CN110119841A - A kind of charging station load forecasting method based on user's more options - Google Patents
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Abstract
The invention discloses a kind of charging station load forecasting methods based on user's more options, comprising: S1. determines the ground point range predicted, obtains the relevant parameter of charging station and the electric car that need to be charged in described ground point range；S2. in described ground point range, different factors is obtained and correspond to the influence of user's progress charging station selection to electric car, and calculate the attraction that each charging station corresponds to user to electric car；S3. the quantity of any time electric car in charging station, and the probability that currently electric car in the charging station leaves are calculated according to the attraction；S4. the charging load of charging station is calculated by Monte Carlo method.The factors such as the shop of the distance of charging station and surrounding, school are included in calculating together when the present invention carries out the load prediction of charging station, simultaneously for the problem that multiple charging stations carry out load prediction, multiple influence factors are not combined during solving existing charging load prediction.
Description
Technical field
The present invention relates to electric car charging load prediction technical field more particularly to a kind of filling based on user's more options
Plant load prediction technique.
Background technique
Important support system of the charging infrastructure as Development of Electric Vehicles, reasonable planning construction is to electric car
The development of industry has great importance.At this stage to the research of charging infrastructure planning, need to charge in electric car negative
It is carried out on lotus fundamentals of forecasting.Scale, charge mode, moving law, battery behavior and the electricity price of usual situation consideration electric car
The factors such as system, establish load forecasting model.
Generally speaking, charging pile load is mainly influenced by two factors, first is that electric car charge power size, second is that electric
Electrical automobile charging duration, wherein charging duration includes that the entirety of single motor automobile charging duration and all electric cars is filled
Electric duration, wherein whole charging duration depends on the queuing number of Rechargeable vehicle.But all it is to the research for being lined up number at present
Carry out load prediction for single charging station, do not account for yet other influences factor around distance and charging pile to user into
It is influenced caused by row charging selection, they can not be subjected to quantitative analysis to the influence degree of charging load.
Summary of the invention
The present invention is not combine multiple influence factors during solving the problems, such as existing charging station load prediction, is mentioned
A kind of charging station load forecasting method based on user's more options is supplied.
To realize the above goal of the invention, and the technological means used is:
A kind of charging station load forecasting method based on user's more options, comprising:
S1. it determines the ground point range predicted, obtain the charging station in described ground point range and need to charge electronic
The relevant parameter of automobile, the relevant parameter include the quantity of charging station, filling in each charging station in described ground point range
Electric stake quantity, the quantity of electric car；
S2. in described ground point range, different factors are obtained, the shadow that user carries out charging station selection is corresponded to electric car
It rings, and calculates the attraction that each charging station corresponds to user to electric car；
S3. the quantity of any time electric car in charging station is calculated according to the attraction, and is currently filled at this
The probability that electric car in power station leaves；
S4. the charging load of charging station is calculated by Monte Carlo method.
In above scheme, by during the load prediction of charging station, introducing different factors to electric car to application
Family carries out the influence of charging station selection, and carries out load prediction for multiple charging stations in described ground point range, thus
Obtain more accurate charging station load prediction results.
Preferably, different factors described in step S2 include electric car at a distance from charging station, around charging station
Hotel's distribution around school's distribution, charging station around shop distribution, charging station.In this preferred embodiment, due to these because
Element is closely bound up with the interests of automobile user and daily life, will have a direct impact on selection of the user for charging station, from
And influence the load prediction results of charging station.
Preferably, the step S2 is specifically included:
S21. in described ground point range, electric car is obtained at a distance from each charging station, obtains distance parameter D are as follows:
Wherein i indicates that i-th of charging station, n indicate n-th electric car, Di,nIndicate i-th of charging station and n-th it is electronic
The distance of automobile；Wherein i, n are positive integer；
S22. in described ground point range, the shop distribution obtained around each charging station corresponds to user to each electric car
Impact factor, then the shop distribution around charging station is to the impact factor F of electric car are as follows:
Wherein i indicates the shop distribution around i-th of charging station, and n indicates n-th electric car, Fi,nIt indicates to fill for i-th
Shop distribution around power station corresponds to the impact factor of user to n-th electric car；Wherein i, n are positive integer；
S23. in described ground point range, the school's distribution obtained around each charging station corresponds to user to each electric car
Impact factor, then school's distribution around charging station is to the impact factor S of electric car are as follows:
Wherein i indicates school's distribution around i-th of charging station, and n indicates n-th electric car, Si,nIt indicates to fill for i-th
School's distribution around power station corresponds to the impact factor of user to n-th electric car；Wherein i, n are positive integer；
S24. in described ground point range, the hotel's distribution obtained around each charging station corresponds to user to each electric car
Impact factor, then hotel's distribution around each charging station is to the impact factor H of electric car are as follows:
Wherein i indicates hotel's distribution around i-th of charging station, and n indicates n-th electric car, Hi,nIt indicates to fill for i-th
Hotel's distribution around power station corresponds to the impact factor of user to n-th electric car；Wherein i, n are positive integer；
S25. the attraction a that each charging station corresponds to user to each electric car is calculatedi,n, calculation formula are as follows:
Wherein ai,nThe attraction of user is corresponded to n-th electric car for i-th of charging station；Wherein i indicates to fill for i-th
Power station, n indicate that n-th electric car corresponds to user, and K is preset nonnegative constant.
In this preferred embodiment, each impact factor and K value are artificially provided according to the actual situation, use the table of random numbers
Show；K takes 2 in the present invention.
Preferably, the step S3 is specifically included:
S31. according to the attraction ai,nCalculate the quantity of any time electric car in charging station:
WhereinIndicate the quantity of any time t electric car in charging station,Indicate the t-1 moment in charging station
The quantity of electric car；PφFor preset probability threshold value, when leaving probability Pt-1When greater than this value, electric car selection is left, no
Then electric car selection leaves；Situation is left for recording electric car, 1 expression electric car selection is left, and 0 indicates electricity
Electrical automobile selection leaves；εb,nIt is attracted situation for recording automobile user, indicates b-th of charging station to n-th of electronic vapour
The attraction at automobile-used family is maximum；WhereinInitial value be preset assumption value；
S32. the probability that the electric car currently in the charging station leaves is calculated:
Wherein PtFor the probability that electric car of the t moment in the charging station leaves, lt-1Troop head is lined up for the t-1 moment
Degree, r is preset value, indicates that weather and other factors correspond to the influence of user, n to electric car2Indicate filling in the charging station
Electric stake quantity.In this preferred embodiment, r random value in (0.3,0.5).
Preferably, the step S4 specifically:
WhereinIndicate t moment charging station charge power size；PdFor preset value, indicate that electric car charge power is big
It is small；Indicate the quantity of current time t electric car in charging station；n2Indicate the charging pile quantity in the charging station.
Compared with prior art, the beneficial effect of technical solution of the present invention is:
The method of the present invention corresponds to user to electric car by during the load prediction of charging station, introducing different factors
The influence for carrying out charging station selection, since the interests and daily life of these factors and automobile user are closely bound up, meeting
Selection of the user for charging station is directly affected, that is, influences the charging load of charging station；Simultaneously in described ground point range
Multiple charging stations carry out load prediction, to obtain more accurate charging station load prediction results.
Charging station load forecasting method accuracy rate based on user's more options of the invention is higher, and it is negative to solve existing charging
The problem of lotus does not combine multiple influence factors during predicting.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention.
Fig. 2 is the charging station distribution schematic diagram of the targeted user's more options of the present invention.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
In order to better illustrate this embodiment, the certain components of attached drawing have omission, zoom in or out, and do not represent actual product
Size；
To those skilled in the art, it is to be understood that certain known features and its explanation, which may be omitted, in attached drawing
's.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
Embodiment 1
A kind of charging station load forecasting method based on user's more options, applied to scene as shown in Figure 2, this method packet
Include following steps as shown in Figure 1:
S1. it determines the ground point range predicted, obtain the charging station in described ground point range and need to charge electronic
The relevant parameter of automobile, the relevant parameter include the quantity of charging station, filling in each charging station in described ground point range
Electric stake quantity, the quantity of electric car；
S2. in described ground point range, different factors are obtained, the shadow that user carries out charging station selection is corresponded to electric car
It rings, and calculates the attraction a that each charging station corresponds to user to electric cari,n；
S3. according to the attraction ai,nThe quantity of any time electric car in charging station is calculated, and currently at this
The probability that electric car in charging station leaves；
S4. the charging load of charging station is calculated by Monte Carlo method.
Embodiment 2
A kind of charging station load forecasting method based on user's more options, applied to scene as shown in Figure 2, this method packet
Include following steps:
S1. it determines the ground point range predicted, obtain the charging station in described ground point range and need to charge electronic
The relevant parameter of automobile, the relevant parameter include the quantity of charging station, filling in each charging station in described ground point range
Electric stake quantity, the quantity of electric car；
S2. in described ground point range, different factors are obtained, the shadow that user carries out charging station selection is corresponded to electric car
It rings, and calculates the attraction a that each charging station corresponds to user to electric cari,n；
Specific steps therein include:
S21. in described ground point range, electric car is obtained at a distance from each charging station, obtains distance parameter D are as follows:
Wherein i indicates that i-th of charging station, n indicate n-th electric car, Di,nIndicate i-th of charging station and n-th it is electronic
The distance of automobile；Wherein i, n are positive integer；
S22. in described ground point range, the shop distribution obtained around each charging station corresponds to user to each electric car
Impact factor, then the shop distribution around charging station is to the impact factor F of electric car are as follows:
Wherein i indicates the shop distribution around i-th of charging station, and n indicates n-th electric car, Fi,nIt indicates to fill for i-th
Shop distribution around power station corresponds to the impact factor of user to n-th electric car；If there is no shop around charging station, F
=0, wherein i, n are positive integer；
S23. in described ground point range, the school's distribution obtained around each charging station corresponds to user to each electric car
Impact factor, then school's distribution around charging station is to the impact factor S of electric car are as follows:
Wherein i indicates school's distribution around i-th of charging station, and n indicates n-th electric car, Si,nIt indicates to fill for i-th
School's distribution around power station corresponds to the impact factor of user to n-th electric car；If there is no school around charging station, S
=0, wherein i, n are positive integer；
S24. in described ground point range, the hotel's distribution obtained around each charging station corresponds to user to each electric car
Impact factor, then hotel's distribution around each charging station is to the impact factor H of electric car are as follows:
Wherein i indicates hotel's distribution around i-th of charging station, and n indicates n-th electric car, Hi,nIt indicates to fill for i-th
Hotel's distribution around power station corresponds to the impact factor of user to n-th electric car, if there is no hotel around charging station, H
=0, wherein i, n are positive integer；
In the present embodiment 2, shop, school, hotel's distribution around each charging station refer both to have an area of 200 meters in each charging station
Within shop, school, hotel.Since the distance can be determined according to the actual situation, the present invention is not restricted it；
S25. the attraction a that each charging station corresponds to user to each electric car is calculatedi,n, calculation formula are as follows:
Wherein ai,nThe attraction of user is corresponded to n-th electric car for i-th of charging station；Wherein i indicates to fill for i-th
Power station, n indicate that n-th electric car corresponds to user, and K is preset nonnegative constant, and K value is artificially limited according to the actual situation
Fixed, in the present embodiment 2, the value of K takes 2.
S3. according to the attraction ai,nThe quantity of any time electric car in charging station is calculated, and currently at this
The probability that electric car in charging station leaves；
Specific steps therein include:
S31. according to the attraction ai,nCalculate the quantity of any time electric car in charging station:
WhereinIndicate the quantity of any time t electric car in charging station,Indicate the t-1 moment in charging station
The quantity of electric car；PφFor preset probability threshold value, when leaving probability Pt-1When greater than this value, electric car selection is left, no
Then electric car selection leaves；Situation is left for recording electric car, 1 expression electric car selection is left, and 0 indicates electricity
Electrical automobile selection leaves；εb,nIt is attracted situation for recording automobile user, indicates b-th of charging station to n-th of electronic vapour
The attraction at automobile-used family is maximum；WhereinInitial value be preset assumption value；
S32. the probability that the electric car currently in the charging station leaves is calculated:
Wherein PtFor the probability that electric car of the t moment in the charging station leaves, lt-1Troop head is lined up for the t-1 moment
Degree, r is preset value, indicates that weather and other factors correspond to the influence of user, n to electric car2Indicate filling in the charging station
Electric stake quantity, n2It is obtained from step S1.
S4. the charging load of charging station is calculated by Monte Carlo method:
WhereinIndicate t moment charging station charge power size；PdFor preset value, indicate that electric car charge power is big
It is small；Indicate the quantity of current time t electric car in charging station；n2Indicate the charging pile quantity in the charging station.
The terms describing the positional relationship in the drawings are only for illustration, should not be understood as the limitation to this patent；
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (5)
1. a kind of charging station load forecasting method based on user's more options, which comprises the following steps:
S1. it determines the ground point range predicted, obtains charging station and the electric car that need to be charged in described ground point range
Relevant parameter, the relevant parameter includes the quantity of charging station, the charging pile in each charging station in described ground point range
Quantity, the quantity of electric car；
S2. in described ground point range, different factors are obtained, the influence that user carries out charging station selection is corresponded to electric car, and
Calculate the attraction that each charging station corresponds to user to electric car；
S3. the quantity of any time electric car in charging station is calculated according to the attraction, and in the charging station
The probability that electric car leaves；
S4. the charging load of charging station is calculated by Monte Carlo method.
2. charging station load forecasting method according to claim 1, which is characterized in that different factors described in step S2
School's distribution around shop distribution, charging station including electric car at a distance from charging station, around charging station, charging station week
The hotel's distribution enclosed.
3. charging station load forecasting method according to claim 2, which is characterized in that the step S2 is specifically included:
S21. in described ground point range, electric car is obtained at a distance from each charging station, obtains distance parameter D are as follows:
Wherein i indicates that i-th of charging station, n indicate n-th electric car, Di,nIndicate i-th of charging station and n-th electric car
Distance；Wherein i, n are positive integer；
S22. in described ground point range, the shadow that the shop distribution around each charging station corresponds to user to each electric car is obtained
The factor is rung, then impact factor F of the shop distribution around charging station to electric car are as follows:
Wherein i indicates the shop distribution around i-th of charging station, and n indicates n-th electric car, Fi,nIndicate i-th of charging station
The shop distribution of surrounding corresponds to the impact factor of user to n-th electric car；Wherein i, n are positive integer；
S23. in described ground point range, the shadow that school's distribution around each charging station corresponds to user to each electric car is obtained
The factor is rung, then impact factor S of the school's distribution around charging station to electric car are as follows:
Wherein i indicates school's distribution around i-th of charging station, and n indicates n-th electric car, Si,nIndicate i-th of charging station
School's distribution of surrounding corresponds to the impact factor of user to n-th electric car；Wherein i, n are positive integer；
S24. in described ground point range, the shadow that hotel's distribution around each charging station corresponds to user to each electric car is obtained
The factor is rung, then impact factor H of the hotel's distribution around each charging station to electric car are as follows:
Wherein i indicates hotel's distribution around i-th of charging station, and n indicates n-th electric car, Hi,nIndicate i-th of charging station
Hotel's distribution of surrounding corresponds to the impact factor of user to n-th electric car；Wherein i, n are positive integer；
S25. the attraction a that each charging station corresponds to user to each electric car is calculatedi,n, calculation formula are as follows:
Wherein ai,nThe attraction of user is corresponded to n-th electric car for i-th of charging station；Wherein i indicates i-th of charging station,
N indicates that n-th electric car corresponds to user, and K is preset nonnegative constant.
4. charging station load forecasting method according to claim 3, which is characterized in that the step S3 is specifically included:
S31. according to the attraction ai,nCalculate the quantity of any time electric car in charging station:
WhereinIndicate the quantity of any time t electric car in charging station,Indicate that the t-1 moment is electronic in charging station
The quantity of automobile；PφFor preset probability threshold value, when leaving probability Pt-1Greater than PφWhen, electric car selection is left, otherwise electronic
Automobile selection leaves；Situation is left for recording electric car, 1 expression electric car selection is left, and 0 indicates electric car
Selection leaves；εb,nIt is attracted situation for recording automobile user, indicates b-th of charging station to n-th of automobile user
Attraction it is maximum；WhereinInitial value be preset assumption value；
S32. the probability that the electric car in the charging station leaves is calculated:
Wherein PtFor the probability that electric car of the t moment in the charging station leaves, lt-1Troop's length is lined up for the t-1 moment；R is
Preset value indicates that weather and other factors correspond to the influence of user to electric car；n2Indicate the charging pile number in the charging station
Amount.
5. charging station load forecasting method according to claim 4, which is characterized in that the step S4 specifically:
WhereinIndicate t moment charging station charge power size；PdFor preset value, electric car charge power size is indicated；Table
Show the quantity of t moment electric car in charging station；n2Indicate the charging pile quantity in the charging station.
Priority Applications (1)
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| CN201910350903.5A CN110119841B (en) | 2019-04-28 | 2019-04-28 | Charging station load prediction method based on multiple choices of users |
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| CN112348591A (en) * | 2020-11-23 | 2021-02-09 | 南方电网科学研究院有限责任公司 | Ordered charging control method and device for electric automobile |
| CN113326883A (en) * | 2021-06-03 | 2021-08-31 | 中创三优（北京）科技有限公司 | Training method, device and medium for power utilization rate prediction model of charging station |
| CN116691413A (en) * | 2023-07-31 | 2023-09-05 | 国网浙江省电力有限公司 | Advanced vehicle-mounted dynamic load pre-configuration method and ordered charging system |
| CN117833240A (en) * | 2024-02-29 | 2024-04-05 | 江苏米特物联网科技有限公司 | Hotel scene-oriented electric automobile charging load prediction method |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112348591A (en) * | 2020-11-23 | 2021-02-09 | 南方电网科学研究院有限责任公司 | Ordered charging control method and device for electric automobile |
| CN113326883A (en) * | 2021-06-03 | 2021-08-31 | 中创三优（北京）科技有限公司 | Training method, device and medium for power utilization rate prediction model of charging station |
| CN113326883B (en) * | 2021-06-03 | 2022-08-30 | 中创三优（北京）科技有限公司 | Training method, device and medium for power utilization rate prediction model of charging station |
| CN116691413A (en) * | 2023-07-31 | 2023-09-05 | 国网浙江省电力有限公司 | Advanced vehicle-mounted dynamic load pre-configuration method and ordered charging system |
| CN116691413B (en) * | 2023-07-31 | 2023-10-20 | 国网浙江省电力有限公司 | Advanced vehicle-mounted dynamic load pre-configuration method and ordered charging system |
| CN117833240A (en) * | 2024-02-29 | 2024-04-05 | 江苏米特物联网科技有限公司 | Hotel scene-oriented electric automobile charging load prediction method |
| CN117833240B (en) * | 2024-02-29 | 2024-05-31 | 江苏米特物联网科技有限公司 | Hotel scene-oriented electric automobile charging load prediction method |
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