CN117010930A - Electric automobile charging guiding method and device, electronic equipment and storage medium - Google Patents
Electric automobile charging guiding method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application provides an electric automobile charging guiding method, an electric automobile charging guiding device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring charging behavior information of an electric automobile user, wherein the charging behavior information comprises station load analysis information and a user charging behavior image; determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and yard load analysis information; carrying out daily offer processing on the electric automobile user according to the yard load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price. According to the scheme of the embodiment of the application, the day-ahead offer price is set for the user in the day-ahead stage, and the user of the electric automobile is invited to charge; in the daytime, a daily guide price is set for a user, and the charging behavior of the electric automobile user is guided, so that the deviation cost of the charging station is reduced.
Description
Technical Field
The embodiment of the application relates to the technical field of power system automation, in particular to an electric automobile charging guiding method, an electric automobile charging guiding device, electronic equipment and a computer readable storage medium.
Background
With the rapid development and popularization of electric vehicles, the demand for electric vehicle charging stations is also increasing. The rapid increase in the number of electric vehicles presents challenges to the charging station operators to ensure that the charging station can be used at any time while still being profitable. One of the main challenges facing charging station operators is managing the load of charging stations. If the charging demand exceeds the capacity of the charging station, customers may not be able to charge their vehicles, resulting in frustration and loss of revenue for the operator. On the other hand, if the charging station is underutilized, the operator may not generate enough revenue to pay the cost. Therefore, it is important for the charging station operator to effectively manage the electric vehicle load of the charging station.
Disclosure of Invention
The embodiment of the application aims to at least solve one of the technical problems existing in the prior art.
Therefore, the embodiment of the application provides an electric vehicle charging guiding method, which sets a day-ahead offer price for a user in a day-ahead stage to invite the electric vehicle user to charge; in the daytime, a daily guide price is set for a user, and the charging behavior of the electric automobile user is guided, so that the deviation cost of the charging station is reduced.
The embodiment of the application also provides a device applying the electric automobile charging guiding method.
The embodiment of the application also provides electronic equipment applying the electric automobile charging guiding method.
The embodiment of the application also provides a computer readable storage medium applying the electric automobile charging guide method.
According to an embodiment of the first aspect of the present application, a method for guiding charging of an electric vehicle includes:
acquiring charging behavior information of an electric automobile user, wherein the charging behavior information comprises station load analysis information and a user charging behavior image;
determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and the yard load analysis information;
performing daily offer processing on the electric automobile user according to the station load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price.
According to some embodiments of the application, the yard load prediction model is obtained by:
acquiring historical charging record information and auxiliary prediction data information of a charging station;
training a preset yard load prediction initial model according to the historical charging record information and the auxiliary prediction data information to obtain a yard load prediction model;
wherein the auxiliary prediction data information includes at least one of: temperature data, weather data, season data, and date type data.
According to some embodiments of the application, the user charging behavior representation is obtained by:
acquiring historical charging order data of a user of a charging station;
and extracting features of the historical charging order data of the user to obtain user charging order features, wherein the user charging order features at least comprise one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charge quantity, charging frequency, charging cost and charging pile type;
and carrying out feature clustering processing on the electric automobile user based on the user charging order feature to obtain the user charging behavior portrait.
According to some embodiments of the application, the determining a day-ahead offer price and a day-in-guide price according to a preset yard load prediction model and the yard load analysis information includes:
obtaining next-day yard load prediction information according to the yard load prediction model and the yard load analysis information;
determining the day-ahead offer price according to a preset price sensitive model, the next-day yard load prediction information and the auxiliary prediction data information;
and determining the daily lead price according to the price sensitive model, the yard load analysis information and the auxiliary prediction data information.
According to some embodiments of the present application, after the performing the daily offer processing on the electric automobile user according to the yard load analysis information, the daily offer price and the user charging behavior portrait, the method further includes:
acquiring electricity utilization starting time, charging ending time and charging quantity of an electric automobile user, and determining charging accumulated offer according to the electricity utilization starting time, the charging ending time and the charging quantity;
and under the condition that the charging accumulated offer is not greater than the total offer load of a charging station, successfully offers to the users of the electric automobile.
According to some embodiments of the present application, the performing daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price includes:
determining a short-term predicted value of the charging station according to the station load analysis information;
and carrying out daily offer processing on the electric automobile user according to the short-term predicted value of the charging station and the daily guide price.
According to some embodiments of the application, the pre-day offer price is less than the day guidance price.
According to a second aspect of the embodiment of the present application, an electric vehicle charging guide device includes:
the first processing module is used for acquiring charging behavior information of the electric automobile user, wherein the charging behavior information comprises station load analysis information and user charging behavior images;
the second processing module is used for determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and the yard load analysis information;
the third processing module is used for carrying out daily offer processing on the electric automobile user according to the station yard load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price.
An electronic device according to an embodiment of a third aspect of the present application includes: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the electric vehicle charging guiding method when executing the computer program.
A computer-readable storage medium according to an embodiment of the fourth aspect of the present application stores computer-executable instructions that, when executed by a control processor, implement the electric vehicle charging guidance method as described above.
The electric automobile charging guiding method provided by the embodiment of the application has at least the following beneficial effects: in the process of conducting electric vehicle charging guidance, firstly, charging behavior information of an electric vehicle user is obtained, wherein the charging behavior information comprises station load analysis information and a user charging behavior image; then determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and yard load analysis information; finally, carrying out daily offer processing on the electric automobile user according to the yard load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price. Through the technical scheme, the day-ahead offer price is set for the user in the day-ahead stage, and the user of the electric automobile is invited to charge; in the daytime, a daily guide price is set for a user, and the charging behavior of the electric automobile user is guided, so that the deviation cost of the charging station is reduced.
Additional features and advantages of embodiments of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of embodiments of the application. The objectives and other advantages of embodiments of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosed embodiments and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain, without limitation, the disclosed embodiments.
Fig. 1 is a flowchart of an electric vehicle charging guiding method according to an embodiment of the present application;
FIG. 2 is a specific flowchart for constructing a yard load prediction model according to one embodiment of the present application;
FIG. 3 is a flowchart showing a user charging behavior image according to an embodiment of the present application;
FIG. 4 is a specific flowchart of S200 provided in one embodiment of the present application;
fig. 5 is a flowchart of an electric vehicle charging guidance method according to another embodiment of the present application;
FIG. 6 is a flowchart showing an embodiment of S300 according to the present application;
fig. 7 is a schematic view of a configuration of an electric vehicle charging guide device according to an embodiment of the present application;
fig. 8 is a schematic diagram of the configuration of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application more apparent, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the embodiment of the application, are intended for purposes of illustration only and are not intended to limit the scope of the application.
In the description of the embodiments of the present application, several means one or more, and plural means two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the embodiments of the present application, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly, and those skilled in the art may reasonably determine the specific meaning of the terms in the embodiments of the present application in combination with the specific contents of the technical solutions.
The embodiment of the application provides an electric automobile charging guiding method, an electric automobile charging guiding device, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: in the process of conducting electric vehicle charging guidance, firstly, charging behavior information of an electric vehicle user is obtained, wherein the charging behavior information comprises station load analysis information and a user charging behavior image; then determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and yard load analysis information; finally, carrying out daily offer processing on the electric automobile user according to the yard load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price. Through the technical scheme, the day-ahead offer price is set for the user in the day-ahead stage, and the user of the electric automobile is invited to charge; in the daytime, a daily guide price is set for a user, and the charging behavior of the electric automobile user is guided, so that the deviation cost of the charging station is reduced.
Embodiments of the present application will be further described below with reference to the accompanying drawings.
As shown in fig. 1, fig. 1 is a flowchart of an electric vehicle charging guidance method according to an embodiment of the present application. The method includes, but is not limited to, step S100, step S200, and step S300.
Step S100, charging behavior information of an electric automobile user is obtained, wherein the charging behavior information comprises station load analysis information and a user charging behavior image;
step S200, determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and yard load analysis information;
step S300, carrying out daily offer processing on the electric automobile user according to the site load analysis information, the daily offer price and the user charging behavior portraits; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price.
In the process of conducting charging guidance of the electric automobile, firstly, charging behavior information of a user of the electric automobile is obtained, wherein the charging behavior information comprises station load analysis information and a charging behavior image of the user; then determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and yard load analysis information; finally, carrying out daily offer processing on the electric automobile user according to the yard load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price. Through the technical scheme, the day-ahead offer price is set for the user in the day-ahead stage, and the user of the electric automobile is invited to charge; in the daytime, a daily guide price is set for a user, and the charging behavior of the electric automobile user is guided, so that the deviation cost of the charging station is reduced.
It is noted that the number of the electric automobile users in the embodiment of the application can be multiple, and the number of the charging stations can be multiple; and combining the plurality of electric automobile users and the plurality of charging stations to perform unified analysis and processing so that the charging stations can meet the charging requirements of the plurality of electric automobile users and reduce the deviation cost of the charging stations.
It is understood that the yard load analysis information may be charging load information of a charging yard; the user charging behavior image can be constructed based on some previous charging behavior information of the electric automobile user, and the image can be understood as a charging behavior prediction model of the electric automobile user.
It is noted that the preset yard load prediction model is the yard load prediction model which has already been trained. The day-ahead offer price is the price of inviting the electric automobile user to charge the electric automobile user in the previous day, and the day-ahead guide price is the price of guiding the electric automobile user to charge the electric automobile user in the current day.
In the process of carrying out daily offer processing on the electric automobile user, the station load analysis information, the daily offer price and the user charging behavior image are required to be combined; in the process of carrying out daily offer processing on electric automobile users, the electric automobile users need to analyze information and daily guide prices according to the site load.
Additionally, in an embodiment, as shown in FIG. 2, constructing the yard load prediction model may include, but is not limited to, step S410 and step S420.
Step S410, historical charging record information and auxiliary prediction data information of a charging station are obtained;
step S420, training a preset yard load prediction initial model according to the historical charging record information and the auxiliary prediction data information to obtain a yard load prediction model; wherein the auxiliary prediction data information includes at least one of: temperature data, weather data, season data, and date type data.
In the process of constructing a yard load prediction model, firstly, acquiring historical charging record information and auxiliary prediction data information of a charging yard; and then training a preset site load prediction initial model according to the historical charging record information and auxiliary prediction data information to obtain a site load prediction model, wherein the auxiliary prediction data information at least comprises one of the following: temperature data, weather data, season data, and date type data.
It can be understood that the temperature data is the temperature data of the charging station; the meteorological data is information such as sunny weather, rainy weather or cloudy weather; the season data is season information, which is spring, summer, autumn and winter respectively; the date type data is information such as holidays.
It is worth noting that the initial model for predicting the yard load is trained by combining the historical charging record information and the related auxiliary prediction data information, so that the obtained yard load prediction model can more accurately predict the subsequent yard load.
Illustratively, the algorithm for constructing the yard load prediction model may be as follows:
historical load data and auxiliary prediction data are imported and data preprocessing is performed, including but not limited to: data cleaning, data fusion, feature selection and extraction, data standardization and normalization, and data division into training sets and test sets. Creating a neural network model and initializing model parameters;
and outputting a daily load prediction result according to the load prediction value of each period.
In addition, in an embodiment, as shown in fig. 3, constructing the user charging behavior image may include, but is not limited to, step S510, step S520, and step S530.
Step S510, acquiring historical charging order data of a user of a charging station;
step S520, extracting features from the historical charging order data of the user to obtain charging order features of the user, where the charging order features of the user at least include one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charge quantity, charging frequency, charging cost and charging pile type;
and step S530, carrying out feature clustering processing on the electric automobile user based on the user charging order feature to obtain a user charging behavior portrait.
In the process of constructing the charging behavior image of the user, firstly, acquiring historical charging order data of the user of a charging station; and then extracting features of the historical charging order data of the user to obtain the charging order features of the user, wherein the charging order features of the user at least comprise one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charge quantity, charging frequency, charging cost and charging pile type; and finally, carrying out feature clustering processing on the electric automobile user based on the user charging order feature to obtain a user charging behavior portrait.
It is noted that the historical charging order data of the user implies various charging behavior characteristics of the user, and the charging order data is subjected to characteristic extraction, including but not limited to charging start time, charging end time, charging start state of charge, charging end state of charge, charging electric quantity, charging frequency, charging expense and charging pile type. Based on the user charging order feature, feature clustering is carried out on the electric automobile charging users, the behavior portraits of the electric automobile charging users are achieved, and charging frequencies, charging positions, charging time periods and charging electric quantity preference of multiple types of electric automobile charging users and corresponding types of users are obtained.
Notably, based on the logic of "offer-user feedback-real data correction", user real charging behavior is collected periodically, user behavior database is updated, and user behavior analysis is performed. The effectiveness of the user behavior analysis database is ensured by updating, iterating and real-time adjusting the user behavior analysis database. Wherein the user behavior database includes user historical charging order data.
Illustratively, the algorithm for constructing the user charging behavior image may be as follows:
initializing and importing user charging characteristic data, determining cluster number k and randomly initializing mass center
for i=1 number of users
Calculating Euclidean distances between the data point and each centroid
Assigning the user to the cluster closest to the cluster
end
for i=1: cluster number k
for j=1: number of kth cluster data
Taking the data point as a new centroid
Calculating Euclidean distances between all data points in the cluster and the new centroid
Selecting the data point with the smallest distance as a new centroid
end
end
The above loop process is repeated until the centroid is no longer changed or the maximum number of iterations is reached.
In addition, in an embodiment, as shown in fig. 4, the step S200 may include, but is not limited to, step S210, step S220, and step S230.
Step S210, station load prediction information of the next day is obtained according to the station load prediction model and station load analysis information;
step S220, determining a day-ahead offer price according to a preset price sensitive model, the next-day yard load prediction information and auxiliary prediction data information;
and step S230, determining the daily guidance price according to the price sensitive model, the yard load analysis information and the auxiliary prediction data information.
In the process of determining the day-ahead offer price and the day-ahead guide price, firstly, obtaining the next day yard load prediction information according to the yard load prediction model and yard load analysis information; determining a day-ahead offer price according to a preset price sensitive model, the next day station load prediction information and auxiliary prediction data information; and finally, determining the daily guide price according to the price sensitive model, the yard load analysis information and the auxiliary prediction data information.
Notably, in the process of making the price of the charging station, a user anti-learning mechanism is firstly constructed, and the price of the previous offer is followed<Day-to-day guide price<The principle of not participating in offer-guided prices "builds an optimal charging pricing mechanism targeting maximization of user interests. Interpretation and rule utilization of historical offer information by users is also targeted at maximizing their own benefits, consistent with the objectives of the pricing mechanism in embodiments of the applicationTherefore, the optimal charging pricing strategy based on the user anti-learning mechanism constructed in the embodiment of the application can ensure the effect of the offer. And (3) making an offer price in a day-ahead stage of the charging station, and constructing a price sensitivity model: d=rf (w t ,Q t ,H t ,P t DA ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein w is t Indicating whether or not the weather is rainy, Q t Indicating temperature, H t Indicating whether or not it is holiday, P t DA Representing the day-ahead offer price. All of this information is used as input to a price sensitivity model built based on the deep learning (RF ()) method to generate the electric vehicle charging demand at that time. And in the day-ahead stage, the charging station purchases electricity according to the next day load prediction information, and determines the day-ahead stage invitation electricity price according to the charging load price sensitivity model and the power station reference electricity price. Features include time period, minimum temperature, maximum temperature, date, and weather type.
Illustratively, the algorithm for determining the day-ahead offer price may be as follows:
initializing a reference price of electricity P of a charging station t 0 Maximum allowable price fluctuation range d max ，
In addition, in determining the daily offer price, the charging terminal operator makes pricing decisions based on the daily electricity purchase plan, the offer accepted by the user, and the observed ultra-short term charging load. The charging station operator decides on the direction and magnitude of the adjustment price based on this information. The pricing strategy for the day-stage lead price is similar to the algorithm described above for determining the day-ahead offer price, except that the maximum allowable price fluctuation range (d max ) From d max ＝P t 0 -P t DA And (5) determining. This is to guide the price P in the day t RT Higher than the offer price in the day-ahead stage.
In addition, in an embodiment, as shown in fig. 5, after the step S300 is performed, the step S310 and the step S320 may be included, but are not limited to.
Step S310, acquiring the power utilization starting time, the charging ending time and the charging amount of the electric automobile user, and determining the charging cumulative offer according to the power utilization starting time, the charging ending time and the charging amount;
step S320, when the charge accumulated offer is not greater than the total offer load of the charging station, the offer to the electric automobile user is successful.
When the user of the electric automobile receives the invitation before the charging day, the user of the electric automobile firstly obtains the power utilization starting time, the charging ending time and the charging quantity, and determines the charging accumulated invitation quantity according to the power utilization starting time, the charging ending time and the charging quantity; and then, under the condition that the charging accumulated offer is not greater than the total offer load of the charging station, successfully offers the user of the electric automobile.
Notably, based on the day-ahead offer prices of the charging stations, the electric automobile user can select and process the offer information on the application program; acquiring charging start time, charging end time and charging amount of each electric automobile user, and when the accumulated offer amount in the period is smaller than the total offer load of the station in the period, successful offer, otherwise, failed offer.
Illustratively, an algorithm for charging offers based on day-ahead offer prices may be as follows:
initializing an offer amount for each period of the ith charging stationReference electricity price P t 0 Day-ahead offer price of electricity P t DA Cumulative offer for each period>
In addition, in an embodiment, as shown in fig. 6, the step S300 may include, but is not limited to, step S330 and step S340.
Step S330, determining a short-term predicted value of the charging station according to the station load analysis information;
and step S340, carrying out daily offer processing on the electric automobile user according to the short-term predicted value of the charging station and the daily guide price.
In the process of carrying out daily offer, firstly determining a short-term predicted value of a charging station according to station load analysis information; and then, carrying out daily offer processing on the electric automobile user according to the short-term predicted value of the charging station and the daily guide price, and carrying out selection processing on the electric automobile user according to the daily offer.
Illustratively, an algorithm for charging offers based on the daily offer price may be as follows:
initializing a permissible maximum price fluctuation range d max ＝P t 0 -P t DA ，
In addition, if the electric car users choose not to participate in the day-ahead invitation and day guidance, they will pay the default price P t 0 . Under the pricing strategy, the day-ahead offer price P t DA Always lower than the daily lead price P t RT Day guidance price P t RT Reference electricity price P always lower than charging station t 0 . Thus, the first and second substrates are bonded together,users responding to pre-day offers and intra-day guidance will get less charging costs.
In some embodiments of the application, the day-ahead offer price is less than the day-ahead lead price, and the day-ahead lead price is in turn less than the electric car user who is not engaged in the offer and lead; setting a day-ahead offer price for a user in a day-ahead stage, and inviting the user of the electric automobile to charge according to the offer amount of each station in each period; in the daytime, a daily guide price is set for a user, and the charging behavior of the electric automobile user is guided, so that the deviation cost of the charging station is reduced.
As shown in fig. 7, an embodiment of the present application further provides an electric vehicle charging guide device 10, which includes:
the first processing module 100 is configured to obtain charging behavior information of a user of the electric automobile, where the charging behavior information includes yard load analysis information and a charging behavior image of the user;
a second processing module 200, configured to determine a day-ahead offer price and a day-ahead guidance price according to a preset yard load prediction model and yard load analysis information;
the third processing module 300 is used for performing daily offer processing on the electric automobile user according to the yard load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price.
The specific implementation of the electric vehicle charging guide device 10 is basically the same as the specific embodiment of the electric vehicle charging guide method described above, and will not be described herein again.
As shown in fig. 8, an embodiment of the present application further provides an electronic device 700, including: the electric vehicle charging guidance method in the above embodiment is implemented when the processor 710 executes the computer program, for example, the above-described method steps S100 to S300 in fig. 1, the method steps S410 to S420 in fig. 2, the method steps S510 to S530 in fig. 3, the method steps S210 to S230 in fig. 4, the method steps S310 to S320 in fig. 5, and the method steps S330 to S340 in fig. 6 are performed.
An embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, for example, by one of the processors in the above-described apparatus embodiments, which may cause the processor to perform the electric vehicle charging guidance method in the above-described embodiment, for example, to perform the method steps S100 to S300 in fig. 1, the method steps S410 to S420 in fig. 2, the method steps S510 to S530 in fig. 3, the method steps S210 to S230 in fig. 4, the method steps S310 to S320 in fig. 5, and the method steps S330 to S340 in fig. 6 described above.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
While the preferred embodiments of the present application have been described in detail, the embodiments of the present application are not limited to the above-described embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of the present application, and these equivalent modifications or substitutions are included in the scope of the embodiments of the present application as defined in the appended claims.
Claims (10)
1. An electric vehicle charging guiding method, characterized in that the method comprises:
acquiring charging behavior information of an electric automobile user, wherein the charging behavior information comprises station load analysis information and a user charging behavior image;
determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and the yard load analysis information;
performing daily offer processing on the electric automobile user according to the station load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price.
2. The electric vehicle charging guidance method according to claim 1, wherein the yard load prediction model is obtained by:
acquiring historical charging record information and auxiliary prediction data information of a charging station;
training a preset yard load prediction initial model according to the historical charging record information and the auxiliary prediction data information to obtain a yard load prediction model;
wherein the auxiliary prediction data information includes at least one of: temperature data, weather data, season data, and date type data.
3. The electric vehicle charging guidance method according to claim 1, wherein the user charging behavior representation is obtained by:
acquiring historical charging order data of a user of a charging station;
and extracting features of the historical charging order data of the user to obtain user charging order features, wherein the user charging order features at least comprise one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charge quantity, charging frequency, charging cost and charging pile type;
and carrying out feature clustering processing on the electric automobile user based on the user charging order feature to obtain the user charging behavior portrait.
4. The electric vehicle charging guidance method according to claim 2, wherein the determining a day-ahead offer price and a day-ahead guidance price according to a preset yard load prediction model and the yard load analysis information includes:
obtaining next-day yard load prediction information according to the yard load prediction model and the yard load analysis information;
determining the day-ahead offer price according to a preset price sensitive model, the next-day yard load prediction information and the auxiliary prediction data information;
and determining the daily lead price according to the price sensitive model, the yard load analysis information and the auxiliary prediction data information.
5. The method for guiding charging of an electric vehicle according to claim 2, wherein after the electric vehicle user performs a daily offer process according to the yard load analysis information, the daily offer price, and the user charging behavior representation, the method further comprises:
acquiring electricity utilization starting time, charging ending time and charging quantity of an electric automobile user, and determining charging accumulated offer according to the electricity utilization starting time, the charging ending time and the charging quantity;
and under the condition that the charging accumulated offer is not greater than the total offer load of a charging station, successfully offers to the users of the electric automobile.
6. The method for guiding charging of an electric vehicle according to claim 1, wherein the performing a daily offer process for the electric vehicle user according to the yard load analysis information and the daily guide price comprises:
determining a short-term predicted value of the charging station according to the station load analysis information;
and carrying out daily offer processing on the electric automobile user according to the short-term predicted value of the charging station and the daily guide price.
7. The electric vehicle charging guidance method of claim 1, wherein the day-ahead offer price is less than the day-ahead guidance price.
8. An electric vehicle charging guide device, characterized in that the device comprises:
the first processing module is used for acquiring charging behavior information of the electric automobile user, wherein the charging behavior information comprises station load analysis information and user charging behavior images;
the second processing module is used for determining a day-ahead offer price and a day-ahead guide price according to a preset yard load prediction model and the yard load analysis information;
the third processing module is used for carrying out daily offer processing on the electric automobile user according to the station yard load analysis information, the daily offer price and the user charging behavior portrait; and carrying out daily offer processing on the electric automobile user according to the yard load analysis information and the daily guide price.
9. An electronic device, comprising:
a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the electric vehicle charging guidance method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing computer-executable instructions that, when executed by a control processor, implement the electric vehicle charging guidance method according to any one of claims 1 to 7.
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| CN114997631A (en) * | 2022-05-26 | 2022-09-02 | 国网浙江电动汽车服务有限公司 | Electric vehicle charging scheduling method, device, equipment and medium |
| CN115630796A (en) * | 2022-09-16 | 2023-01-20 | 武汉理工大学 | Electric vehicle multi-objective optimization charging scheduling method under hybrid demand response |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114997631A (en) * | 2022-05-26 | 2022-09-02 | 国网浙江电动汽车服务有限公司 | Electric vehicle charging scheduling method, device, equipment and medium |
| CN115630796A (en) * | 2022-09-16 | 2023-01-20 | 武汉理工大学 | Electric vehicle multi-objective optimization charging scheduling method under hybrid demand response |
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