Electric vehicle charging elastic load prediction method and device, electronic equipment and medium

Abstract

The invention relates to an electric vehicle charging elastic load prediction method and device, electronic equipment and a medium, and the method comprises the steps: calculating the charging time needed for charging each electric vehicle at a parking place; traversing each stop of all the electric vehicles based on the charging time to obtain the maximum load and the minimum load of each charging node within one day; and predicting the total maximum load and the total minimum load of the target area within one day according to the maximum load and the minimum load of each charging node within one day, and taking the total maximum load and the total minimum load of the target area within one day as the charging elastic load. The accuracy of prediction data can be improved, and a data basis is provided for interaction between the electric vehicle and a power grid.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicles, and in particular to a method, device, electronic equipment and medium for predicting elastic charging load of electric vehicles. Background Art

[0002] Electric vehicles are vehicles that use an onboard power source as a driving force and an electric motor to drive the wheels. Its working principle is that the battery provides current, which is regulated by the power regulator to drive the motor, and then the power transmission system drives the vehicle. Electric vehicles have relatively little impact on the environment and their prospects are widely optimistic.

[0003] As people continue to pay attention to hot issues such as environmental pollution and energy consumption, electric vehicles have become a new way to solve current problems due to their obvious advantages in environmental protection, cleanliness, and energy saving. The study of electric vehicle charging load model is the theoretical premise for the impact of large-scale electric vehicles on the power grid and their participation in power grid services. The charging load is affected by many subjective and objective factors such as user behavior and market conditions. Therefore, higher requirements are put forward for the charging load model under the influence of multiple factors. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method, device, electronic equipment and medium for predicting the elastic charging load of an electric vehicle, which can improve the accuracy of the predicted data.

[0005] The technical solution adopted by the present invention to solve the technical problem is: to provide a method for predicting the elastic load of electric vehicle charging, comprising the following steps:

[0006] Calculate the charging time required for each electric vehicle to charge at the parking location;

[0007] Based on the charging time, each stop of all electric vehicles is traversed to obtain the maximum load and minimum load of each charging node in a day;

[0008] The total maximum load and the total minimum load of the target area in one day are predicted according to the maximum load and the minimum load of each charging node in one day, and the total maximum load and the total minimum load of the target area in one day are used as the charging elastic load.

[0009] The charging time required for the electric vehicle to be charged at the stop location is Calculated, where t charge,j,i is the charging time of the jth electric vehicle at its i-th stop, Q need,j,i is the power demand of the jth electric vehicle for its i-th stop, is the charging node n where the j-th electric vehicle stops for the i-th time j,i of charging power.

[0010] When traversing each stop of all electric vehicles based on the charging time to obtain the maximum load and minimum load of the charging node within a day, the charging time required for charging is compared with the duration of the vehicle's stop. When the charging time required for charging is greater than the vehicle's stop time, the charging load of this charging is accumulated in the minimum load; when the charging time required for charging is less than the vehicle's stop time, the charging load of this charging is accumulated in the maximum load.

[0011] The maximum load of each charging node in one day is: Among them, P max (t arrive,j,i :t leave,j,i ,n j,i ) is the time at t arrive,j,i At time t leave,j,i During this period of time, it stays at charging node n j,i Maximum load, t arrive,j,i is the starting time of the i-th stop of the j-th electric vehicle, t leave,j,i is the end time of the i-th stop of the j-th electric vehicle, P charge,nj,i is the charging power at the i-th stop of the j-th electric vehicle.

[0012] The minimum load of each charging node in the day is: Among them, P min (t arrive,j,i :t leave,j,i ,n j,i ) is the time at t arrive,j,i At time t leave,j,i During this period of time, it stays at charging node n j,i Minimum load, t arrive,j,i is the starting time of the i-th stop of the j-th electric vehicle, t leave,j,i is the end time of the i-th stop of the j-th electric vehicle, is the charging power at the i-th stop of the j-th electric vehicle.

[0013] The total maximum load in the target area during the day is Among them, P max,all is the total maximum load, P max (t,n) is the maximum load of charging node n at time t, and N is the total number of charging nodes in the target area.

[0014] The total minimum load in the target area during the day is Among them, P min,all is the total minimum load, P min (t,n) is the minimum load of charging node n at time t, and N is the total number of charging nodes in the target area.

[0015] The technical solution adopted by the present invention to solve the technical problem is: to provide an electric vehicle charging elastic load prediction device, comprising:

[0016] A calculation module, used for calculating the charging time required for each electric vehicle to be charged at the stop location;

[0017] The traversal module is used to traverse each stop of all electric vehicles based on the charging time to obtain the maximum load and minimum load of each charging node in a day;

[0018] The prediction module is used to predict the total maximum load and total minimum load of the target area within a day according to the maximum load and minimum load of each charging node within a day, and use the total maximum load and total minimum load of the target area within a day as the charging elastic load.

[0019] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned electric vehicle charging elastic load prediction method when executing the computer program.

[0020] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned electric vehicle charging elastic load prediction method are implemented.

[0021] Beneficial Effects

[0022] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention obtains the spatiotemporal distribution of the elastic load of electric vehicles in a region in one day through modeling and simulation while taking into account the charging demand and parking characteristics, thereby making the predicted data more accurate and providing a data basis for the interaction between electric vehicles and the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a method for predicting electric vehicle charging elastic load according to a first embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of the parking time and charging time of an electric vehicle in the first embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of the elastic load of an electric vehicle in a first embodiment of the present invention;

[0026] Figure 4 Schematic diagram of the elastic load distribution of electric private cars on weekdays in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0028] The charging time of electric vehicles is often shorter than the parking time, e.g. Figure 2 As shown in the figure, selecting the appropriate charging time T2 in the stay time T1 can meet the user's charging needs, and this part of the charging load is the elastic load in this time period. Predicting the spatiotemporal distribution of electric vehicle elastic load is to predict the maximum load and minimum load at each time in a certain area.

[0029] When the dwell time of electric vehicles is longer than the time required for charging, the charging load of electric vehicles can be shifted during the dwell time period. Therefore, taking into account the charging demand and the characteristics of parking, the maximum and minimum values ​​of the charging load of all electric vehicles in the area are obtained. The actual charging load of electric vehicles in the area must be between the maximum load and the minimum load. The elastic load of electric vehicles in a certain area for a period of time is as follows: Figure 3 shown.

[0030] Based on the data such as the start and end time of the electric vehicle and the charging demand, the maximum load Pmax and minimum load Pmin of the electric vehicles in the area can be calculated by the following formula.

[0031]

[0032] Where P t is the charging power of the area at time t; P c is the charging power of an electric car; ΔP t+1 Indicates the electric vehicle load that must be added at time t+1, and the load that must be connected at the next moment Load that must be removed It consists of two parts; The number of electric vehicles that meet the conditions for electric vehicle access to the grid at time t+1 and whose stay time T1 is greater than the charging time T2; The number of electric vehicles that meet the access conditions at time t+1 and whose stay time T1 is less than or equal to the charging time T2; is the number of electric vehicles that are off-grid or fully charged at time t+1.

[0033] The first embodiment of the present invention relates to a method for predicting the elastic load of electric vehicle charging, such as Figure 1 As shown, the following steps are included:

[0034] Step 101, calculating the charging time required for each electric vehicle to charge at the stop location.

[0035] In this step, the charging time required for the electric vehicle to charge at the stop location can be calculated based on the required power of the electric vehicle and the charging power of the charging node. The specific calculation method is:

[0036]

[0037] Among them, t charge,j,i is the charging time of the jth electric vehicle at its i-th stop, Q need,j,i is the power demand of the jth electric vehicle for its i-th stop, is the charging node n where the j-th electric vehicle stops for the i-th time j,i of charging power.

[0038] Step 102, traverse each stop of all electric vehicles based on the charging time to obtain the maximum load and minimum load of each charging node in a day.

[0039] In this step, the charging time required for charging is compared with the duration of the vehicle's stay. When the charging time is greater than the stay time, the electric vehicle must be charged immediately, and the charging load should be accumulated in the minimum load of the area. When the charging time is less than the stay time, the charging load is transferable within the stay time period, and the load that does not have to be charged immediately should be accumulated in the maximum load of the area. By traversing every stop of all electric vehicles, the maximum load and minimum load of each charging node in a day can be obtained, which can be specifically expressed as:

[0040]

[0041] Among them, P max (t arrive,j,i :t leave,j,i ,n j,i ) is the time at t arrive,j,i At time t leave,j,i During this period of time, it stays at charging node n j,i The maximum load, P min (t arrive,j,i :t leave,j,i ,n j,i ) is the time at t arrive,j,i At time t leave,j,i During this period of time, it stays at charging node n j,i Minimum load, t arrive,j,i is the starting time of the i-th stop of the j-th electric vehicle, t leave,j,i is the end time of the i-th stop of the j-th electric vehicle, is the charging power of the jth electric vehicle at the i-th stop, t during,j,i =t leave,j,i -t arrive,j,i , represents the duration of the i-th stop of the j-th electric vehicle.

[0042] Step 103, predicting the total maximum load and total minimum load of the target area within a day according to the maximum load and the minimum load of each charging node within a day, and taking the total maximum load and the total minimum load of the target area within a day as the charging elastic load.

[0043] The total maximum load of the target area in one day in this step can be expressed as:

[0044]

[0045] The total minimum load in the target area within a day can be expressed as:

[0046]

[0047] Among them, P max,all is the total maximum load of the target area in one day, P max (t,n) is the maximum load of charging node n at time t, that is, P min,all is the total minimum load of the target area in one day, P min (t,n) is the minimum load of charging node n at time t, and N is the total number of charging nodes in the target area.

[0048] Figure 4 The schematic diagram of the elastic load distribution of electric private cars on weekdays predicted by the method of this embodiment is shown. Figure 4 It can be seen that the minimum load of electric private cars is 0, that is, the stay time of electric private cars is much longer than the time required for charging, so there is no immediate need for charging. The maximum load is larger during the daytime from 10:00 to 18:00, because most private cars stay in industrial areas at this time, and there are fast chargers in industrial areas, so the maximum load is larger. In the early morning, private cars stay in residential areas and stay for a long time, so there is a maximum load, and residential areas are all slow charging piles, so the maximum load is smaller than that in industrial areas.

[0049] The present invention obtains the spatiotemporal distribution of the elastic load of electric vehicles in a region in one day by modeling and simulating the charging demand and the parking characteristics, thereby making the predicted data more accurate and providing a data basis for the interaction between electric vehicles and the power grid.

[0050] A second embodiment of the present invention relates to a device for predicting flexible charging load of an electric vehicle, comprising:

[0051] A calculation module, used for calculating the charging time required for each electric vehicle to be charged at the stop location;

[0052] The traversal module is used to traverse each stop of all electric vehicles based on the charging time to obtain the maximum load and minimum load of each charging node in a day;

[0053] The prediction module is used to predict the total maximum load and total minimum load of the target area within a day according to the maximum load and minimum load of each charging node within a day, and use the total maximum load and total minimum load of the target area within a day as the charging elastic load.

[0054] The calculation module is Calculate the charging time required for an electric vehicle to charge at the stop location, where, t charge,j,i is the charging time of the jth electric vehicle at its i-th stop, Q need,j,i is the power demand of the jth electric vehicle for its i-th stop, is the charging node n where the j-th electric vehicle stops for the i-th time j,i of charging power.

[0055] The traversal module traverses each stop of all electric vehicles based on the charging time, and when the maximum load and the minimum load of each charging node in a day are obtained, the charging time required for charging is compared with the duration of the vehicle's stay. When the charging time required for charging is greater than the vehicle's stay time, the charging load of this charging is accumulated in the minimum load; when the charging time required for charging is less than the vehicle's stay time, the charging load of this charging is accumulated in the maximum load.

[0056] The traversal module is passed Calculate the maximum load of each charging node in a day, where P max (t arrive,j,i :t leave,j,i ,n j,i ) is the time at t arrive,j,i At time t leave,j,i During this period of time, it stays at charging node n j,i Maximum load, t arrive,j,i is the starting time of the i-th stop of the j-th electric vehicle, t leave,j,i is the end time of the i-th stop of the j-th electric vehicle, is the charging power at the i-th stop of the j-th electric vehicle.

[0057] The traversal module is passed Calculate the minimum load of each charging node in a day, where P min (t arrive,j,i :t leave,j,i ,nj,i ) is the time at t arrive,j,i At time t leave,j,i During this period of time, it stays at charging node n j,i Minimum load, t arrive,j,i is the starting time of the i-th stop of the j-th electric vehicle, t leave,j,i is the end time of the i-th stop of the j-th electric vehicle, is the charging power at the i-th stop of the j-th electric vehicle.

[0058] The prediction module is The total maximum load of the target area within one day is predicted, where P max,all is the total maximum load, P max (t,n) is the maximum load of charging node n at time t, and N is the total number of charging nodes in the target area.

[0059] The prediction module is The total minimum load of the target area within one day is predicted, where P min,all is the total minimum load, P min (t,n) is the minimum load of charging node n at time t, and N is the total number of charging nodes in the target area.

[0060] The third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned electric vehicle charging elastic load prediction method when executing the computer program.

[0061] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned electric vehicle charging elastic load prediction method.

[0062] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0063] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0064] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0065] 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.

[0066] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for predicting electric vehicle charging elastic load, characterized in that: The following steps are involved: Calculate the charging time required for each electric vehicle to charge at the parking location; Based on the charging time, each stop of all electric vehicles is traversed to obtain the maximum load and minimum load of each charging node in a day; The total maximum load and the total minimum load of the target area in one day are predicted according to the maximum load and the minimum load of each charging node in one day, and the total maximum load and the total minimum load of the target area in one day are used as the charging elastic load.

2. The electric vehicle charging elastic load prediction method according to claim 1 is characterized in that: The charging time required for the electric vehicle to be charged at the stop location is Calculated, where t charge,j,i is the charging time of the jth electric vehicle at its i-th stop, Q need,j,i is the power demand of the jth electric vehicle for its i-th stop, is the charging node n where the j-th electric vehicle stops for the i-th time j,i The charging power.

3. The electric vehicle charging elastic load prediction method according to claim 1 is characterized in that: When traversing each stop of all electric vehicles based on the charging time to obtain the maximum load and minimum load of the charging node within a day, the charging time required for charging is compared with the duration of the vehicle's stop. When the charging time required for charging is greater than the vehicle's stop time, the charging load of this charging is accumulated in the minimum load; when the charging time required for charging is less than the vehicle's stop time, the charging load of this charging is accumulated in the maximum load.

4. The electric vehicle charging elastic load prediction method according to claim 3 is characterized in that: The maximum load of each charging node in one day is: Among them, P max (t arrive,j,i :t leave,j,i ,n j,i ) is the time at t arrive,j,i At time t leave,j,i During this period of time, it stays at charging node n j,i Maximum load, t arrive,j,i is the starting time of the i-th stop of the j-th electric vehicle, t leave,j,i is the end time of the i-th stop of the j-th electric vehicle, P charge,nj,i is the charging power at the i-th stop of the j-th electric vehicle.

5. The electric vehicle charging elastic load prediction method according to claim 3 is characterized in that: The minimum load of each charging node in the day is: Among them, P min (t arrive,j,i :t leave,j,i ,n j,i ) is the time at t arrive,j,i At time t leave,j,i During this period of time, it stays at charging node n j,i Minimum load, t arrive,j,i is the starting time of the i-th stop of the j-th electric vehicle, t leave,j,i is the end time of the i-th stop of the j-th electric vehicle, P charge,nj,i is the charging power at the i-th stop of the j-th electric vehicle.

6. The electric vehicle charging elastic load prediction method according to claim 1 is characterized in that: The total maximum load in the target area during the day is Among them, P max,all is the total maximum load, P max (t,n) is the maximum load of charging node n at time t, and N is the total number of charging nodes in the target area.

7. The electric vehicle charging elastic load prediction method according to claim 1 is characterized in that: The total minimum load in the target area during the day is Among them, P min,all is the total minimum load, P min (t,n) is the minimum load of charging node n at time t, and N is the total number of charging nodes in the target area.

8. An electric vehicle charging elastic load prediction device, characterized in that: include: A calculation module, used for calculating the charging time required for each electric vehicle to be charged at the stop location; The traversal module is used to traverse each stop of all electric vehicles based on the charging time to obtain the maximum load and minimum load of each charging node in a day; The prediction module is used to predict the total maximum load and total minimum load of the target area within a day according to the maximum load and minimum load of each charging node within a day, and use the total maximum load and total minimum load of the target area within a day as the charging elastic load.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the electric vehicle charging elastic load prediction method as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electric vehicle charging elastic load prediction method as described in any one of claims 1-7 are implemented.

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