CN108001282B - Charging device and method for realizing dynamic electricity price adjustment based on big data - Google Patents
Charging device and method for realizing dynamic electricity price adjustment based on big data Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/64—Optimising energy costs, e.g. responding to electricity rates
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
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Abstract
The invention discloses a charging device and a method for realizing dynamic electricity price adjustment based on big data, wherein the charging device comprises an electricity price scheme management module, an electricity price scheme prediction module and a charging behavior monitoring module; the output end of the charging behavior monitoring module is connected with the input end of the electricity price scheme prediction module and used for acquiring and analyzing charging data of the charging pile and outputting the charging data to the electricity price scheme prediction module; the electricity price scheme prediction module is used for analyzing the charging data and outputting an optimal electricity price scheme to the electricity price scheme management module; the electricity price scheme management module is used for adjusting the electricity price of the charging pile according to the optimal electricity price scheme. The device and the method provided by the invention have the advantages that through monitoring the utilization rate and the load of the charging station, the geographical position, the operation cost and the profit situation of the charging station are combined, the differentiated electricity price strategies are formulated for different charging stations, the charging electricity price is utilized to guide a user to realize the ordered charging behavior, and the maximization of the charging profit is realized on the premise of ensuring the safe and stable operation of a power grid.
Description
Technical Field
The invention relates to the technical field of electric vehicle charging, in particular to a charging device and method for realizing dynamic electricity price adjustment based on big data.
Background
In recent years, national policies strongly support the development of new energy automobiles, and as the holding capacity of electric automobiles is larger and larger, the charging demand is increased in a blowout manner. The problems of queuing for charging, no charging in piles, uneven distribution of charging time, uneven utilization rate of charging stations and the like are increasingly prominent. On the other hand, with the access of a large number of high-power charging piles to the power grid, great challenges are generated for safe and reliable operation of the power distribution network which runs under high load at present.
Disclosure of Invention
The invention aims to provide a charging device and a charging method for realizing dynamic adjustment of electricity price based on big data aiming at the defects in the prior art, the ordered charging behavior of a user is guided by the charging electricity price, the utilization rate of a charging station is improved, and the safe and reliable operation of a power distribution network is facilitated.
In order to achieve the object of the present invention, according to a first aspect of the present invention, an embodiment of the present invention provides a charging device for implementing dynamic adjustment of electricity price based on big data, the charging device including an electricity price scheme management module, an electricity price scheme prediction module, and a charging behavior monitoring module; the output end of the charging behavior monitoring module is connected with the input end of the electricity price scheme prediction module and is used for acquiring charging data of a charging pile, analyzing the charging data and outputting the charging data to the electricity price scheme prediction module; the output end of the electricity price scheme prediction module is connected with the input end of the electricity price scheme management module and used for analyzing the charging data and outputting an optimal electricity price scheme to the electricity price scheme management module; the electricity price scheme management module is used for adjusting the electricity price of the charging pile according to the optimal electricity price scheme.
In some embodiments, the charging behavior monitoring module includes a charging behavior data acquisition unit, a charging behavior data processing unit, a charging behavior data storage unit, and a charging behavior data output unit; the charging behavior data processing unit is respectively connected with the charging behavior data acquisition unit, the charging behavior data storage unit and the charging behavior data output unit.
In some embodiments, the charging behavior data acquisition unit is configured to form the acquired charging data into a message; the charging behavior data processing unit is used for analyzing the message into a data structure of a relational database; the charging behavior data storage unit is used for storing the analyzed charging behavior data into a relational database; the charging behavior data output unit is used for processing and outputting data according to application requirements.
In some embodiments, the electricity price scheme prediction module includes a charging data input unit, a charging data analysis unit, and a prediction data output unit, and the charging data analysis unit is connected to the charging data input unit and the prediction data output unit, respectively.
In some embodiments, the electricity rate scheme management module includes an electricity rate scheme acquisition unit, an electricity rate scheme issuing unit, and an issuing state feedback unit; the electricity price scheme obtaining unit is used for obtaining electricity price scheme information predicted by the electricity price scheme predicting module; the electricity price scheme issuing unit is used for issuing the electricity price scheme information to the charging pile charging unit module; the delivery state feedback unit is used for feeding back the delivery state of the electricity price scheme and the charging behavior data analysis result of the charging pile delivered by the electricity price scheme to an electricity price scheme manager, and the charging behavior data analysis result is used for verifying the feasibility of the electricity price scheme and assisting the electricity price scheme manager in decision making.
Compared with the prior art, the charging device in the embodiment of the invention monitors the charging information of the charging piles of each charging station in real time, analyzes the charging information to obtain a power price scheme, performs differentiated adjustment on the power price, guides a user to realize ordered charging behavior through the differentiated charging power price, ensures reasonable utilization of charging resources, and improves the operation benefit and reliability of a power grid.
In order to achieve the object of the present invention, based on the same inventive concept, according to a second aspect of the present invention, an embodiment of the present invention provides a charging method for dynamically adjusting power rates based on big data, which includes a charging device for dynamically adjusting power rates based on big data as described in one of the above embodiments.
The charging method comprises the following steps:
s1, the charging behavior monitoring module collects charging data of charging piles of all stations, analyzes the charging data and outputs the charging data to the electricity price scheme prediction module;
s2, after receiving the charging data, the electricity price scheme prediction module analyzes the charging data and outputs the optimal electricity price scheme information to the electricity price scheme management module;
and S3, the electricity price scheme management module adjusts the electricity price of the charging pile at each site according to the received optimal electricity price scheme information and the optimal electricity price scheme information.
In certain embodiments, the step S1 includes:
s1.1, a charging behavior data acquisition unit acquires charging data of a charging pile and forms a message;
s1.2, the charging behavior data processing unit analyzes the message into a data structure of a relational database;
s1.3 the charging behavior data storage unit stores the analyzed charging behavior data in a relational database
And S1.4, the charging behavior data output unit processes the data according to the application requirements and outputs the data to the electricity price scheme prediction module.
In certain embodiments, the step S2 includes:
s2.1, analyzing the input data according to data statistics, data mining, machine learning and an intelligent algorithm by a charging data analysis unit based on a cloud computing technology, and finally realizing optimal electricity price scheme recommendation prediction;
and S2.2, the prediction data output unit outputs the optimal electricity price scheme prediction information to the electricity price scheme management module, and a power price scheme manager makes a decision.
In certain embodiments, the step S3 includes:
s3.1, the electricity price scheme obtaining unit obtains electricity price scheme information predicted by the electricity price scheme prediction module;
s3.2 the electricity price scheme issuing unit issues the electricity price scheme information to the charging pile charging unit module;
and S3.3, the issuing state feedback unit feeds back the issuing state of the electricity price scheme and the charging behavior data analysis result after the electricity price scheme issues the charging pile to an electricity price scheme manager, and the charging behavior data analysis result is used for verifying and analyzing the feasibility of the electricity price scheme and assisting decision making of an electricity price scheme prediction module.
In certain embodiments, said step S3.3 comprises: and the issuing state feedback unit tracks the influence of the electricity price scheme change on the site charging load and the charging utilization rate in real time through the charging monitoring module.
Compared with the prior art, the charging method provided by the embodiment of the invention guides the user to realize the ordered charging behavior through the differentiated charging electricity price, and the differentiated charging electricity price is realized by adjusting the charging service fee in the peak-valley period under the condition that the peak-valley basic electricity price is not changed, so that the reasonable utilization of charging resources is ensured, and the operation benefit and reliability of a power grid are improved.
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 diagram of a functional module of a charging device according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a charging behavior monitoring module according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a power rate scheme prediction module according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a power rate scheme management module according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a dynamic electricity price adjustment process in an application scenario according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical solution of the present invention, the following description is made by referring to the specific embodiments and the accompanying drawings.
Fig. 1 is a charging device for implementing dynamic electricity price adjustment based on big data according to an embodiment of the present invention, and the charging device includes an electricity price scheme management module, an electricity price scheme prediction module, and a charging behavior monitoring module;
the functional function of each functional module of the charging device and the connection relationship of each component in the embodiment of the invention are described in detail below.
The output end of the charging behavior monitoring module is connected with the input end of the electricity price scheme prediction module and is used for acquiring charging data of a charging pile, analyzing the charging data and outputting the charging data to the electricity price scheme prediction module;
the output end of the electricity price scheme prediction module is connected with the input end of the electricity price scheme management module and used for analyzing the charging data and outputting an optimal electricity price scheme to the electricity price scheme management module;
the electricity price scheme management module is used for adjusting the electricity price of the charging pile according to the optimal electricity price scheme.
Fig. 2 is a schematic diagram of a framework of an electricity price scheme prediction module, and further, as shown in fig. 2, the charging behavior monitoring module includes a charging behavior data acquisition unit, a charging behavior data processing unit, a charging behavior data storage unit, and a charging behavior data output unit; the charging behavior data processing unit is respectively connected with the charging behavior data acquisition unit, the charging behavior data storage unit and the charging behavior data output unit.
Specifically, the charging behavior data acquisition unit is used for forming the acquired charging data into a message; the charging behavior data processing unit is used for analyzing the message into a data structure of a relational database; the charging behavior data storage unit is used for storing the analyzed charging behavior data into a relational database; the charging behavior data output unit is used for processing and outputting data according to application requirements.
Specifically, the charging behavior monitoring module collects the load and the charging utilization rate of each charging pile at regular time, and then calculates the load and the charging utilization rate of the site according to the site clustering. Based on massive monitoring data, a charging load and a charging utilization rate rule of an entity site are obtained through big data analysis, the charging operation condition of the electric vehicle charging all day is displayed according to regions and time intervals, operation decision and analysis of charging behavior habits of users are assisted, and a necessary data basis is provided for continuously improving the accuracy of a power price scheme prediction model.
Fig. 3 is a schematic diagram of a power rate scheme prediction module, and further, as shown in fig. 3, the power rate scheme prediction module includes a charging data input unit, a charging data analysis unit, and a prediction data output unit, the charging data analysis unit is respectively connected to the charging data input unit and the prediction data output unit, and the power rate scheme prediction module includes three parts of data input, data analysis, and data output.
The charging data input unit performs data input: the input content at least comprises charging monitoring data, charging station capacity, regional load information, charging station geographical position information, charging pile electricity price scheme information and the like.
The charging data analysis unit performs data analysis: based on the cloud computing technology, the input data are analyzed according to data statistics, data mining, machine learning and intelligent algorithms, and finally the optimal electricity price scheme recommendation prediction is achieved. Specifically, the charging monitoring data is comprehensively analyzed, the charging electricity price is improved for the time periods with high load and high utilization rate of the charging station, and the charging electricity price is properly reduced for the time periods with low load and low utilization rate of the charging station on the basis of ensuring profit and loss balance. According to the rule, big data analysis and prediction are carried out by combining the capacity of the charging station, the regional load condition, the charging pile electricity price scheme information, the charging station geographical position relation and other information, and the optimal electricity price scheme in the current stage is recommended for the manager. Meanwhile, the charging user can be timely notified through the mobile application when the electricity price scheme is changed, the user is guided to go from a station with a high utilization rate to a station with a low utilization rate for charging, reasonable utilization of charging resources is achieved, and charging operation benefits are improved.
The prediction data output unit performs data output: and outputting the optimal electricity price scheme prediction information to an electricity price scheme management module, and making a decision by a power price scheme manager.
In some embodiments, as shown in fig. 4, the electricity rate scheme management module includes an electricity rate scheme acquisition unit, an electricity rate scheme issuing unit, and an issuing state feedback unit; the electricity price scheme obtaining unit is used for obtaining electricity price scheme information predicted by the electricity price scheme predicting module; the electricity price scheme issuing unit is used for issuing the electricity price scheme information to the charging pile charging unit module; the delivery state feedback unit is used for feeding back the delivery state of the electricity price scheme and tracking the influence of the change of the electricity price scheme on the charging load and the charging utilization rate of the station in real time through the charging monitoring module. The electricity price scheme management module is mainly used for executing and issuing an electricity price scheme to the charging pile charging module, and unified management of charging electricity prices is achieved.
Compared with the prior art, the charging device in the embodiment of the invention monitors the charging information of the charging piles of each charging station in real time, analyzes the charging information to obtain a power price scheme, performs differentiated adjustment on the power price, guides a user to realize ordered charging behavior through the differentiated charging power price, ensures reasonable utilization of charging resources, and improves the operation benefit and reliability of a power grid.
In order to achieve the object of the present invention, based on the same inventive concept, according to a second aspect of the present invention, an embodiment of the present invention provides a charging method for dynamically adjusting power rates based on big data, which includes a charging device for dynamically adjusting power rates based on big data as described in one of the above embodiments.
The charging method comprises the following steps:
s1, the charging behavior monitoring module collects charging data of charging piles of all stations, analyzes the charging data and outputs the charging data to the electricity price scheme prediction module;
s2, after receiving the charging data, the electricity price scheme prediction module analyzes the charging data and outputs the optimal electricity price scheme information to the electricity price scheme management module;
and S3, the electricity price scheme management module adjusts the electricity price of the charging pile at each site according to the received optimal electricity price scheme information and the optimal electricity price scheme information.
In certain embodiments, the step S1 includes:
s1.1, a charging behavior data acquisition unit acquires charging data of a charging pile and forms a message;
s1.2, the charging behavior data processing unit analyzes the message into a data structure of a relational database;
s1.3 the charging behavior data storage unit stores the analyzed charging behavior data in a relational database
And S1.4, the charging behavior data output unit processes the data according to the application requirements and outputs the data to the electricity price scheme prediction module.
In certain embodiments, the step S2 includes:
s2.1, analyzing the input data according to data statistics, data mining, machine learning and an intelligent algorithm by a charging data analysis unit based on a cloud computing technology, and finally realizing optimal electricity price scheme recommendation prediction; specifically, for the time slot that charging station load is high, the high utilization ratio, improve the power price of charging, for charging station load is low, the utilization ratio is low, suitably reduces the power price of charging on the basis of guaranteeing profit and loss balance. According to the rule, big data analysis and prediction are carried out by combining the capacity of the charging station, the regional load condition, the charging pile electricity price scheme information, the charging station geographical position relation and other information, and the optimal electricity price scheme in the current stage is recommended for the manager.
And S2.2, the prediction data output unit outputs the optimal electricity price scheme prediction information to the electricity price scheme management module, and a power price scheme manager makes a decision.
In certain embodiments, the step S3 includes:
s3.1, the electricity price scheme obtaining unit obtains electricity price scheme information predicted by the electricity price scheme prediction module;
s3.2 the electricity price scheme issuing unit issues the electricity price scheme information to the charging pile charging unit module;
and S3.3, the issuing state feedback unit feeds back the issuing state of the electricity price scheme and the charging behavior data analysis result after the electricity price scheme issues the charging pile to an electricity price scheme manager, and the charging behavior data analysis result is used for verifying and analyzing the feasibility of the electricity price scheme and assisting decision making of an electricity price scheme prediction module.
In certain embodiments, said step S3.3 comprises: and the issuing state feedback unit tracks the influence of the electricity price scheme change on the site charging load and the charging utilization rate in real time through the charging monitoring module.
The method of the present invention will be described in detail below with reference to an application example.
The specific scene is that three physical charging stations are arranged in the same area, the utilization rate of one charging station located in the center is 90% in a certain time period every day, and the utilization rates of two charging stations within a range of 5 kilometers away from the center are 50% and 40% respectively. If the electricity price information of all the charging stations is the same, the charging electricity price is adjusted according to the charging service fee adjustment mode, and if the charging service fee is increased or decreased by 0.05 yuan/degree, the utilization rate of the charging stations is decreased or increased by 5%. Aiming at the station with high charging utilization rate, the charging service fee is increased to reduce the charging utilization rate; and aiming at the station with low charging utilization rate, the charging service fee is reduced to improve the charging utilization rate. The user can be effectively guided to charge from the station with high charging utilization rate to the station with low charging utilization rate through the change of the charging electricity price, and the optimal regional electricity price scheme strategy is obtained by applying the electricity price scheme prediction model, so that the regional profit maximization is realized.
The method provided by the embodiment of the invention aims at the entity charging station, comprehensively analyzes and obtains the optimal electricity price scheme strategy in the area by monitoring the charging load and the utilization rate of the station and combining the capacity, the position information and the electricity price information of the station, and recommends the strategy to a user for reference and decision making.
Compared with the prior art, the charging method provided by the embodiment of the invention guides the user to realize the ordered charging behavior through the differentiated charging electricity price, and the differentiated charging electricity price is realized by adjusting the charging service fee in the peak-valley period under the condition that the peak-valley basic electricity price is not changed, so that the reasonable utilization of charging resources is ensured, and the operation benefit and reliability of a power grid are improved.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (2)
1. A charging device for realizing dynamic electricity price adjustment based on big data is characterized by comprising an electricity price scheme management module, an electricity price scheme prediction module and a charging behavior monitoring module;
the output end of the charging behavior monitoring module is connected with the input end of the electricity price scheme prediction module and is used for acquiring charging data of a charging pile, analyzing the charging data and outputting the charging data to the electricity price scheme prediction module; the charging behavior monitoring module comprises a charging behavior data acquisition unit, a charging behavior data processing unit, a charging behavior data storage unit and a charging behavior data output unit; the charging behavior data processing unit is respectively connected with the charging behavior data acquisition unit, the charging behavior data storage unit and the charging behavior data output unit; the charging behavior data acquisition unit is used for forming the acquired charging data into a message; the charging behavior data processing unit is used for analyzing the message into a data structure of a relational database; the charging behavior data storage unit is used for storing the analyzed charging behavior data into a relational database; the charging behavior data output unit is used for processing and outputting data according to application requirements;
the output end of the electricity price scheme prediction module is connected with the input end of the electricity price scheme management module and used for analyzing the charging data and outputting an optimal electricity price scheme to the electricity price scheme management module;
the electricity price scheme management module is used for adjusting the electricity price of the charging pile according to the optimal electricity price scheme; the electricity price scheme prediction module comprises a charging data input unit, a charging data analysis unit and a prediction data output unit, wherein the charging data analysis unit is respectively connected with the charging data input unit and the prediction data output unit; the electricity price scheme management module comprises an electricity price scheme acquisition unit, an electricity price scheme issuing unit and an issuing state feedback unit; the electricity price scheme obtaining unit is used for obtaining electricity price scheme information predicted by the electricity price scheme predicting module; the electricity price scheme issuing unit is used for issuing the electricity price scheme information to the charging pile charging unit module; the delivery state feedback unit is at least used for feeding back the delivery state of the electricity price scheme and the charging behavior data analysis result of the charging pile delivered by the electricity price scheme to an electricity price scheme manager, and the charging behavior data analysis result is used for verifying and analyzing the feasibility of the electricity price scheme and assisting the decision of the electricity price scheme prediction module.
2. A charging method for dynamically adjusting electricity prices based on big data, which is implemented based on the charging device for dynamically adjusting electricity prices based on big data in claim 1, wherein the method comprises the following steps:
s1, the charging behavior monitoring module acquires charging data of charging piles of each station, analyzes the charging data and outputs the charging data to the electricity price scheme prediction module;
the step S1 includes steps S1.1-S1.4:
s1.1, a charging behavior data acquisition unit acquires charging data of a charging pile and forms a message;
s1.2, the charging behavior data processing unit analyzes the message into a data structure of a relational database;
s1.3, the charging behavior data storage unit stores the analyzed charging behavior data into a relational database;
s1.4, the charging behavior data output unit processes the data according to application requirements and outputs the data to the electricity price scheme prediction module;
s2, after receiving the charging data, the electricity price scheme prediction module analyzes the charging data and outputs the optimal electricity price scheme information to the electricity price scheme management module;
the step S2 includes S2.1 to S2.2:
s2.1, analyzing the input data according to data statistics, data mining, machine learning and intelligent algorithms by a charging data analysis unit based on a cloud computing technology, and finally realizing optimal electricity price scheme recommendation prediction;
s2.2, the prediction data output unit outputs the optimal electricity price scheme prediction information to the electricity price scheme management module, and a power price scheme manager makes a decision;
s3, the electricity price scheme management module adjusts the electricity price of the charging pile of each site according to the received optimal electricity price scheme information and the optimal electricity price scheme information;
the step S3 includes S3.1 to S3.3:
s3.1, the electricity price scheme obtaining unit obtains electricity price scheme information predicted by the electricity price scheme prediction module;
s3.2 the electricity price scheme issuing unit issues the electricity price scheme information to the charging pile charging unit module;
s3.3, the issuing state feedback unit feeds back the issuing state of the electricity price scheme and the charging behavior data analysis result after the electricity price scheme issues the charging pile to an electricity price scheme manager, and the charging behavior data analysis result is used for verifying and analyzing the feasibility of the electricity price scheme and assisting decision of an electricity price scheme prediction module; and the issuing state feedback unit tracks the influence of the electricity price scheme change on the site charging load and the charging utilization rate in real time through the charging monitoring module.
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| US12018955B2 (en) | 2019-08-14 | 2024-06-25 | Honda Motor Co., Ltd. | System and method for presenting electric vehicle charging options |
| CN110733371B (en) * | 2019-10-30 | 2021-06-04 | 深圳供电局有限公司 | Charging analysis method for electric automobile charging pile |
| CN111274469B (en) * | 2020-02-27 | 2023-10-17 | 百度在线网络技术（北京）有限公司 | Charging control method and device based on block chain, electronic equipment and medium |
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| US9509158B2 (en) * | 2013-12-31 | 2016-11-29 | Lite-On, Inc. | Power supply configuration system and method |
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