CN113629709A - Electric vehicle charging and discharging model obtaining method, system, equipment and storage medium - Google Patents
Electric vehicle charging and discharging model obtaining method, system, equipment and storage medium Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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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
- B60L53/63—Monitoring or controlling charging stations in response to network capacity
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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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- 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
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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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
- 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 method, a system, equipment and a storage medium for acquiring a charge and discharge model of an electric vehicle, and relates to the technical field of load prediction. The invention sets the established parameters in the model according to the driving time, the parking time and the charging data of the existing electric vehicles of different types, defines the charging and discharging models of the different electric vehicles and realizes the function of predicting the charging and discharging load change of the electric vehicles through space-time analysis. And sampling by utilizing a large scale sample, setting different parameters for different types of electric vehicles, acquiring corresponding electric vehicle charging and discharging models under various charging types, and fully considering the space-time distribution electric vehicle charging load prediction. The invention can solve the problem that the influence of space-time analysis on the charging and discharging of the electric vehicle is neglected.
Description
Technical Field
The invention relates to the technical field of load prediction, in particular to a method, a system, equipment and a storage medium for acquiring a charge and discharge model of an electric vehicle.
Background
In recent years, with the continuous progress of science and technology and the increasing demand of people for energy efficiency and economic efficiency in different environments, the distributed energy storage technology is rapidly developed. Compared with centralized energy storage, the distributed energy storage is applied to different scenes such as a power distribution side, a user side and a distributed power supply side, and the line loss, the construction cost and the investment pressure of a centralized energy storage power station can be reduced by the distributed energy storage. The electric vehicle, as one of the applications of distributed energy storage, not only meets the requirements of people on flexible power utilization, but also can effectively relieve the damage and influence of the current highly-polluted fossil energy on the environment. With the reduction of the cost of the battery, the continuous improvement of the electric power market and the increasing maturity of the fifth generation mobile communication technology, the fusion application of the electric vehicle and the distributed energy storage becomes one of the important life ways of people in the future and is bound to be operated on a large scale. With the continuous progress of electric power and battery technology, the research on charging and discharging of electric vehicles is valued and researched by experts at home and abroad, and different charging and discharging models are established.
In the existing electric vehicle charging and discharging model, much attention is paid to policy making of a macroscopic module and a mesoscopic module and development potential of an electric vehicle; the micro module, such as the type of the electric vehicle, the charging method, and the corresponding charging time, are not considered in depth, and the influence of different charging modes of the electric vehicle on the distributed energy storage load is not considered. Therefore, the existing electric vehicle charge-discharge model has certain limitations in the aspect of specific situation analysis, and is difficult to be applied to practice in practical situations.
Disclosure of Invention
The invention aims to provide a method, a system, equipment and a storage medium for acquiring a charging and discharging model of an electric vehicle, so as to solve the problem that the influence of space-time analysis on the charging and discharging of the electric vehicle is neglected.
In order to achieve the above object, the present invention provides a method for obtaining a charge and discharge model of an electric vehicle, comprising:
acquiring a preset charge-discharge power coefficient of the electric vehicle, and driving and parking space distribution information and charge-discharge time information of different electric vehicles within a preset time period;
randomly generating a charging and discharging load curve related to time and space according to the driving and parking space distribution information and the typical characteristics of the charging and discharging time information;
analyzing the number of the electric vehicles of different types and corresponding load information according to the charge-discharge power coefficient;
obtaining a load change curve influenced by space-time analysis according to the charge and discharge load curve;
performing load superposition according to the number of the electric vehicles of different types and corresponding load information to obtain load prediction information;
and fitting the load change curve and the load prediction information to obtain charge and discharge models of different electric vehicles.
Preferably, the acquiring of the preset charge-discharge power coefficient of the electric vehicle, and the driving and parking spatial distribution information and the charge-discharge time information of different electric vehicles within the preset time period includes:
the method comprises the steps of respectively obtaining charging time probability, charging power, charging time, discharging time probability, discharging probability and discharging time of buses, private cars, taxis and business cars in a preset time period, and obtaining the one-year accumulated charging load and discharging power of all types of electric vehicles.
Preferably, the expression of the charging time probability of the bus is as follows:
wherein, Pc_b(T) is the charging time probability of the bus, TsFor the user's working hours, TxFor the user's off-duty time, TfullTime of full charge, TminAt the lowest charging time, Tn1To the start time of noon break, Tn2The lunch break end time.
Preferably, the expression of the charging time probability of the private car is:
where μ is the expected value of the random time variable, σ is the standard deviation of the random time variable, T is the charging time, Pc_p(T; mu; sigma) is the charging time probability of the private car.
Preferably, the randomly generating a charge and discharge load curve with respect to time and space according to the driving and parking space distribution information and the typical characteristics of the charge and discharge time information includes:
according to the type of the electric vehicle, the charging behavior of the vehicle, the initial position and the initial charge amount of the electric vehicle, calculating the initial charging and discharging moment, the charging and discharging duration and the charge state of the electric vehicle, recording the charging and discharging place, the charging and discharging power, the charging and discharging time and the accumulated load curve, and obtaining the charging and discharging load curve related to time and space.
Preferably, the obtaining of the load change curve influenced by the space-time analysis according to the charge and discharge load curve includes:
acquiring the history data, the parking generation rate and the parking distribution characteristics of the electric vehicle, calculating the difference between the charging and discharging load, the accumulated load and the parking number and the demand, generating the charging and discharging load after the preset time is superposed, and obtaining a load change curve influenced by space-time analysis.
Preferably, the obtaining the load prediction information and outputting the load prediction information to the user side includes:
and acquiring the load prediction information, and outputting the load prediction information to a CSV file of a user side.
The invention also provides a system for acquiring the charging and discharging model of the electric vehicle, which comprises: the device comprises an input module, a sampling simulation module, a data graphic processing module and an output module;
the input module comprises an initialization unit and a data acquisition unit;
the initialization unit is used for acquiring a preset charge-discharge power coefficient of the electric vehicle;
the data acquisition unit is used for acquiring driving and parking space distribution information and charging and discharging time information of different electric vehicles within a preset time period;
the sampling simulation module comprises a random number generation unit and an electric vehicle classification unit;
the random number generation unit is used for randomly generating a charging and discharging load curve related to time and space according to the driving and parking space distribution information and the typical characteristics of the charging and discharging time information;
the electric vehicle classification unit is used for analyzing the number of different types of electric vehicles and corresponding load information according to the charge-discharge power coefficient;
the data graph processing module comprises a time-space analysis unit and a prediction unit;
the time-space analysis unit is used for obtaining a load change curve influenced by time-space analysis according to the charge-discharge load curve;
the prediction unit is used for carrying out load superposition according to the number of the electric vehicles of different types and the corresponding load information to obtain load prediction information;
the output module is used for fitting the load change curve and the load prediction information to obtain charge and discharge models of different electric vehicles.
The embodiment of the invention also provides computer terminal equipment which comprises one or more processors and a memory. A memory coupled to the processor for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method for obtaining the charging and discharging model of the electric vehicle according to any one of the embodiments.
The embodiment of the invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for obtaining the charging and discharging model of the electric vehicle is implemented as described in any one of the above embodiments.
Compared with the prior art, the invention has the following beneficial effects:
the invention sets the established parameters in the model according to the driving time, the parking time and the charging data of the existing electric vehicles of different types, defines the charging and discharging models of the different electric vehicles and realizes the function of predicting the charging and discharging load change of the electric vehicles through space-time analysis. Sampling on a large scale, adopting different parameter settings for different types of electric vehicles, and analyzing the number of the different types of electric vehicles and corresponding load information; obtaining a load change curve influenced by space-time analysis according to the charge and discharge load curve; performing load superposition according to the number of the electric vehicles of different types and corresponding load information to obtain load prediction information; and fitting the load change curve and the load prediction information to obtain charge and discharge models of different electric vehicles. Compared with the existing electric vehicle charging and discharging model system, the electric vehicle charging and discharging model system solves the problems of the existing electric vehicle charging and discharging model, and carries out detailed simulation on the charging mode, the charging time and the load allocation of various types of electric vehicles; and predicting the charge and discharge load of the electric automobile according to the space-time distribution analysis.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for acquiring a charge and discharge model of an electric vehicle according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electric vehicle charging and discharging model obtaining system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the 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 understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
The invention aims to set established parameters in the model according to the driving time, the parking time and the charging data of the existing electric vehicles of different types, clarify charging and discharging models of the different electric vehicles and realize the function of predicting the charging and discharging load change of the electric vehicles through space-time analysis. Sampling by utilizing a large scale sample, adopting different parameter settings for different types of electric vehicles, and establishing an electric vehicle charging and discharging model acquisition system under various charging types; and based on Monte Carlo simulation, an electric vehicle charging load prediction model fully considering space-time distribution is established. Compared with the existing electric vehicle charging and discharging model system, the electric vehicle charging and discharging model system solves the problems of the existing electric vehicle charging and discharging model, and carries out detailed simulation on the charging mode, the charging time and the load allocation of various types of electric vehicles; and formulating a prediction method and a prediction system for the charge and discharge load of the electric automobile according to the space-time distribution analysis. The invention considers how to solve the problems of single type of the charging and discharging model of the electric vehicle and inaccurate model output; the problem that the charging and discharging load of the electric vehicle cannot be effectively predicted due to randomness is solved; how to solve the problem that the influence of the space-time analysis on the charging and discharging of the electric vehicle is neglected.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a method for obtaining a charging/discharging model of an electric vehicle according to an embodiment of the present invention. In this embodiment, the method for obtaining the charging and discharging model of the electric vehicle includes the following steps:
s110, acquiring a preset charge-discharge power coefficient of the electric vehicle, and driving and parking space distribution information and charge-discharge time information of different electric vehicles within a preset time period;
s120, randomly generating a charging and discharging load curve related to time and space according to the driving and parking space distribution information and the typical characteristics of the charging and discharging time information;
s130, analyzing the number of the electric vehicles of different types and corresponding load information according to the charge-discharge power coefficient;
s140, obtaining a load change curve influenced by space-time analysis according to the charge and discharge load curve;
s150, performing load superposition according to the number of the electric vehicles of different types and the corresponding load information to obtain load prediction information;
and S160, fitting the load change curve and the load prediction information to obtain charge and discharge models of different electric vehicles.
In this embodiment of the present invention, in step S110, the obtaining of the preset charge-discharge power coefficient of the electric vehicle, and the driving and parking space distribution information and the charge-discharge time information of different electric vehicles within the preset time period includes:
the method comprises the steps of respectively obtaining charging time probability, charging power, charging time, discharging time probability, discharging probability and discharging time of buses, private cars, taxis and business cars in a preset time period, and obtaining the one-year accumulated charging load and discharging power of all types of electric vehicles.
In one embodiment, the expression of the charging time probability of the bus is:
wherein, Pc_b(T) is the charging time probability of the bus, TsFor the user's working hours, TxFor the user's off-duty time, TfullTime of full charge, TminAt the lowest charging time, Tn1To the start time of noon break, Tn2The lunch break end time.
In one embodiment, the expression of the charging time probability of the private car is:
where μ is the expected value of the random time variable, σ is the standard deviation of the random time variable, T is the charging time, Pc_p(T; mu; sigma) is the charging time probability of the private car.
In an embodiment of the present invention, in step S120, the randomly generating a charging and discharging load curve with respect to time and space according to the driving and parking space distribution information and the typical characteristics of the charging and discharging time information includes:
according to the type of the electric vehicle, the charging behavior of the vehicle, the initial position and the initial charge amount of the electric vehicle, calculating the initial charging and discharging moment, the charging and discharging duration and the charge state of the electric vehicle, recording the charging and discharging place, the charging and discharging power, the charging and discharging time and the accumulated load curve, and obtaining the charging and discharging load curve related to time and space.
In this embodiment of the present invention, in step S140, obtaining a load variation curve affected by a space-time analysis according to the charge and discharge load curve includes:
acquiring the history data, the parking generation rate and the parking distribution characteristics of the electric vehicle, calculating the difference between the charging and discharging load, the accumulated load and the parking number and the demand, generating the charging and discharging load after the preset time is superposed, and obtaining a load change curve influenced by space-time analysis.
In this embodiment of the present invention, in step S160, the obtaining the load prediction information and outputting the load prediction information to a user side includes:
and acquiring the load prediction information, and outputting the load prediction information to a CSV file of a user side.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an electric vehicle charging and discharging model obtaining system according to an embodiment of the present invention. In this embodiment, the electric vehicle charge/discharge model acquisition system includes: an input module 210, a sampling simulation module 220, a data graphics processing module 230, and an output module 240;
the input module 210 comprises an initialization unit 211 and a data acquisition unit 212;
the initialization unit 211 is configured to obtain a preset charge-discharge power coefficient of the electric vehicle;
the data acquisition unit 212 is configured to acquire driving and parking space distribution information and charging and discharging time information of different electric vehicles within a preset time period;
the sampling simulation module 220 comprises a random number generation unit 221 and an electric vehicle classification unit 222;
the random number generation unit 221 is configured to randomly generate a charge and discharge load curve with respect to time and space according to the driving and parking space distribution information and the typical characteristics of the charge and discharge time information;
the electric vehicle classification unit 222 is configured to analyze the number of different types of electric vehicles and corresponding load information according to the charge-discharge power coefficient;
the data graphic processing module 230 includes a spatio-temporal analysis unit 231 and a prediction unit 232;
the time-space analysis unit 231 is configured to obtain a load change curve affected by time-space analysis according to the charge and discharge load curve;
the prediction unit 232 is configured to perform load superposition according to the number of the electric vehicles of different types and the corresponding load information to obtain load prediction information;
the output module 240 is configured to fit the load change curve and the load prediction information to obtain charge and discharge models of different electric vehicles.
For specific limitations of the electric vehicle charge and discharge model acquisition system, reference may be made to the above limitations of the electric vehicle charge and discharge model acquisition method, which are not described herein again. All modules in the electric vehicle charging and discharging model obtaining system can be completely or partially realized through software, hardware and a combination of the software and the hardware. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, the electric vehicle charge-discharge model is analyzed, and specific parameters and coefficients of electric buses, private cars, taxis and official cars are set; establishing a corresponding typical database for the actual statistical data of the electric vehicle in each year and each city through related documents and related departments; generating a large number of reasonable random curves on the basis of a typical database by applying Monte Carlo simulation; establishing different types of electric vehicle data archiving systems, counting the number of electric vehicles, and generating corresponding weight values; carrying out data analysis on the operation discharging and parking charging conditions of the electric vehicle at different time and different places, and immediately establishing a corresponding model by applying Monte Carlo simulation; combining corresponding data and models, reasonably predicting the charging and discharging conditions of electric vehicles in the same season and the same place, overlapping loads, and quantifying the final load standard; importing existing analysis data into a terminal CSV file; charging and discharging conditions of the four types of electric vehicles in different periods and the prediction condition of future load are put on a terminal visual screen in a chart form by combining a terminal CSV file and an existing model. The charging and discharging probability, time and required superposed load setting parameters for different types of electric vehicles are shown in table 1, and the statistical data of collected relevant documents and relevant departments for the number of electric vehicles in each year and each city and real-time charging and discharging are shown in table 2.
TABLE 1 input Module data parameter settings
TABLE 2 data acquisition parameters
In a specific embodiment, in consideration of fluctuation in charge and discharge amount of the electric vehicle caused by seasonal variation, the time is set to a period of one year and a basic unit of one minute, and the period is divided into: 365 × 24 × 60 — 525600 minutes, the superimposed charging load is calculated as:
similarly, the one-year superimposed discharge power can be calculated as follows:
in addition, the charge and discharge power of each type of electric vehicle within a certain time can be obtained by integrating the instantaneous charge and discharge power of a single electric vehicle, taking the charge power of a bus as an example:
the power of other types of electric vehicles and the constant charging and discharging can be analogized.
For the charge and discharge probability distribution function, due to the commonality of the bus and the taxi, the service time of the charge and discharge probability distribution function is more easily influenced by people going on and off duty;since private cars and business cars are more random, four types of electric cars are classified into (1) buses and taxis and (2) private cars and business cars. Suppose that the user is on-duty and off-duty time TsAnd TxFull charge time of TfullMinimum charging time of TminThe start and end time of noon break is Tn1And Tn2. In practical situations, if the minimum required charging time is smaller than the noon break interval, the noon break interval time will be a factor influencing the probability of charging time, and vice versa. Taking the charging time probability function of the bus as an example:
the randomness of private cars and business cars is stronger, and the charging time probability of the private cars and the business cars approximately conforms to normal distribution. If μ is an expected value of the random time variable and σ is a standard deviation of the random time variable, taking the charging time probability of the private car as an example:
in a sampling simulation module, given collected data is used as a reference, corresponding codes are applied in Matlab to carry out Monte Carlo simulation, and random data and random curves under a plurality of conditions (100000) are randomly generated within a certain reasonable range.
According to the system setting parameters calculated in the input module, the number, the charging and discharging power, the time and the place records of the four different types of electric vehicles are counted and filed, two groups of weight values with the sum of 1 are generated according to the total charging and discharging power of a single type of electric vehicle, and the charging and discharging weight values Y of the electric bus are usedc_bFor example, the following steps are carried out:
wherein, Yc_b+Yc_p+Yc_t+Yc_o1, and the discharge weight value of each type of electric vehicle is Yd_b+Yd_p+Yd_t+Yd_o＝1。
And carrying out data analysis on the operation discharging and parking charging conditions of the electric vehicle at different time and different places, and immediately establishing a corresponding model by applying Monte Carlo simulation in Matlab.
And reasonably predicting the charging and discharging conditions of the electric vehicles in the same season and the same place, overlapping the loads and quantifying the final load standard. According to the requirements under different charging and discharging states and the exploration of the stability of the power system under different conditions, the prediction is divided into two situations and analyzed, wherein the two situations are respectively as follows: the electric vehicle does not participate in load prediction under the discharge scene, and the electric vehicle participates in load prediction under the discharge scene.
The existing analysis data is imported into a terminal CSV file, and charging and discharging conditions of the four types of electric vehicles in different periods and the predicted conditions of future loads are put on a terminal visual screen in a chart form by combining the terminal CSV file and an existing model.
Referring to fig. 3, an embodiment of the invention provides a computer terminal device, which includes one or more processors and a memory. The memory is coupled to the processor and configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method for obtaining the charging and discharging model of the electric vehicle according to any of the embodiments.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the electric vehicle charging and discharging model obtaining method. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is configured to perform the above method for obtaining the charging and discharging model of the electric vehicle, and achieve the technical effects consistent with the above method.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions, which when executed by a processor, implement the steps of the electric vehicle charging and discharging model obtaining method in any one of the above embodiments. For example, the computer readable storage medium may be the memory including the program instructions, and the program instructions may be executed by a processor of a computer terminal device to implement the method for obtaining a charging and discharging model of an electric vehicle, and achieve the same technical effects as the method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. An electric vehicle charging and discharging model obtaining method is characterized by comprising the following steps:
acquiring a preset charge-discharge power coefficient of the electric vehicle, and driving and parking space distribution information and charge-discharge time information of different electric vehicles within a preset time period;
randomly generating a charging and discharging load curve related to time and space according to the driving and parking space distribution information and the typical characteristics of the charging and discharging time information;
analyzing the number of the electric vehicles of different types and corresponding load information according to the charge-discharge power coefficient;
obtaining a load change curve influenced by space-time analysis according to the charge and discharge load curve;
performing load superposition according to the number of the electric vehicles of different types and corresponding load information to obtain load prediction information;
and fitting the load change curve and the load prediction information to obtain charge and discharge models of different electric vehicles.
2. The method for acquiring the charging and discharging model of the electric vehicle according to claim 1, wherein the acquiring of the preset charging and discharging power coefficient of the electric vehicle, and the driving and parking space distribution information and the charging and discharging time information of different electric vehicles within the preset time period comprises:
the method comprises the steps of respectively obtaining charging time probability, charging power, charging time, discharging time probability, discharging probability and discharging time of buses, private cars, taxis and business cars in a preset time period, and obtaining the one-year accumulated charging load and discharging power of all types of electric vehicles.
3. The method for acquiring the electric vehicle charge-discharge model according to claim 2, wherein the expression of the charge time probability of the bus is as follows:
wherein, Pc_b(T) is the charging time probability of the bus, TsFor the user's working hours, TxFor the user's off-duty time, TfullTime of full charge, TminAt the lowest charging time, Tn1To the start time of noon break, Tn2The lunch break end time.
4. The electric vehicle charge-discharge model acquisition method according to claim 2, wherein the expression of the charging time probability of the private car is:
where μ is the expected value of the random time variable, σ is the standard deviation of the random time variable, T is the charging time, Pc_pThe charging time probability of the private car is [ mu ], [ sigma ].
5. The method for obtaining the charging and discharging model of the electric vehicle according to claim 1, wherein the randomly generating the charging and discharging load curve with respect to time and space according to the driving and parking space distribution information and the typical characteristics of the charging and discharging time information comprises:
according to the type of the electric vehicle, the charging behavior of the vehicle, the initial position and the initial charge amount of the electric vehicle, calculating the initial charging and discharging moment, the charging and discharging duration and the charge state of the electric vehicle, recording the charging and discharging place, the charging and discharging power, the charging and discharging time and the accumulated load curve, and obtaining the charging and discharging load curve related to time and space.
6. The method for obtaining the charging and discharging model of the electric vehicle according to claim 1, wherein obtaining the load change curve influenced by the time-space analysis according to the charging and discharging load curve comprises:
acquiring the history data, the parking generation rate and the parking distribution characteristics of the electric vehicle, calculating the difference between the charging and discharging load, the accumulated load and the parking number and the demand, generating the charging and discharging load after the preset time is superposed, and obtaining a load change curve influenced by space-time analysis.
7. The method for acquiring the charging and discharging model of the electric vehicle according to claim 1, wherein the acquiring the load prediction information and outputting the load prediction information to a user side comprises:
and acquiring the load prediction information, and outputting the load prediction information to a CSV file of a user side.
8. An electric vehicle charge-discharge model acquisition system, comprising: the device comprises an input module, a sampling simulation module, a data graphic processing module and an output module;
the input module comprises an initialization unit and a data acquisition unit;
the initialization unit is used for acquiring a preset charge-discharge power coefficient of the electric vehicle;
the data acquisition unit is used for acquiring driving and parking space distribution information and charging and discharging time information of different electric vehicles within a preset time period;
the sampling simulation module comprises a random number generation unit and an electric vehicle classification unit;
the random number generation unit is used for randomly generating a charging and discharging load curve related to time and space according to the driving and parking space distribution information and the typical characteristics of the charging and discharging time information;
the electric vehicle classification unit is used for analyzing the number of different types of electric vehicles and corresponding load information according to the charge-discharge power coefficient;
the data graph processing module comprises a time-space analysis unit and a prediction unit;
the time-space analysis unit is used for obtaining a load change curve influenced by time-space analysis according to the charge-discharge load curve;
the prediction unit is used for carrying out load superposition according to the number of the electric vehicles of different types and the corresponding load information to obtain load prediction information;
the output module is used for fitting the load change curve and the load prediction information to obtain charge and discharge models of different electric vehicles.
9. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the electric vehicle charge and discharge model acquisition method according to any one of claims 1 to 7.
10. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the electric vehicle charge and discharge model acquisition method according to any one of claims 1 to 7.
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