Power grid operation scheduling method based on big data and V2G technology

Abstract

The invention proposes a big data and V2G technology-based power grid operation scheduling method, and the method comprises the steps: analyzing the charging behaviors near all stages of charging power supply points in a power grid through the operation data of a real vehicle, so as to estimate the distribution and dynamic changes of a power grid increment load caused by a charging demand; and on the basis, a V2G facility is utilized to send a corresponding recommendation instruction to the vehicle which can be used as an energy storage source, and the power grid can execute electric energy scheduling by means of the vehicle reverse charging electric energy obtained by the V2G facility. And the power grid load distribution obtained through analysis is used for guiding construction planning of V2G facilities, and the discharge behavior of the energy storage vehicle is guided and adjusted through an electricity fee pricing means.

Description

Technical Field

[0001] The present invention belongs to the technical fields of new energy electric vehicle big data and V2G, and in particular to a power grid operation dispatching method based on big data and V2G technology. Background Art

[0002] With the continuous growth of the number of new energy electric vehicles, the high vehicle energy consumption and charging demand have also brought a heavy burden to the power grid, causing it to fluctuate violently during peak hours and regions, posing new challenges to the normal operation of the power grid, and also causing adverse effects on power dispatching, power quality, etc. In response to this problem, V2G technology came into being. It uses the power stored in a large number of on-board power batteries to discharge to the power grid, which can achieve the effect of peak shaving and valley filling for the power grid. However, the existing technology mostly only considers the load on the power supply side for power grid fluctuations, and provides corresponding plans based on the data of the historical load of the power grid, but ignores the phenomenon that the actual load changes dynamically with the charging demand and location of the vehicle, making it difficult to achieve efficient coordination between the vehicle as an energy storage and the power grid. The incoming power is often not well allocated to the high-load area, and the charging demand and power supply capacity at different substations and charging nodes cannot be accurately matched. In addition, since V2G technology is in the early stages of exploration, the relevant infrastructure is not yet mature and the construction cost is extremely high. If the actual charging demand is not fully considered in the design and planning, it may lead to the failure of V2G technology to achieve its due effect, and cause serious waste of manpower and material resources. Therefore, how to achieve accurate estimation of charging demand and the grid load it brings, and at the same time use V2G technology to achieve more efficient grid operation and scheduling, is a technical problem that urgently needs to be solved in this field. Summary of the invention

[0003] In view of this, in order to solve the technical problems existing in this field, the present invention provides a power grid operation scheduling method based on big data and V2G technology.

[0004] The present invention specifically adopts the following technical solutions: A grid operation dispatching method based on big data and V2G technology: using real vehicle operation data to analyze the charging behavior near charging power points at all levels in the grid, so as to estimate the distribution and dynamic changes of the grid incremental load caused by the charging demand; on this basis, using V2G facilities to send corresponding recommended instructions to vehicles that can be used as energy storage, and enabling the grid to perform power dispatching with the help of the reverse charging power of the vehicles obtained by V2G facilities.

[0005] The grid load distribution obtained through analysis can be further used to guide the construction planning of V2G facilities and to guide and regulate the discharge behavior of energy storage vehicles through electricity pricing.

[0006] Further, the method of using the actual vehicle operation data to analyze the charging behavior near each level of charging power source points in the power grid to estimate the grid incremental load distribution and dynamic changes caused by the charging demand is specifically as follows: Obtain the historical operation data of each new energy vehicle. Each frame of data includes VIN, vehicle model, vehicle driving or parking status, charge and discharge status, SOC, vehicle speed, time, cumulative mileage, longitude and latitude; After distinguishing each vehicle based on the VIN field, extract the driving data frames from the extracted historical operation data frames based on the vehicle driving status, discharge status, and vehicle speed information; After sorting the extracted driving data frames according to the time information, divide the continuous driving data frames based on time to obtain vehicle driving data segments; Use the longitude and latitude information in the driving data segments to match with the digital map information to identify the corresponding charging power source points and their levels for different segments, and assign corresponding power source point labels to each driving data segment; Perform secondary division on the driving data segments based on the power source point field so that each obtained segment corresponds one-to-one to the coverage range of the charging power source points in different regions of the city; Process the segments after secondary division based on the power source point labels, obtain the start and end times, driving mileage, start and end SOC of the segments, calculate the unit mileage power consumption and average vehicle speed within the segments, and establish a corresponding segment library for each charging power source point; After normalizing the unit mileage power consumption of different vehicle models under the segment library corresponding to each charging power source point, count the cumulative power consumption of each charging power source point at different times of each day; For a continuous time period under given conditions, count the cumulative power consumption distribution of each charging power source point at different times of each day, and on this basis, determine the corresponding load increment of each charging power source point during the peak electricity consumption period.

[0007] Further, the specific implementation method of using the V2G facility to send corresponding recommendation instructions to vehicles that can be used as energy storage sources and enabling the power grid to perform power dispatching by means of the reverse charging electric energy obtained by the V2G facility is as follows: Match the V2G facilities within a certain distance based on the load increment of each charging power source point at different times, and send a go-to recommendation instruction to the vehicles with reverse charging function; The power grid determines the available capacity that each V2G facility is expected to provide based on the response of each vehicle to the instruction; Based on the determined load increment and the available capacity of each V2G facility, perform comprehensive power dispatching control on each charging power source point and assign corresponding charging power to it.

[0008] Further, each of the charging power supply points includes a charging device, a transformer, and an electric energy router; wherein, the charging device is connected to the low-voltage side of the transformer, and the high-voltage side of the transformer is connected to the power supply line; each charging device is provided with an electric energy router, and the electric energy routers communicate with each other to form an electric energy dispatching and exchange network for adjusting the available capacity of each level of charging power supply point in real time through the overall control of the power grid. The control process specifically includes: based on the determined load increment and the available capacity of the V2G facilities, allocating the electric energy from the power grid and the energy storage vehicles to each charging power supply point matched with each V2G facility; increasing the available capacity of the high-load increment charging power supply points without matching V2G facilities; and controlling the total available capacity of each lower-level charging power supply point not to exceed the available capacity of any upper-level charging power supply point.

[0009] Further, each piece of information extracted from the historical operation data is in the form of a field tag; after the data is extracted, cleaning preprocessing including data filling, interpolation, replacement, and elimination is respectively performed for various types of data anomalies.

[0010] Further, the longitude and latitude information in the driving data segment is matched with the digital map information to identify the charging power supply points and their levels corresponding to different segments, and corresponding power supply point tags are assigned to each driving data segment. Specifically: using the digital map information, converting the longitude and latitude of the vehicle into the same format as the map and performing corresponding coordinate corrections, and matching the road section ranges covered by each level of charging power supply points in the map corresponding to the corrected coordinates, thereby obtaining the corresponding power supply point tags.

[0011] Further, the electricity consumption per unit mileage within the segment is calculated based on the SOC at the start and end of the segment and the cumulative mileage information; the specific process of normalizing the electricity consumption per unit mileage of different vehicle models under the segment library corresponding to each charging power supply point includes: based on the vehicle model information, extracting all vehicles with a specific vehicle model number of 1, 2, …, i - 1 from the segment library, respectively corresponding to the electricity consumption per unit mileage Q1, Q2, …, Q i-1 , Q i , and calculating the average value E Q of the electricity consumption per unit mileage and the variance σ Q . For any segment n of the same vehicle model, through the formula: E nor =(Q n -EQ) / σ Q , the normalized electricity consumption per unit mileage E nor of the segment is calculated; combining the start and end cumulative mileage of the segment, for each day period in a continuous time period with a certain range of changes in specific months, seasons, or environmental temperature and climate conditions, the cumulative electricity consumption value distribution corresponding to each charging power supply point is statistically analyzed.

[0012] Further, after the power grid receives the response from the vehicle with reverse charging function, it assigns corresponding reverse charging pricing strategies to each vehicle based on the load increments of different charging power supply points.

[0013] Based on the above design, the load increments corresponding to each charging power supply point during the peak electricity consumption can also be used as a reference for the planning and construction of V2G facilities.

[0014] Moreover, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that when the processor executes the program, it implements the steps of the power grid operation scheduling method based on big data and V2G technology as described above.

[0015] A non-transitory computer-readable storage medium stores a computer program thereon, characterized in that when the computer program is executed by a processor, it implements the steps of the power grid operation scheduling method based on big data and V2G technology as described above.

[0016] Compared with the prior art, the present invention and its preferred solutions analyze the actual charging demands of new energy electric vehicles, utilize the big data of actual vehicle operation to analyze the charging behavior habits near charging power supply points at all levels in the power grid, so as to estimate the distribution and dynamic changes of the grid incremental load caused by a large number of charging demands. On this basis, the V2G facilities are used to send corresponding recommendation instructions to the vehicles that can be used as energy storage sources, and the power grid can execute more precise power scheduling by means of the reverse charging electric energy obtained by the V2G facilities, which helps to reduce the impact of high charging loads on the power grid and improve the coordination degree between the power grid and V2G facilities, so that the advantages of V2G technology can be fully exerted. The analyzed grid load distribution can also be used to guide the construction planning of new V2G facilities, and the discharge behavior of energy storage vehicles can be guided and regulated by means of electricity pricing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The following further describes the present invention in detail with reference to the drawings and specific embodiments: Figure 1 It is a schematic flowchart of the method provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In the following, specific embodiments of the present application will be described in detail with reference to the drawings. According to these detailed descriptions, those skilled in the art can clearly understand the present application and can implement the present application. Without departing from the principle of the present application, the features in different embodiments can be combined to obtain new implementation manners, or some features in certain embodiments can be replaced to obtain other preferred implementation manners.

[0019] To make the features and advantages of the present invention more obvious and understandable, specific embodiments are given below and described in detail in conjunction with the accompanying drawings as follows: The power grid operation scheduling method based on big data and V2G technology provided by the embodiments of the present invention, as Figure 1 shown, specifically includes the following steps: Step 1: Use the new energy vehicle big data platform to obtain the historical operation data of each vehicle. Each frame of data includes various information such as VIN, vehicle model, vehicle driving or parking status, charge and discharge status, SOC, vehicle speed, time, cumulative mileage, longitude and latitude, etc.; Step 2: After distinguishing each vehicle based on the VIN field, extract the driving data frames from the extracted historical operation data frames based on the vehicle driving status, discharge status, and vehicle speed information; after sorting the extracted driving data frames according to the time information, divide them based on the continuously time-related driving data frames to obtain vehicle driving data segments; Step 3: Use the longitude and latitude information in the driving segments to match with the digital map information to identify the corresponding charging power supply points and their levels for different segments, and assign corresponding power supply point labels to each driving data segment; perform secondary division on the driving data segments based on the power supply point field so that the obtained segments correspond one by one to the coverage ranges of the charging power supply points in different regions of the city; Step 4: Process the segments after secondary division based on the power supply point labels, obtain the start and end times, driving mileage, start and end SOC of the segments, calculate the unit mileage power consumption and average vehicle speed within the segments, and establish corresponding segment libraries for each charging power supply point; Step 5: After normalizing the unit mileage power consumption of different vehicle models in the segment libraries corresponding to each charging power supply point, statistically calculate the cumulative power consumption of each charging power supply point at different times of each day; in view of the phenomenon that the vehicle energy consumption level, corresponding charging demand, and behavior may change groupwise at different times of winter or summer, here, for specific months, seasons, etc., or continuous time periods within a certain range of changes in environmental temperature and climate conditions, etc., statistically calculate the cumulative power consumption distribution of each charging power supply point at different times of each day, such as during the busy commuting peak or other idle times, and on this basis, determine the corresponding load increment of each charging power supply point during the peak power consumption period; Step 6: Match the V2G facilities within a certain distance based on the load increment of each charging power supply point at different times (it should be noted that each charging power supply point can also have the V2G function. In this case, only the coverage range served by itself or the lower-level transformer needs to be matched), and the platform sends a recommendation instruction to the vehicles with reverse charging function to go; the power grid determines the available capacity that each V2G facility is expected to provide based on the response of each vehicle to the instruction; Step 7: Based on the determined load increment and the available capacity of each V2G facility, according to the rated capacity of the transformers at each level of power supply points and the reverse charging electric energy that can be obtained by the battery energy storage stations at each V2G facility, comprehensively conduct power dispatching control for each charging power supply point and allocate corresponding charging power to it, while ensuring that the load at each level of power supply points does not exceed the rated value and the utilization rate of the charging piles under the power supply points.

[0020] In a preferred embodiment of the present invention, each charging power supply point includes a charging device, a transformer, and an energy router; wherein, the charging device is connected to the low-voltage side of the transformer, and the high-voltage side of the transformer is connected to the power supply line; each charging device is provided with an energy router, and the energy routers communicate with each other to form an energy dispatching and exchange network, which is used to adjust the available capacity of each level of charging power supply points in real time through the overall control of the power grid, specifically including: based on the determined load increment and the available capacity of the V2G facility, allocating the electric energy from the power grid and the energy storage vehicles to each charging power supply point matched by each V2G facility; increasing the available capacity of the high-load increment charging power supply points without matching V2G facilities; and controlling the total available capacity of each lower-level charging power supply point not to exceed the available capacity of any upper-level charging power supply point.

[0021] In a preferred embodiment of the present invention, each piece of information extracted from the historical operation data in Step 1 adopts the form of field tags; after data extraction, cleaning and preprocessing including data filling, interpolation, replacement, and elimination are respectively carried out for various data anomalies.

[0022] In a preferred embodiment of the present invention, in Step 3, specifically using the digital map information, the longitude and latitude of the vehicle are converted into the same format as the map and corresponding coordinate corrections are made, and the road section ranges covered by each level of charging power supply points in the map corresponding to the corrected coordinates are matched, thereby obtaining the corresponding power supply point tags.

[0023] In a preferred embodiment of the present invention, in Step 4, the electricity consumption per unit mileage within the segment is specifically calculated based on the SOC and cumulative mileage information at the start and end of the segment; the specific process of normalizing the electricity consumption per unit mileage of different vehicle models under the segment library corresponding to each charging power supply point in Step 5 includes: based on the vehicle model information, extracting all the vehicles with a specific vehicle model number of 1, 2, …, i - 1 from the segment library, and respectively corresponding unit mileage energy consumptions Q1, Q2, …, Q i-1 ,Q i , and calculating the average value E Q of the unit mileage energy consumption and the variance σ Q , and then for any segment n of the same vehicle model, through the formula: E nor =(Q n -EQ) / σ Q , calculating the normalized unit mileage energy consumption E nor; Subsequently, based on the start and end cumulative mileage of the segments, the cumulative power consumption value distribution corresponding to each charging power source point is statistically analyzed for specific periods such as specific months and seasons, or for each daily time period within a continuous time period when the ambient temperature and climate conditions vary within a certain range.

[0024] In a preferred embodiment of the present invention, when the power grid receives the response of a vehicle with reverse charging function in step six, it also assigns corresponding reverse charging pricing strategies to each vehicle based on the load increments of different charging power source points. For example, the electricity price for reverse charging from the V2G facility at higher load positions and / or time periods is increased, and this price adjustment means is used to promote the transfer of electric energy to high-demand and high-load areas.

[0025] In a preferred embodiment of the present invention, the determined load increments corresponding to each charging power source point during the peak electricity consumption are also used as a reference for the planning and construction of V2G facilities.

[0026] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions, specifically for loading and executing one or more instructions in the computer storage medium to implement the above method.

[0027] It should be further noted that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the above method. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0028] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0029] The above shows and describes the basic principles, main features, and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure will have various changes and improvements, and these changes and improvements all fall within the scope of the present disclosure claimed.

[0030] The present invention is not limited to the above best implementation manner. Anyone can obtain other various forms of power grid operation scheduling methods based on big data and V2G technology under the inspiration of the present invention. All equal changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope covered by the present invention.

Claims

1. A power grid operation scheduling method based on big data and V2G technology, characterized in that: Using the actual vehicle operation data to analyze the charging behaviors near the charging power supply points at all levels in the power grid, so as to estimate and obtain the distribution and dynamic changes of the incremental load in the power grid caused by the charging demand; on this basis, using the V2G facilities to send corresponding recommended instructions to the vehicles that can be used as energy storage sources, and enabling the power grid to perform power dispatching by means of the reverse charging electric energy obtained by the V2G facilities.

2. The power grid operation scheduling method based on big data and V2G technology according to claim 1, characterized in that: The specific method of using the actual vehicle operation data to analyze the charging behaviors near the charging power supply points at all levels in the power grid, so as to estimate and obtain the distribution and dynamic changes of the incremental load in the power grid caused by the charging demand is as follows: Obtain the historical operation data of each new energy vehicle. Each frame of data includes VIN, vehicle model, vehicle driving or parking status, charge and discharge status, SOC, vehicle speed, time, cumulative mileage, longitude and latitude. After differentiating each vehicle based on the VIN field, extract the driving data frames from the extracted historical operation data frames based on the vehicle driving status, discharge status, and vehicle speed information. After sorting the extracted driving data frames according to the time information, divide the continuous driving data frames based on time to obtain vehicle driving data segments; use the longitude and latitude information in the driving data segments to match with the digital map information to identify the corresponding charging power supply points and their levels for different segments, and assign corresponding power supply point labels to each driving data segment. Perform secondary division on the driving data segments based on the power supply point field, so that each obtained segment corresponds one-to-one to the coverage range of the charging power supply points in different regions of the city; process the segments after secondary division based on the power supply point labels, obtain the start and end times, driving mileage, start and end SOC of the segments, and calculate the unit mileage power consumption and average vehicle speed within the segments, and then establish a corresponding segment library for each charging power supply point; after normalizing the unit mileage power consumption of different vehicle models under the segment library corresponding to each charging power supply point, count the cumulative power consumption of each charging power supply point at different times of each day. For a continuous time period under given conditions, count the cumulative power consumption distribution of each charging power supply point at different times of each day, and on this basis, determine the corresponding load increment of each charging power supply point during the peak electricity consumption period.

3. The power grid operation scheduling method based on big data and V2G technology according to claim 2, wherein: The specific implementation method of using the V2G facilities to send corresponding recommended instructions to the vehicles that can be used as energy storage sources, and enabling the power grid to perform power dispatching by means of the reverse charging electric energy obtained by the V2G facilities is as follows: Match the V2G facilities within a certain distance based on the load increment of each charging power supply point at different times, and send a go-to recommended instruction to the vehicles with reverse charging function; the power grid determines the available capacity that each V2G facility is expected to provide based on the response of each vehicle to the instruction; based on the determined load increment and the available capacity of each V2G facility, perform comprehensive power dispatching control on each charging power supply point and allocate corresponding charging power to it.

4. The power grid operation scheduling method based on big data and V2G technology according to claim 2, characterized in that: Each of the charging power supply points includes a charging device, a transformer, and an electric energy router; wherein, the charging device is connected to the low-voltage side of the transformer, and the high-voltage side of the transformer is connected to the power supply line; each charging device is provided with an electric energy router, and the electric energy routers communicate with each other to form an electric energy scheduling and exchange network for real-time adjusting the available capacity of each level of charging power supply points through the overall control of the power grid. The control process specifically includes: based on the determined load increment and the available capacity of the V2G facilities, allocating the electric energy from the power grid and the energy storage vehicles to each charging power supply point matched with each V2G facility; increasing the available capacity of the high-load increment charging power supply points without matched V2G facilities; and controlling the total available capacity of each lower-level charging power supply point not to exceed the available capacity of any upper-level charging power supply point.

5. The power grid operation scheduling method based on big data and V2G technology according to claim 2, characterized in that: Each piece of information extracted from the historical operation data is in the form of a field tag; after the data extraction, cleaning preprocessing including data filling, interpolation, replacement, and elimination is respectively performed for various types of data anomalies.

6. The power grid operation scheduling method based on big data and V2G technology according to claim 2, characterized in that: The longitude and latitude information in the driving data segment is matched with the digital map information to identify the charging power supply points and their levels corresponding to different segments, and corresponding power supply point tags are assigned to each driving data segment. Specifically: using the digital map information, the longitude and latitude of the vehicle are converted into the same format as the map and corresponding coordinate corrections are made, and the road section ranges covered by each level of charging power supply points in the map corresponding to the corrected coordinates are matched, thereby obtaining the corresponding power supply point tags.

7. The power grid operation scheduling method based on big data and V2G technology according to claim 2, characterized in that: The electricity consumption per unit mileage within the segment is calculated based on the SOC at the start and end of the segment and the cumulative mileage information; the specific process of normalizing the electricity consumption per unit mileage of different vehicle models under the segment library corresponding to each charging power supply point includes: based on the vehicle model information, extract from the segment library all the vehicles with a specific vehicle model number of 1, 2, …, i - 1, and the corresponding electricity consumption per unit mileage Q1, Q2, …, Q i-1 , Q i , and calculate the average value E Q and variance σ Q of the electricity consumption per unit mileage. For any segment n of the same vehicle model, through the formula: E nor =(Q n - EQ) / σ Q , calculate the normalized electricity consumption per unit mileage E nor of the segment; combine the start and end cumulative mileage of the segment to count the distribution of the cumulative electricity consumption values corresponding to each charging power supply point for each daily time period in a continuous time period when the specific month, season, or environmental temperature and climate conditions change within a certain range.

8. The power grid operation scheduling method based on big data and V2G technology according to claim 2, characterized in that: After the power grid receives the response of the vehicle with reverse charging function, based on the load increments of different charging power supply points, corresponding reverse charging pricing strategies are assigned to each vehicle.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the steps of the power grid operation scheduling method based on big data and V2G technology as described in any one of claims 1-8 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the power grid operation scheduling method based on big data and V2G technology as described in any one of claims 1-8 are implemented.

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