Electric vehicle intelligent charging and discharging control method and system based on vehicle network interaction

Abstract

The invention relates to the technical field of intelligent power grids and interaction between electric vehicles and power grids, and provides an electric vehicle intelligent charging and discharging control method and system based on vehicle-network interaction. The method comprises the following steps: acquiring charging pile related information, load data and charging resources of a to-be-regulated area; calculating a baseline load according to the load data, and calculating the adjustable capability of the to-be-regulated region according to the baseline load; constructing a user charging behavior analysis model according to the battery initial charging electric quantity, the battery end charging electric quantity, the time of arriving at the charging place and the vehicle taking time; according to the power grid regulation and control demand and the adjustable capability, the response capability of the vehicle network resources is judged, and a vehicle network resource first backup list is obtained through screening; matching the charging resource with a power grid regulation and control target, and performing secondary recommendation matching on the first backup list of the vehicle network resource based on a matching result to obtain a second backup list of the vehicle network resource; and formulating an ordered charging regulation and control scheme based on the vehicle network resource second backup list.

Description

Technical Field

[0001] The present invention relates to the field of smart grid and electric vehicle and grid interaction technology, and in particular to an electric vehicle intelligent charging and discharging control method and system based on vehicle-grid interaction. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the gradual large-scale promotion of electric vehicles, their large-scale charging is bound to increase the load level of the power system, especially random and disorderly charging, which will greatly increase the load level of the power system during special periods, and aggravate the voltage drop and branch capacity mismatch in the local power grid. At the same time, it will bring about power quality problems such as injected harmonics, low power factor, and three-phase imbalance, which will seriously affect the safe and stable operation of the power grid.

[0004] Developing and applying flexible control technology for intelligent charging and discharging of electric vehicles to achieve vehicle-grid integration is an effective means of reducing the impact of large-scale electric vehicle charging and discharging on the diversity of the power grid, an important basis for improving the reliability, economy and compatibility of power grid operation, and a basic link for realizing the active participation of electric vehicles in the interactive optimization operation of wide-area power grids and building a comprehensive intelligent dispatch and win-win mechanism for electric vehicles and power grids.

[0005] Flexible control of electric vehicle charging and discharging is mostly targeted at individual electric vehicles, and there is rarely effective flexible control of charging and discharging of large-scale electric vehicles as a whole. There is a lack of effective charging and discharging control means to enable large-scale electric vehicle loads to participate in grid zone peak shaving control. Summary of the invention

[0006] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides an intelligent charging and discharging control method and system for electric vehicles based on vehicle-grid interaction. The present invention aims to fully dispatch electric vehicles to participate in vehicle-grid interaction, provide grid flexibility and promote the consumption of new energy, while taking into account the operating cost of the distribution network and the charging satisfaction of electric vehicle users, to achieve more efficient and clean energy utilization.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] The first aspect of the present invention provides an intelligent charging and discharging control method for an electric vehicle based on vehicle-grid interaction.

[0009] An intelligent charging and discharging control method for an electric vehicle based on vehicle-grid interaction, comprising:

[0010] Obtain information about charging piles in the area to be regulated, load data, charging resources, battery starting charging capacity, battery ending charging capacity, time to arrive at the charging location and time to pick up the vehicle;

[0011] According to the load data, the baseline load is calculated, and according to the baseline load, the adjustable capacity of the area to be regulated is calculated;

[0012] Build a user charging behavior analysis model based on the battery's starting charging capacity, battery's ending charging capacity, time to reach the charging location, and time to pick up the vehicle;

[0013] According to the grid control demand and adjustable capacity, the response capacity of the vehicle grid resources is judged, and the first backup list of vehicle grid resources is screened; the charging resources are matched with the grid control target, and based on the matching results, the first backup list of vehicle grid resources is recommended for secondary matching to obtain the second backup list of vehicle grid resources;

[0014] Based on the second backup list of vehicle-network resources, formulate an orderly charging control plan.

[0015] Furthermore, the area to be regulated includes the residential charging pile area and the social public charging pile area in the low-voltage area.

[0016] Furthermore, the adjustable capacity of the area to be regulated is calculated based on the baseline load, and the method includes: for the residential charging pile area in the low-voltage zone, its adjustable capacity is the cumulative value of the baseline loads of all residential charging piles; for the social public charging pile area, its adjustable capacity is the charging pile baseline load × the set value.

[0017] Furthermore, the user charging behavior analysis model is constructed according to the battery initial charging capacity, the battery final charging capacity, the time of arriving at the charging location and the time of picking up the vehicle; the method includes: establishing a first probability model according to the battery initial charging capacity and the battery final charging capacity; establishing a second probability model according to the time of arriving at the charging location and the time of picking up the vehicle, combined with the characteristic that the user behavior habits obey the normal distribution; the user charging behavior analysis model includes the first probability model and the second probability model.

[0018] Furthermore, the response capability of the vehicle network resources is judged according to the grid control demand and adjustable capacity, and a first backup list of vehicle network resources is screened out; the method includes: if the adjustable capacity is greater than the grid control demand, the vehicle network resources meet the control demand, and the first backup list of vehicle network resources whose adjustable capacity meets the requirements is screened; otherwise, it is fed back that the vehicle network resources cannot respond to this control.

[0019] Furthermore, the charging resources are matched with the power grid regulation objectives, and based on the matching results, the first backup list of vehicle network resources is secondarily recommended and matched to obtain the second backup list of vehicle network resources; the method includes: based on the analysis results of the user charging behavior analysis model, the charging resources are matched with the power grid regulation objectives according to the regulation method, adjustable capacity, response speed and user participation enthusiasm, and based on the matching results, the first backup list of vehicle network resources is secondarily recommended and matched to obtain the second backup list of vehicle network resources.

[0020] Furthermore, the regulation scheme includes a rigid control scheme and a flexible control scheme.

[0021] A second aspect of the present invention provides an intelligent charging and discharging control system for electric vehicles based on vehicle-grid interaction.

[0022] An intelligent charging and discharging control system for electric vehicles based on vehicle-grid interaction, comprising:

[0023] A data acquisition module is configured to: acquire information related to charging piles in the area to be regulated, load data, charging resources, battery starting charging power, battery ending charging power, time to arrive at the charging location and time to pick up the vehicle;

[0024] A calculation module is configured to: calculate a baseline load according to the load data, and calculate an adjustable capacity of the area to be regulated according to the baseline load;

[0025] A model building module, which is configured to: build a user charging behavior analysis model based on the battery starting charging power, the battery ending charging power, the time of arriving at the charging location and the time of picking up the vehicle;

[0026] The backup list generation module is configured to: determine the response capability of the vehicle network resources according to the power grid control demand and adjustable capacity, and screen to obtain the first backup list of vehicle network resources; match the charging resources with the power grid control target, and based on the matching result, perform secondary recommendation matching on the first backup list of vehicle network resources to obtain the second backup list of vehicle network resources;

[0027] The plan formulation module is configured to formulate an orderly charging control plan based on the second backup list of vehicle network resources.

[0028] A third aspect of the present invention provides a computer-readable storage medium.

[0029] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the electric vehicle intelligent charging and discharging control method based on vehicle-grid interaction as described in the first aspect above.

[0030] A fourth aspect of the present invention provides a computer device.

[0031] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for intelligent charging and discharging control of electric vehicles based on vehicle-grid interaction as described in the first aspect above are implemented.

[0032] A fifth aspect of the present invention provides a computer program product or a computer program.

[0033] The present invention provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in the electric vehicle intelligent charging and discharging control method based on vehicle-grid interaction as described in the first aspect above.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention establishes a user charging behavior model to describe the charging resource characteristics of electric vehicles participating in vehicle-grid interaction in an urban power grid, which is helpful to accurately predict and manage the behavior of electric vehicles.

[0036] The present invention analyzes the adjustable capacity of electric vehicle charging resources, establishes an orderly charging control scheme that supports multiple control methods, combines the power grid control needs and the electric vehicle charging behavior model to match resources, solves the problems of vehicle-pile equipment resource mismatch and high distribution network operation costs, takes into account the distribution network control and electric vehicle user charging satisfaction requirements, and achieves a balance in the power grid load. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0038] Figure 1 It is a flow chart of an electric vehicle intelligent charging and discharging control method based on vehicle-grid interaction shown in the present invention;

[0039] Figure 2 It is a structural diagram of an electric vehicle intelligent charging and discharging control method based on vehicle-grid interaction shown in the present invention;

[0040] Figure 3 It is a schematic diagram of the implementation of an intelligent charging and discharging flexible regulation control system for an electric vehicle in a vehicle-grid interaction mode shown in the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0044] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0045] Embodiment 1

[0046] like Figure 1As shown, this embodiment provides an intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0047] Obtain information about charging piles in the area to be regulated, load data, charging resources, battery starting charging capacity, battery ending charging capacity, time to arrive at the charging location and time to pick up the vehicle;

[0048] According to the load data, the baseline load is calculated, and according to the baseline load, the adjustable capacity of the area to be regulated is calculated;

[0049] Build a user charging behavior analysis model based on the battery's starting charging capacity, battery's ending charging capacity, time to reach the charging location, and time to pick up the vehicle;

[0050] According to the grid control demand and adjustable capacity, the response capacity of the vehicle grid resources is judged, and the first backup list of vehicle grid resources is screened; the charging resources are matched with the grid control target, and based on the matching results, the first backup list of vehicle grid resources is recommended for secondary matching to obtain the second backup list of vehicle grid resources;

[0051] Based on the second backup list of vehicle-network resources, formulate an orderly charging control plan.

[0052] The solution described in this embodiment is described in detail below, including:

[0053] Step 1: Access the relevant systems of residential charging piles and social public charging piles to obtain the required archive data and real-time load data. The station archives of social public charging piles include data such as the region, detailed address, user number, charging station number, charging station name, rated capacity, charging station category, charging station purpose, site status, construction site, and organizational code. The pile archives of social public charging piles include charging pile number, charging pile name, charging station name, charging station number, user number, meter number, charging pile operation status, production date, commissioning date, whether adjustable, charging pile type, pile model, rated power, rated current, adjustable power upper limit, and adjustable power lower limit. The archives of residential charging piles include the region, management unit, user number, user name, user address, contract capacity, operating capacity, rated power, station area, whether time-of-use electricity price, urban and rural categories, and whether adjustable.

[0054] Step 2: Calculate the baseline load of the charging pile, the formula is as follows:

[0055]

[0056] Among them, Q base,t represents the baseline load of the day period t involved in the regulation, n is the number of similar days, Q i,t represents the actual load during time period t on the i-th day.

[0057] The load data of all residential charging piles under the substation are summarized and accumulated to generate the total charging load under the substation, and then the baseline load is calculated using the formula.

[0058] For public charging piles, the charging station is taken as the smallest aggregation unit to calculate the baseline load of each charging station.

[0059] Step 3: Process the characteristics of various charging resources and sort out or calculate various parameters. Improve the parameters of the charging pile, such as charging power, operating capacity, response speed, control method, etc. Calculate the characteristic parameters of the charging pile, such as the maximum load peak, the time of the maximum load peak, the minimum load value, the time of the minimum load, etc.

[0060] In order to better carry out vehicle-grid interaction business and allow charging resources to fully participate in grid regulation, it is necessary to analyze the characteristics of charging resources, such as charging power, operating capacity, response speed, adjustable capacity, load fluctuation characteristics, etc. And classify and analyze two different subjects: charging resources for residents in low-voltage areas and social public charging resources.

[0061] Residential charging pile resources in low-voltage areas: Statistics or analysis of the charging power, operating capacity, load curve trend, response speed, control method and other parameters of each resident charging pile, and analysis of the contribution to the energy consumption of the area where it is located. Access the charging facility-related system to obtain basic information on the resident charging pile resources in the low-voltage area, such as user number, area to which it belongs, location, operating capacity, response speed, etc. Access the electricity consumption information collection system to obtain the real-time load of the resident charging pile resources in the low-voltage area and the area where it is located. Aggregate the load resources of residential users in the area, calculate the proportion of the load of the area where the electric vehicle is located at the peak load; calculate the operating capacity of the resident charging resources and the proportion of the area where it is located; calculate the maximum load peak, the time when the maximum load peak is located, the minimum load value, the time when the minimum load is located, and other information for the aggregated load curve. For the aggregated load curve, take the average of the load data of the previous 5 days, and calculate the 96-point load curve within the day to form the charging load baseline of the area.

[0062] Social public charging pile resources: Statistics or analysis of the average charging load, operating capacity, response speed, adjustable capacity, control mode and other parameters of each charging station. Connect with the aggregation platform of social public charging pile resources to obtain its basic archive information and real-time load data, and clarify parameters such as operating capacity and response speed. By tracking the operating information of the charging station, calculate the average charging load per hour, the average charging load per day, and the load baseline of the charging station. Assuming that the charging load drops by 20% based on the original working power, which is acceptable to the users of the charging station, use the load baseline to calculate the load adjustable capacity value within the day.

[0063] Step 4: Calculate the adjustable capacity of the residential charging pile resources. The maximum adjustable capacity of a single user is the rated power of the charging pile equipment. The adjustable capacity of all residential charging piles under the substation is the baseline load after all residential charging piles under the substation are aggregated.

[0064] Step 5: Calculate the adjustable capacity of the public charging pile resources. The adjustable capacity of the charging station is the baseline load of the charging station multiplied by 20%.

[0065] Step 6: Use the user charging behavior analysis model to calculate the estimated end time of charging for the charging piles currently in the charging state. Based on the user charging behavior model, the system establishes a probability model for the battery's starting charging capacity and the battery's end charging capacity based on the acquired electric vehicle charging behavior data. Based on the characteristics of user behavior habits that obey the normal distribution, a probability model for the time when the user arrives at the charging location and the time to pick up the car is established. Based on the two probability models, determine the electric vehicles currently charging during the regulation period. Filter the corresponding charging piles.

[0066] Specifically, the Monte Carlo method is used to simulate the user's charging demand to establish an electric vehicle load model. Based on probability and statistical theory, random numbers are used for computer simulation or sampling to obtain an approximate solution to the problem. According to the characteristics of residents' daily behavior habits that obey the normal distribution, the charging behavior of electric vehicle users is simulated. The choice of user charging time is mainly affected by travel habits and life patterns. The probability distribution density curve of daily driving distance and travel / return time of electric vehicles in residential areas can be fitted. Assuming that the owner starts charging at the last return time, and the charging time is proportional to the daily driving distance, it is concluded that under uncontrolled conditions, the owner starts charging at the time f TS and charging duration f TC (x) Probability distribution density of the two parameters (To simplify the probability density function of tc, assume that all vehicles are P c Constant power charging):

[0067]

[0068] Among them, f TS Indicates the time when the owner starts charging in an uncontrolled situation, f TC (x) represents the charging duration, P c Indicates constant power charging, μ s =17.6,σ s =3.4, μ D =3.2,σ D =0.88.

[0069] Step 7: Determine the responsiveness of the vehicle-grid resources based on the grid control demand. The grid control demand comes from the distribution network line variable overload control demand and the autonomously generated orderly charging demand of the substation. Analyze the key elements in the control demand, such as administrative regions, power supply companies, control periods, control targets, control methods, response speed, etc. According to the administrative region and control period information in the grid control demand, aggregate the charging resources in the corresponding time and space, and accumulate the adjustable capacity of various resources. If the adjustable capacity is greater than the control target of the control demand, the vehicle-grid resources meet the control demand and continue with the subsequent steps. Otherwise, the process ends and feedback is given that the vehicle-grid resources cannot respond to this control.

[0070] Step 8: Charging resource matching, that is, matching the characteristics of various charging resources obtained in step 3 with the adjustable capacity of the resident charging pile resources obtained in step 4. First, using the charging behavior analysis model, before the start of the regulation execution period, analyze the charging end time according to the start charging time of each charging pile that is in the charging state at that time. Secondly, by sorting out the charging end time of each charging pile, the charging piles whose charging period can cover the regulation period are preferentially included in the regulation reserve list. Thirdly, combined with the analysis results of the charging behavior analysis model, according to the parameters such as regulation mode, adjustable capacity, response speed, and user participation enthusiasm, the regulation reserve list is recommended and matched twice to generate a charging resource list that meets the regulation requirements. The users of this list are the main users participating in the regulation, and based on this, the subsequent regulation plan is compiled. According to the requirements of the regulation needs, the resident charging pile resources or the social public charging pile resources can be specified to participate, or they can participate at the same time.

[0071] Step 9: Prepare orderly charging control plans, and formulate orderly charging plans for social public charging resources and orderly charging plans for residential charging resources. Set the configuration items of the orderly charging plan for social public charging resources, such as plan name, response type, control start time, control end time, target power, control range, and list of resources participating in the control. Set the configuration items of the orderly charging plan for residential charging resources, such as plan name, power supply unit, control target, business type, control start time, control end time, and list of resources participating in the control.

[0072] In addition, the orderly charging demand of the substation can be generated autonomously according to the load rate of the current substation of the power grid, so as to formulate a control plan. When the load rate of the substation exceeds 80%, it is a heavy load state, and the power of the adjustable charging resources in the substation is reduced by 20%; when the load rate of the substation exceeds 100%, it is an overload state, and the power of the adjustable resources in the substation is reduced by 50%. After the load rate triggers the threshold, the control measures will be automatically enabled.

[0073] Step 10: Control scheme settings. The control scheme can be set in two ways: rigid control and flexible control. Residential charging pile resources support both rigid control and flexible control, while social public charging resources can only be flexibly controlled, such as flexible control by voltage drop ratio, flexible control by target value, and elastic flexible control. This improves the convenience of charging in residential areas, guides based on time-of-use electricity prices, reduces charging costs, and reduces the impact on household power grids.

[0074] Step 11: Issuance and execution of the control plan. If the control plan is implemented by the public charging pile resources to respond to the control event, the plan will be issued and the instructions will be parsed through the load aggregation platform; if the control event is implemented by the residential charging pile resources, the plan will be issued and the instructions will be parsed through the electricity consumption information collection system.

[0075] Through the implementation of these strategies and technical solutions, electric vehicle charging resources can be effectively managed, grid load can be optimized, charging costs can be reduced, and the impact on the grid can be reduced, achieving a win-win situation between the grid and electric vehicle users.

[0076] Embodiment 2

[0077] like Figure 2 As shown, this embodiment provides an electric vehicle intelligent charging and discharging control system based on vehicle-grid interaction, including:

[0078] A data acquisition module is configured to: acquire information related to charging piles in the area to be regulated, load data, charging resources, battery starting charging power, battery ending charging power, time to arrive at the charging location and time to pick up the vehicle;

[0079] A calculation module is configured to: calculate a baseline load according to the load data, and calculate an adjustable capacity of the area to be regulated according to the baseline load;

[0080] A model building module, which is configured to: build a user charging behavior analysis model based on the battery starting charging power, the battery ending charging power, the time of arriving at the charging location and the time of picking up the vehicle;

[0081] The backup list generation module is configured to: determine the response capability of the vehicle network resources according to the power grid control demand and adjustable capacity, and screen to obtain the first backup list of vehicle network resources; match the charging resources with the power grid control target, and based on the matching result, perform secondary recommendation matching on the first backup list of vehicle network resources to obtain the second backup list of vehicle network resources;

[0082] The plan formulation module is configured to formulate an orderly charging control plan based on the second backup list of vehicle network resources.

[0083] In some embodiments, the area to be regulated includes a residential charging pile area and a social public charging pile area in a low-voltage area.

[0084] In some embodiments, the calculation module is also configured as follows: for the residential charging pile area in the low-voltage zone, its adjustable capacity is the cumulative value of the baseline load of all residential charging piles; for the social public charging pile area, its adjustable capacity is the charging pile baseline load × the set value.

[0085] In some embodiments, the model building module is further configured to: establish a first probability model based on the battery's starting charging capacity and the battery's ending charging capacity; establish a second probability model based on the time of arrival at the charging location and the time of picking up the vehicle, combined with the characteristic that user behavior habits obey the normal distribution; the user charging behavior analysis model includes the first probability model and the second probability model.

[0086] In some embodiments, the backup list generation module is further configured to: if the adjustable capacity is greater than the grid control demand, the vehicle network resources meet the control demand, and the first backup list of vehicle network resources with adjustable capacity that meets the requirements is screened; otherwise, it is fed back that the vehicle network resources cannot respond to this control.

[0087] In some embodiments, the backup list generation module is further configured to: based on the analysis results of the user charging behavior analysis model, match the charging resources with the power grid control goals according to the control method, adjustable capacity, response speed and user participation enthusiasm; based on the matching results, perform secondary recommendation matching on the first backup list of vehicle network resources to obtain the second backup list of vehicle network resources.

[0088] In some embodiments, the regulation scheme includes a rigid control scheme and a flexible control scheme.

[0089] like Figure 3 As shown, the system includes a new power load management system, a power consumption information collection system, a concentrator, an orderly charging pile access unit, a smart energy meter (metering energy meter) and charging piles and other elements.

[0090] The new power load management system is the basic platform for realizing orderly charging control in residential areas. Based on the orderly charging management function of the new power load management system, it is responsible for monitoring the load in the substation area, the operation information of the charging piles, etc.; it is responsible for formulating orderly charging control strategies, and issuing control instructions through procurement to realize orderly charging in residential areas.

[0091] The electricity consumption information collection system is a basic platform for collecting, monitoring and forwarding control instructions on the operating status of residents' charging piles. It is responsible for collecting metering information from the smart electricity meters of users' charging piles and sharing it with the new power load management system. It is responsible for transmitting the orderly charging control instructions formulated by the new power load management system to the charging piles through the concentrator and the orderly charging pile access unit link.

[0092] The orderly charging pile access unit is a communication device that connects the concentrator and the charging pile. It communicates with the charging pile through the RS-485 interface and is responsible for forwarding start, stop and power flexible adjustment control instructions to the charging pile.

[0093] The online State Grid APP serves as the interface between residential users and the power grid. It is responsible for promoting the peak-valley time-of-use electricity price policy, confirming whether users are willing to participate in orderly charging, and pushing charging process monitoring information and charging bills to users.

[0094] As the execution terminal, the orderly charging pile is responsible for receiving and responding to instructions such as starting charging, stopping charging, and adjusting charging power, and is responsible for sending PWM power adjustment instructions to the vehicle.

[0095] Embodiment 3

[0096] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction as described in the first embodiment above are implemented.

[0097] Embodiment 4

[0098] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction as described in the first embodiment are implemented.

[0099] Embodiment 5

[0100] This embodiment provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps of the intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction described in the first embodiment.

[0101] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0102] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0105] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction, characterized in that: include: Obtain information about charging piles in the area to be regulated, load data, charging resources, battery starting charging capacity, battery ending charging capacity, time to arrive at the charging location and time to pick up the vehicle; According to the load data, the baseline load is calculated, and according to the baseline load, the adjustable capacity of the area to be regulated is calculated; Build a user charging behavior analysis model based on the battery's starting charging capacity, battery's ending charging capacity, time to reach the charging location, and time to pick up the vehicle; According to the grid control requirements and adjustable capacity, the response capability of the vehicle grid resources is judged, and the first backup list of vehicle grid resources is obtained by screening; Match the charging resources with the grid control targets, and based on the matching results, perform secondary recommendation matching on the first backup list of vehicle-grid resources to obtain the second backup list of vehicle-grid resources; Based on the second backup list of vehicle-network resources, formulate an orderly charging control plan.

2. The intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction according to claim 1 is characterized in that: The area to be regulated includes the residential charging pile area and the social public charging pile area in the low-voltage area.

3. The intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction according to claim 2 is characterized in that: The method of calculating the adjustable capacity of the area to be regulated based on the baseline load includes: for the residential charging pile area in the low-voltage zone, its adjustable capacity is the cumulative value of the baseline loads of all residential charging piles; for the social public charging pile area, its adjustable capacity is the charging pile baseline load × the set value.

4. The intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction according to claim 1 or 2, characterized in that: The method of constructing a user charging behavior analysis model according to the battery starting charging power, the battery ending charging power, the time of arriving at the charging location and the time of picking up the vehicle comprises: Establishing a first probability model according to the initial charging power of the battery and the final charging power of the battery; According to the arrival time at the charging location and the time to pick up the car, combined with the characteristics that user behavior habits obey normal distribution, a second probability model is established; The user charging behavior analysis model includes a first probability model and a second probability model.

5. The intelligent charging and discharging control method for electric vehicles based on vehicle-grid interaction according to claim 1 or 2, characterized in that: The method comprises: judging the response capability of the vehicle network resources according to the power grid regulation demand and the adjustable capacity, and screening out the first backup list of vehicle network resources; if the adjustable capacity is greater than the power grid regulation demand, the vehicle network resources meet the regulation demand, and screening out the first backup list of vehicle network resources whose adjustable capacity meets the requirements; otherwise, feedback is given that the vehicle network resources cannot respond to this regulation.

6. The electric vehicle intelligent charging and discharging control method based on vehicle-grid interaction according to claim 1 or 2 is characterized in that: The method includes matching the charging resources with the power grid control target, and based on the matching result, performing a secondary recommendation match on the first backup list of vehicle network resources to obtain a second backup list of vehicle network resources; the method includes: matching the charging resources with the power grid control target according to the control method, adjustable capacity, response speed and user participation enthusiasm based on the analysis result of the user charging behavior analysis model, and performing a secondary recommendation match on the first backup list of vehicle network resources based on the matching result to obtain a second backup list of vehicle network resources; or, The control scheme includes a rigid control scheme and a flexible control scheme.

7. The intelligent charging and discharging control system of electric vehicles based on vehicle-grid interaction is characterized by: include: A data acquisition module is configured to: acquire information related to charging piles in the area to be regulated, load data, charging resources, battery starting charging power, battery ending charging power, time to arrive at the charging location and time to pick up the vehicle; A calculation module is configured to: calculate a baseline load according to the load data, and calculate an adjustable capacity of the area to be regulated according to the baseline load; A model building module, which is configured to: build a user charging behavior analysis model based on the battery starting charging power, the battery ending charging power, the time of arriving at the charging location and the time of picking up the vehicle; A backup list generation module is configured to: determine the response capability of vehicle network resources according to the power grid control demand and adjustable capacity, and screen and obtain a first backup list of vehicle network resources; Match the charging resources with the grid control targets, and based on the matching results, perform secondary recommendation matching on the first backup list of vehicle-grid resources to obtain the second backup list of vehicle-grid resources; The plan formulation module is configured to formulate an orderly charging control plan based on the second backup list of vehicle network resources.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the intelligent charging and discharging control method of an electric vehicle based on vehicle-grid interaction as described in any one of claims 1 to 6 are implemented.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the intelligent charging and discharging control method of an electric vehicle based on vehicle-grid interaction as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps in the intelligent charging and discharging control method of an electric vehicle based on vehicle-grid interaction as described in any one of claims 1 to 6 are implemented.

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