CN116353399A (en) * | 2023-05-09 | 2023-06-30 | 湖北国网华中科技开发有限责任公司 | Dynamic operation method, device and equipment of charging pile and readable storage medium |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711630A (en) * | 2018-12-28 | 2019-05-03 | 郑州大学 | A kind of electric car fast charge station addressing constant volume method based on trip probability matrix |
| US11498450B2 (en) * | 2019-05-21 | 2022-11-15 | Rolls-Royce Plc | Forecast of electric vehicle state of charge and energy storage capacity |
| CN110516372A (en) * | 2019-08-29 | 2019-11-29 | 重庆大学 | Meter and the electric car state-of-charge spatial and temporal distributions analogy method of Quasi dynamic traffic flow |
| CN110516372B (en) * | 2019-08-29 | 2023-02-17 | 重庆大学 | Electric vehicle charge state space-time distribution simulation method considering quasi-dynamic traffic flow |
| CN111199320A (en) * | 2020-01-07 | 2020-05-26 | 国家电网有限公司 | Electric vehicle charging load space-time distribution prediction method based on travel probability matrix |
| CN116353399A (en) * | 2023-05-09 | 2023-06-30 | 湖北国网华中科技开发有限责任公司 | Dynamic operation method, device and equipment of charging pile and readable storage medium |
| CN116353399B (en) * | 2023-05-09 | 2023-11-03 | 湖北国网华中科技开发有限责任公司 | Dynamic operation method, device and equipment of charging pile and readable storage medium |
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