CN110990781A - Electric vehicle charging load prediction method and system and storage medium - Google Patents

Electric vehicle charging load prediction method and system and storage medium Download PDF

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CN110990781A
CN110990781A CN201911082682.4A CN201911082682A CN110990781A CN 110990781 A CN110990781 A CN 110990781A CN 201911082682 A CN201911082682 A CN 201911082682A CN 110990781 A CN110990781 A CN 110990781A
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孙淑霞
李兰哲
崔永
张效声
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Shenzhen Power Supply Co ltd
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Abstract

The invention provides a method for predicting charging load of an electric automobile, a system and a storage medium thereof, wherein the method comprises the following steps: calculating a load curve of each sub-model; each sub-model comprises a plurality of electric automobile types, and each electric automobile type comprises the number of corresponding electric automobiles, battery capacity, charging power and charging efficiency; and accumulating the load curves of all the submodels to obtain an integrated total charging load curve of the electric automobile. By implementing the invention, the accuracy of the charging load prediction can be improved.

Description

Electric vehicle charging load prediction method and system and storage medium
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a method and a system for predicting charging load of an electric vehicle and a storage medium.
Background
Electric automobiles are a clean energy vehicle, and have been rapidly developed and applied in recent years. The large-scale electric automobile is connected to a power grid for charging at the same time, and the stable operation of a power system is possibly influenced. Therefore, accurately predicting the charging load of the electric automobile is a key basic problem for solving the influence of large-scale development of the electric automobile on a power grid system and improving the configuration efficiency of the power grid, and is also beneficial to optimizing operation and improving benefits of an electric automobile charging station.
The existing load prediction method based on probability statistics is that a probability statistical model of each load influence factor is established at first, the initial charging time and the initial state of charge (SOC) of a battery are obtained through sampling, the charging time is calculated, and then the loads of all electric vehicles are accumulated in the time period to obtain a load prediction result. However, since the number of electric vehicles in a certain area is limited, it is difficult to satisfy the condition that the number of samples is sufficiently large; and the probability model sampling has larger randomness, so that the prediction result obtained by accumulation has deviation from the actual condition, and the accuracy of the charging load prediction is seriously influenced.
Disclosure of Invention
The invention aims to provide a method, a system and a storage medium for predicting the charging load of an electric vehicle so as to improve the accuracy of the charging load prediction.
In a first aspect, an embodiment of the present invention provides a method for predicting a charging load of an electric vehicle, including:
calculating a load curve of each sub-model; each sub-model comprises a plurality of electric automobile types, and each electric automobile type comprises the number of corresponding electric automobiles, battery capacity, charging power and charging efficiency;
and accumulating the load curves of all the submodels to obtain an integrated total charging load curve of the electric automobile.
Wherein the calculating the load curve of each sub-model comprises:
calculating a load curve corresponding to each electric vehicle type;
and accumulating the load curves corresponding to all the electric automobile types in the sub-model to obtain the load curve of the sub-model.
Wherein, the calculating the load curve corresponding to each electric automobile type comprises the following steps:
calculating a load curve corresponding to each electric automobile according to the battery capacity, the charging power and the charging efficiency corresponding to each electric automobile;
and accumulating the load curves of all the electric automobiles of each type to obtain the load curve corresponding to each type.
The load curve corresponding to each electric vehicle calculated according to the battery capacity, the charging power and the charging efficiency corresponding to each electric vehicle is specifically as follows:
s1, obtaining the electric automobile charging time t of each electric automobilenAnd initial state of charge SOC of the batteryn；
S2, calculating the electric vehicle charging duration time of each electric vehicle according to the following formula:
T＝(1-SOCn).C/(η.P)
wherein C is the battery capacity of the electric automobile, P is the charging power of the electric automobile, and η is the charging efficiency of the electric automobile;
s3, charging time t of each electric automobile according to electric automobilenAnd generating a corresponding load curve by the electric vehicle charging power P and the electric vehicle charging duration T.
The method for accumulating the load curves of all the submodels to obtain the integrated total charging load curve of the electric automobile comprises the following steps:
accumulating the load curves of all the submodels to obtain an accumulated result;
and averaging the accumulated result and outputting an integrated total charging load curve of the electric automobile.
In a second aspect, an embodiment of the present invention provides an electric vehicle charging load prediction system, including:
the submodel calculation module is used for calculating a load curve of each submodel; each sub-model comprises a plurality of electric automobile types, and each electric automobile type comprises the number of corresponding electric automobiles, battery capacity, charging power and charging efficiency;
and the submodel accumulation module is used for accumulating the load curves of all the submodels to obtain an integrated total charging load curve of the electric automobile.
Wherein the calculation module comprises:
the automobile type calculating unit is used for calculating a load curve corresponding to each electric automobile type;
and the automobile type accumulation unit is used for accumulating the load curves corresponding to all the electric automobile types in the sub-model to obtain the load curve of the sub-model.
Wherein the car type calculation unit includes:
the automobile load calculation unit is used for calculating a corresponding load curve according to the battery capacity, the charging power and the charging efficiency corresponding to each electric automobile;
and the automobile load accumulation unit is used for accumulating the load curves of all the electric automobiles of each type to obtain the load curve corresponding to each type.
Wherein the car load calculation unit includes:
a data acquisition unit for acquiring the charging time t of each electric vehiclenAnd initial state of charge SOC of the batteryn；
A charging time calculation unit for calculating (1-SOC) according to the formula Tn) Calculating the charging duration time of each electric vehicle according to C/(η. P), wherein C is the battery capacity of the electric vehicle, P is the charging power of the electric vehicle, and η is the charging efficiency of the electric vehicle;
a load curve generation unit for generating a load curve according to the electric vehicle charging time t of each electric vehiclenAnd generating a corresponding load curve by the electric vehicle charging power P and the electric vehicle charging duration T.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the electric vehicle charging load prediction method according to the embodiment.
The embodiment of the invention provides a method, a system and a storage medium for predicting charging load of an electric vehicle. This process necessarily leads to uncertainty, i.e. multiple predictions using the same model, the load curve obtained each time is necessarily different. The embodiment of the invention integrates a plurality of submodels and averages the load curve, thereby effectively eliminating the uncertainty introduced by each submodel in the sampling process. Secondly, the prediction precision is improved by increasing the sampling number, and the embodiment of the invention effectively breaks through the limit of the number of the charging automobiles in the existing area by an integrated means, namely, the number of the electric automobile samples is greatly increased, so that the electric automobile samples can better meet the condition of the Monte Carlo's theorem, and the method can obtain a more accurate prediction result theoretically.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for predicting a charging load of an electric vehicle according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a method for predicting a charging load of an electric vehicle according to a second embodiment of the present invention.
Fig. 3 is a schematic view of a sub-model electric vehicle load curve calculation process according to the second embodiment of the invention.
Fig. 4 is a schematic diagram of a framework of a system for predicting a charging load of an electric vehicle according to a third embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures closely related to the solution according to the present invention are shown in the drawings, and other details not closely related to the present invention are omitted.
Example one
The embodiment of the invention provides a method for predicting charging load of an electric vehicle, fig. 1 is a schematic flow chart of the method, and referring to fig. 1, the method in the embodiment comprises the following steps:
step S101, calculating a load curve of each sub-model; each sub-model comprises a plurality of electric automobile types, and each electric automobile type comprises the number of corresponding electric automobiles, battery capacity, charging power and charging efficiency;
and S102, accumulating the load curves of all the submodels to obtain an integrated total charging load curve of the electric automobile.
Wherein the step S101 includes:
step S201, calculating a load curve corresponding to each type of the electric automobile;
and S202, accumulating the load curves corresponding to all the electric automobile types in the sub-model to obtain the load curve of the sub-model.
Wherein the step S201 includes:
step S301, calculating a corresponding load curve according to the battery capacity, the charging power and the charging efficiency corresponding to each electric automobile;
and step S302, accumulating the load curves of all the electric automobiles of each type to obtain a load curve corresponding to each type.
Wherein the step S301 includes:
step S401, obtaining the electric vehicle charging time t of each electric vehiclenAnd initial state of charge SOC of the batteryn；
Step S402, calculating the electric vehicle charging duration time of each electric vehicle according to the following formula:
T＝(1-SOCn).C/(η.P)
wherein C is the battery capacity of the electric automobile, P is the charging power of the electric automobile, and η is the charging efficiency of the electric automobile;
step S403, according to the electric automobile charging time t of each electric automobilenAnd generating a corresponding load curve by the electric vehicle charging power P and the electric vehicle charging duration T.
Wherein the step S102 includes:
step S501, accumulating the load curves of all sub models to obtain an accumulation result;
and S502, averaging the accumulated result and outputting an integrated total charging load curve of the electric automobile.
Example two
The second embodiment is a specific embodiment of the method described in the second embodiment, and as shown in fig. 2, a flow diagram of the method in the second embodiment is shown, and as can be seen from fig. 2, given M sub-models, load curves of each sub-model are respectively calculated and generated, and finally, the load curves of the M sub-models are accumulated and integrated to obtain a total charging load curve of the electric vehicle. The larger the value of M, the better, but the larger the number of M, the more complicated the calculation, and usually 15-20 is desirable.
Fig. 3 is a schematic diagram of a specific process of calculating a load curve of a sub-model according to a second embodiment, and a detailed description is given to a process of the second embodiment with reference to fig. 3:
step 601, inputting basic information of the electric vehicles, wherein the basic information of the electric vehicles refers to the types of the electric vehicles, and each type of the electric vehicles comprises the number, the battery capacity, the charging power and the charging efficiency of the corresponding electric vehicles. In the embodiment, the electric automobile is divided into three types of calculation, namely electric bus, electric private car and electric taxi. The battery capacity, the charging power and the charging efficiency of each type are valued according to certain urban statistical data as follows:
electric bus: the battery capacity is 324kWh, the quick charging is adopted, the charging power is 90kW, and the charging efficiency is 90%;
electric private car: the battery capacity is 45kWh, the conventional charging is adopted, the charging power is 8kW, and the charging efficiency is 90%;
electric taxi: the battery capacity is 45kWh, the rapid charging is adopted, the charging power is 32kW, and the charging efficiency is 90%.
Step 602, in the iterative calculation process of the electric vehicle, setting the number n of the electric vehicle of the current type to 1.
Step 603, acquiring the charging time and the initial charge state of the battery, and generating a current charging load curve of the electric vehicle.
Firstly, the charging time t is respectively extracted from an initial charging time probability model and a battery initial state of charge (SOC) probability model corresponding to the electric automobilen(unit, minute), initial state of charge SOC of the batterynThen, the charging duration of the electric vehicle is calculated according to the following formula:
T＝(1-SOCn).C/(η.P)
wherein C is the battery capacity of the type of electric automobile, P is the charging power of the type of electric automobile, η is the charging efficiency of the type of electric automobile, then [ tn,tn+T]And charging the electric automobile in a charging period, wherein the charging power is the power P of the electric automobile.
The initial charging time probability model of the three different types of electric vehicles is set as follows according to statistical data of a certain city in the embodiment of the invention:
electric bus: because the bus cannot meet the operation requirement by charging once a day, the bus has two charging periods, and the charging period is 1440 minutes from one dayRespectively obey normal distribution N (840,100)2) And N (1380,100)2) Normal distribution of (2);
electric private car: the initial charging time approximately obeys N (1150,100)2) Normal distribution of (2);
electric taxi: different with electronic private car, the electric taxi all-day mileage of traveling is longer, needs to charge many times, and it satisfies the normal distribution of segmentation, totally four periods: n (231,102)2)，N(708,822)，N(1004,552) And N (1290, 55)2)。
The initial state of charge SOC probability models of the batteries of the three different types of electric vehicles in the embodiment of the invention are respectively set as follows according to certain urban statistical data:
the SOC of the bus follows normal distribution N (0.5, 0.1)2)；
The SOC of the private car follows normal distribution N (0.6, 0.1)2)；
Taxi SOC obeys normal distribution N (0.3, 0.1)2)。
After the calculation is finished, according to the calculated charging time t of the current electric automobilenAnd generating a load curve corresponding to the current electric automobile by the electric automobile charging power P and the electric automobile charging duration T.
Step S604, accumulating the load curve with the previous load curve to obtain a charging load curve of the current iteration number n
Figure BDA0002264448900000081
Figure BDA0002264448900000082
Wherein,
Figure BDA0002264448900000083
1440, P is the total charging power for the current i-th minute, i ═ 1,2n,iIndicating the charging power of the nth car in the ith minute.
Step S605, in the iteration process, comparing n with the maximum value nmaxIf n is equal to nmaxAnd if the maximum number of the type of electric vehicles in a certain area is reached, the load curve accumulation of one type of electric vehicle is completed, otherwise, n is equal to n +1, and the step S603 is returned.
Step S606, if l ═ lmax(in this examplemax3, namely three types of electric vehicles), namely, completing the calculation of the load curves of all types of electric vehicles, and adding the load curves to obtain the total charging load curve L of all electric vehiclestotal. Otherwise, return to step S602.
Step S607, the total charging load curve of the electric automobile obtained by the M submodels is accumulated according to the following formula:
Figure BDA0002264448900000084
and step S608, averaging the accumulated result according to the following formula, and outputting a final integrated electric vehicle total charging load curve.
Figure BDA0002264448900000085
EXAMPLE III
An embodiment of the present invention provides a system for predicting a charging load of an electric vehicle, where fig. 4 is a schematic diagram of a framework of the system according to the third embodiment, and referring to fig. 4, the system includes:
the submodel calculating module 1 is used for calculating a load curve of each submodel; each sub-model comprises a plurality of electric automobile types, and each electric automobile type comprises the number of corresponding electric automobiles, battery capacity, charging power and charging efficiency;
and the submodel accumulation module 2 is used for accumulating the load curves of all the submodels to obtain an integrated total charging load curve of the electric automobile.
Wherein the submodel calculation module comprises:
the automobile type calculating unit 11 is used for calculating a load curve corresponding to each electric automobile type;
and the automobile type accumulation unit 12 is used for accumulating the load curves corresponding to all the electric automobile types in the sub-model to obtain the load curve of the sub-model.
Wherein the car type calculation unit 11 includes:
the automobile load calculation unit 111 is used for calculating a load curve corresponding to each electric automobile according to the battery capacity, the charging power and the charging efficiency corresponding to each electric automobile;
and the vehicle load accumulation unit 112 is configured to accumulate the load curves of all the electric vehicles of each type to obtain a load curve corresponding to each type.
Wherein the car load calculation unit 111 includes:
a data acquisition unit for acquiring the charging time t of each electric vehiclenAnd initial state of charge SOC of the batteryn；
A charging time calculation unit for calculating (1-SOC) according to the formula Tn) Calculating the charging duration time of each electric vehicle according to C/(η. P), wherein C is the battery capacity of the electric vehicle, P is the charging power of the electric vehicle, and η is the charging efficiency of the electric vehicle;
a load curve generation unit for generating a load curve according to the electric vehicle charging time t of each electric vehiclenAnd generating a corresponding load curve by the electric vehicle charging power P and the electric vehicle charging duration T.
Wherein, the submodel accumulation module 2 comprises:
the accumulation processing unit 21 is configured to accumulate the load curves of all the submodels to obtain an accumulation result;
and the average processing unit 22 is configured to output an integrated electric vehicle total charging load curve after averaging the accumulated result.
It should be noted that the system described in the third embodiment corresponds to the method described in the first embodiment, and therefore, the parts of the system described in the third embodiment that are not described in detail can be obtained by referring to the method described in the first embodiment. In this embodiment, the division of the functional modules of the system described in the third embodiment does not constitute a limitation of the division of the physical modules, and is a system framework corresponding to the method described in the first embodiment, and a plurality of functional modules can be integrated into one functional module to implement a plurality of functions.
It is to be noted that, based on the content, those skilled in the art can clearly understand that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to implement the method/system according to the embodiments of the present invention.
Example four
The fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for predicting a charging load of an electric vehicle according to the first embodiment of the present invention.
As can be seen from the description of the above embodiments, the embodiment of the present invention provides a method for predicting a charging load of an electric vehicle, a system thereof, and a storage medium thereof, and firstly, uncertainty in a random sampling process of a conventional method is eliminated through model integration, because the conventional method generates samples through random sampling of an influence factor statistical model. This process necessarily leads to uncertainty, i.e. multiple predictions using the same model, the load curve obtained each time is necessarily different. The embodiment of the invention integrates a plurality of submodels and averages the load curve, thereby effectively eliminating the uncertainty introduced by each submodel in the sampling process. Secondly, the prediction precision is improved by increasing the sampling number, and the embodiment of the invention effectively breaks through the limit of the number of the charging automobiles in the existing area by an integrated means, namely, the number of the electric automobile samples is greatly increased, so that the electric automobile samples can better meet the condition of the Monte Carlo's theorem, and the method can obtain a more accurate prediction result theoretically.
The foregoing is directed to embodiments of the present invention, and it is understood that various modifications and improvements can be made by those skilled in the art without departing from the spirit of the invention.

Claims (10)

1. A method for predicting charging load of an electric vehicle is characterized by comprising the following steps:
calculating a load curve of each sub-model; each sub-model comprises a plurality of electric automobile types, and each electric automobile type comprises the number of corresponding electric automobiles, battery capacity, charging power and charging efficiency;
and accumulating the load curves of all the submodels to obtain an integrated total charging load curve of the electric automobile.
2. The method for predicting the charging load of the electric vehicle according to claim 1, wherein the calculating the load curve of each submodel comprises:
calculating a load curve corresponding to each electric vehicle type;
and accumulating the load curves corresponding to all the electric automobile types in the sub-model to obtain the load curve of the sub-model.
3. The method for predicting the charging load of the electric vehicle according to claim 2, wherein the calculating the load curve corresponding to each electric vehicle type comprises:
calculating a load curve corresponding to each electric automobile according to the battery capacity, the charging power and the charging efficiency corresponding to each electric automobile;
and accumulating the load curves of all the electric automobiles of each type to obtain the load curve corresponding to each type.
4. The method for predicting the charging load of the electric vehicle according to claim 3, wherein the load curve corresponding to each electric vehicle calculated according to the battery capacity, the charging power and the charging efficiency corresponding to the electric vehicle is specifically as follows:
s1, obtaining the electric automobile charging time t of each electric automobilenAnd initial state of charge SOC of the batteryn；
S2, calculating the electric vehicle charging duration time of each electric vehicle according to the following formula:
T＝(1-SOCn).C/(η.P)
wherein C is the battery capacity of the electric automobile, P is the charging power of the electric automobile, and η is the charging efficiency of the electric automobile;
s3, charging time t of each electric automobile according to electric automobilenAnd generating a corresponding load curve by the electric vehicle charging power P and the electric vehicle charging duration T.
5. The method for predicting the charging load of the electric vehicle as claimed in claim 3, wherein the step of accumulating the load curves of all the submodels to obtain an integrated total charging load curve of the electric vehicle comprises the following steps:
accumulating the load curves of all the submodels to obtain an accumulated result;
and averaging the accumulated result and outputting an integrated total charging load curve of the electric automobile.
6. An electric vehicle charging load prediction system, comprising:
the submodel calculation module is used for calculating a load curve of each submodel; each sub-model comprises a plurality of electric automobile types, and each electric automobile type comprises the number of corresponding electric automobiles, battery capacity, charging power and charging efficiency;
and the submodel accumulation module is used for accumulating the load curves of all the submodels to obtain an integrated total charging load curve of the electric automobile.
7. The system of claim 6, wherein the calculation module comprises:
the automobile type calculating unit is used for calculating a load curve corresponding to each electric automobile type;
and the automobile type accumulation unit is used for accumulating the load curves corresponding to all the electric automobile types in the sub-model to obtain the load curve of the sub-model.
8. The electric vehicle charging load prediction method according to claim 7, wherein the vehicle type calculation unit includes:
the automobile load calculation unit is used for calculating a corresponding load curve according to the battery capacity, the charging power and the charging efficiency corresponding to each electric automobile;
and the automobile load accumulation unit is used for accumulating the load curves of all the electric automobiles of each type to obtain the load curve corresponding to each type.
9. The electric vehicle charging load prediction method according to claim 8, wherein the vehicle load calculation unit includes:
a data acquisition unit for acquiring the charging time t of each electric vehiclenAnd initial state of charge SOC of the batteryn；
A charging time calculation unit for calculating (1-SOC) according to the formula Tn) Calculating the charging duration time of each electric vehicle according to C/(η. P), wherein C is the battery capacity of the electric vehicle, P is the charging power of the electric vehicle, and η is the charging efficiency of the electric vehicle;
a load curve generation unit for generating a load curve according to the electric vehicle charging time t of each electric vehiclenAnd generating a corresponding load curve by the electric vehicle charging power P and the electric vehicle charging duration T.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements the steps of the method for predicting charging load of an electric vehicle according to any one of claims 1 to 5.
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Cited By (3)

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CN111626514A (en) * 2020-05-29 2020-09-04 深圳供电局有限公司 Electric vehicle charging load prediction method and device
CN113627661A (en) * 2021-08-02 2021-11-09 深圳供电局有限公司 Method for predicting charging load of electric automobile
CN116699412A (en) * 2023-05-17 2023-09-05 盐城工学院 Residual capacity estimation method of energy storage battery module

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