Electric energy distribution network method and system based on high-resolution numerical weather forecast

Abstract

The invention relates to an electric energy distribution network method and system based on high-resolution numerical weather forecast, and relates to the technical field of electric energy management. The method comprises the following steps: acquiring meteorological monitoring data in a preset first area where photovoltaic power generation equipment is located, and calculating to obtain preset key meteorological parameter change data; presetting the key meteorological parameter change data to obtain predicted generating capacity data; calculating a difference value between the predicted generating capacity data and the previous power consumption statistical data to obtain power supply gap / redundant data; key meteorological parameters in a preset second area are monitored in real time, sampling monitoring data are acquired, and whether the change rate of the sampling monitoring data is out of a preset change rate range or not is judged; and if yes, acquiring the meteorological detection data in the preset first area again, and calculating to obtain power supply gap / redundant data. According to the invention, the monitoring cost required by high-precision meteorological prediction in electric energy prediction can be effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of electric energy management technology, and in particular to an electric energy distribution network method, system, computer equipment, storage medium and computer program product based on high-resolution numerical weather forecasting. Background Art

[0002] With the development of meteorological monitoring and forecasting technology, human beings have made great progress in their ability to predict meteorological changes. The photovoltaic power generation industry, which is restricted by meteorological conditions, has ushered in a new turning point with the further evolution and improvement of high-resolution weather forecasts and large meteorological forecast models.

[0003] In traditional technologies, photovoltaic power generation is usually used as a supplement to traditional thermal power and hydropower because of its poor power supply stability and obvious influence of weather. It is usually not able to serve as the main power supply, resulting in large areas of photovoltaic power plants being idle. In order to accurately predict the meteorological changes in the areas where photovoltaic power plants are located, it is necessary to conduct long-term detection of multiple parameters in the area and combine the data for high-frequency forecasting calculations. The cost of the meteorological monitoring network and data processing computing power required to complete this task is extremely high, making it difficult to promote. Summary of the invention

[0004] Based on this, it is necessary to provide an electric energy distribution network method, system, computer equipment, computer-readable storage medium and computer program product based on high-resolution numerical weather forecasting that can save monitoring costs in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for power distribution network based on high-resolution numerical weather forecast, comprising: Acquire meteorological monitoring data in a preset first area where the photovoltaic power generation equipment is located, and input the meteorological monitoring data into a preset meteorological prediction model to calculate preset key meteorological parameter change data; Substituting the preset key meteorological parameter change data into a preset power generation prediction model to output predicted power generation data; The predicted power generation data is calculated with the previous power consumption statistical data of the target power supply area to obtain the power supply gap / redundancy data of the target power supply area; Performing real-time monitoring of some or all of the key meteorological parameters in the preset second area to obtain sampled monitoring data, calculating the change rate of the sampled monitoring data, and determining whether the change rate of the sampled monitoring data is outside a preset change rate range; If so, the meteorological monitoring data in the preset first area is obtained again to calculate the power supply gap / redundancy data of the target power supply area; the preset second area is an area located in the preset first area and is greatly affected by the surrounding climate.

[0006] In one embodiment, the specific steps of obtaining meteorological monitoring data in a preset first area where the photovoltaic power generation equipment is located, and inputting the meteorological monitoring data into a preset meteorological prediction model to calculate preset key meteorological parameter change data include: Acquiring real-time meteorological monitoring data within a preset first area at a first resolution; The real-time meteorological monitoring data is input into a preset meteorological forecasting model for calculation to obtain a first meteorological forecast result. In one embodiment, the specific steps of performing real-time monitoring of some or all of the key meteorological parameters in the preset second area to obtain sampled monitoring data include: The sampling monitoring data within the preset second area is obtained with a preset second resolution, and the relationship between the preset second resolution and the preset first resolution satisfies the following relationship: Q1*S1 / S2≥Q2≥Q1, wherein S1 is the area of ​​the preset first area, Q1 is the resolution of the preset first area, S2 is the area of ​​the preset second area, and Q2 is the resolution of the preset second area.

[0007] In one embodiment, the predicted power generation data is data on the power generation of the photovoltaic power generation equipment over a period of time in the future, and the past power consumption statistical data is data on the power consumption of the target power supply area over time; The specific steps of performing difference calculation between the predicted power generation data and the previous power consumption statistical data of the target power supply area to calculate the power supply gap / redundancy data of the target power supply area include: The predicted power generation data is subtracted from the previous power consumption statistics and then integrated to calculate the power supply gap / redundancy data.

[0008] In one of the embodiments, the method further includes: determining whether the power supply gap / redundancy data is gap type data or redundant type data; If the power supply gap / redundancy data is gap type data, then the used power stock data of the preset power storage device is obtained, and it is determined whether the used power stock data is higher than the power supply gap / redundancy data; If so, the preset power storage device is set to the power supply mode before the power supply gap occurs to supplement the power supply gap.

[0009] In one of the embodiments, the method further includes: if the power supply gap / redundancy data is redundant data, obtaining unused power stock data of a preset power storage device, and determining whether the unused power stock data is higher than the power supply gap / redundancy data; If so, the preset power storage device is set to the power storage mode before the power shortage occurs to recover the power supply redundancy.

[0010] In a second aspect, the present application also provides an electric energy distribution network system based on high-resolution numerical weather forecasting, comprising: A meteorological data detection module is provided in a preset first area and is used to obtain meteorological monitoring data and sampling monitoring data; The meteorological forecast module is used to store and run the preset meteorological forecast model, and calculate the preset key meteorological parameter change data based on the input meteorological monitoring data; Photovoltaic power generation equipment control module, used to control photovoltaic power generation equipment, and store and run preset power generation prediction models, and calculate predicted power generation data based on input preset key meteorological parameter change data; The data storage and processing module is used to store the previous electricity consumption statistics and predicted power generation data of the target power supply area, calculate the power supply gap / redundancy data based on the predicted power generation data and the previous electricity consumption statistics, and store the sampled monitoring data to calculate the change rate of the sampled monitoring data, and determine whether the change rate of the sampled monitoring data is outside the preset change rate range.

[0011] In a third aspect, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the electric energy distribution method based on high-resolution numerical weather forecasting when executing the computer program.

[0012] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the electric energy distribution network method based on high-resolution numerical weather forecasting are implemented.

[0013] In a fifth aspect, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the electric energy distribution network method based on high-resolution numerical weather forecasting.

[0014] The above-mentioned electric energy distribution network method, device, computer equipment, storage medium and computer program product based on high-resolution numerical weather forecast, through real-time monitoring of the preset second area which is relatively small and susceptible to weather changes, when the change rate is abnormal, comprehensive data detection is performed on the preset first area and the predicted power generation is calculated to ensure normal power supply. Since the preset second area is part of the preset first area, it can make full use of the existing meteorological data detection module in the area, adopt small-scale short-period sampling monitoring when the meteorological changes are not large, and conduct high-resolution monitoring of the entire area after major changes occur, thereby making full use of the resources of existing equipment and reducing the cost of meteorological monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a flow chart of a method for power distribution network based on high-resolution numerical weather forecasting in one embodiment; Figure 2 An application environment diagram of an electric energy distribution network method based on high-resolution numerical weather forecasting in one embodiment; Figure 3 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] The electric energy distribution network method based on high-resolution numerical weather forecast provided in the embodiment of the present application can be applied to Figure 2 In the application environment shown. The terminal communicates with the server through the network. The data storage system can store data that the server needs to process. The data storage system can be integrated on the server, or placed on the cloud or other network servers. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablets, IoT devices, and portable wearable devices, and the server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0018] In one embodiment, Figure 1 As shown in the figure, a method for power distribution network based on high-resolution numerical weather forecast is provided. Figure 2 The server in the example is used to illustrate the following steps: Step A: Acquire meteorological monitoring data in a preset first area where the photovoltaic power generation equipment is located, and input the meteorological monitoring data into a preset meteorological prediction model to calculate preset key meteorological parameter change data.

[0019] Among them, photovoltaic power generation equipment is a power generation device that converts light energy into electrical energy through the photoelectric effect, such as solar panels and their peripheral supporting equipment. The preset first area is an area for setting up photovoltaic power generation equipment, such as a photovoltaic power field. The meteorological monitoring data is meteorological parameter data related to photovoltaic power generation collected by various sensors. The types of meteorological parameters include but are not limited to light intensity, cloud distribution, wind speed, wind direction, etc. Specifically, when executing step A, the real-time meteorological monitoring data in the preset first area is obtained at a first resolution, and the real-time meteorological monitoring data is input into the preset meteorological prediction model for calculation to obtain the first meteorological prediction result. The first resolution is the distribution density of meteorological detection data in the preset first area. The preset meteorological prediction model can adopt an existing meteorological prediction large model, such as the Pangu meteorological prediction large model.

[0020] Step B: Substituting the preset key meteorological parameter change data into a preset power generation prediction model to output predicted power generation data.

[0021] Among them, the preset power generation prediction model is a mathematical model pre-stored in the storage space, which is used to predict the power generation of photovoltaic power generation equipment in a period of time in the future by combining data such as light intensity and sunshine. The specific predicted power generation data is the data of the change of power generation of photovoltaic power generation equipment over time in a period of time in the future. The change of power generation over time can be represented by a function f(t) about t.

[0022] Step C: performing difference calculation between the predicted power generation data and the previous power consumption statistical data of the target power supply area to calculate the power supply gap / redundancy data of the target power supply area.

[0023] The past electricity consumption statistics are data on the daily electricity consumption of the target power supply area changing with time, and the data on the daily electricity consumption of the target power supply area changing with time can be represented by a function g(t) about t. The specific steps of step C also include performing an integral operation after performing a difference operation between the predicted power generation data f(t) and the past electricity consumption statistics g(t) to calculate the power supply gap / redundancy data.

[0024] Step D: Monitor some or all of the key meteorological parameters in the preset second area in real time to obtain sampled monitoring data, calculate the change rate of the sampled monitoring data and determine whether the change rate of the sampled monitoring data is outside the preset change rate range.

[0025] The preset second area is an area located within the preset first area and greatly affected by the surrounding climate, such as an area located upwind of the wind on that day and easily blocked by clouds. In actual use, the preset second area of ​​the day can be determined by artificial setting. When executing step D, the sampling detection data within the preset second area is obtained with the preset second resolution. The relationship between the preset second resolution and the preset first resolution satisfies the following relationship: Q1*S1 / S2≥Q2≥Q1, where S1 is the area of ​​the preset first area, Q1 is the resolution of the preset first area, S2 is the area of ​​the preset second area, and Q2 is the resolution of the preset second area.

[0026] Step E: If yes, the meteorological monitoring data in the preset first area is obtained again to calculate the power supply gap / redundancy data of the target power supply area.

[0027] Step F: Determine whether the power supply gap / redundancy data is gap type data or redundant type data.

[0028] Step G: If the power supply gap / redundancy data is gap type data, obtain the used power stock data of the preset power storage device, and determine whether the used power stock data is higher than the power supply gap / redundancy data; if so, set the preset power storage device to power supply mode before the power supply gap occurs to supplement the power supply gap.

[0029] Step H: If the power supply gap / redundancy data is redundant data, obtain the unused power stock data of the preset power storage device, and determine whether the unused power stock data is higher than the power supply gap / redundancy data; if so, set the preset power storage device to power storage mode before the power supply gap occurs to recover power supply redundancy.

[0030] By conducting real-time monitoring of the preset second area which is relatively small and easily affected by weather changes, when the change rate is abnormal, a comprehensive data detection is conducted on the preset first area and the predicted power generation is calculated to ensure normal power supply. Since the preset second area is part of the preset first area, the existing meteorological data detection module in the area can be fully utilized. When the meteorological changes are not large, small-scale short-period sampling monitoring is adopted. After major changes occur, high-resolution monitoring of the entire area is carried out, thereby making full use of the resources of existing equipment and reducing the cost of meteorological monitoring.

[0031] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0032] Based on the same inventive concept, the embodiment of the present application also provides a high-resolution numerical weather forecast-based power distribution network system for implementing the above-mentioned high-resolution numerical weather forecast-based power distribution network method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the power distribution network system based on high-resolution numerical weather forecast provided below can refer to the limitations of the power distribution network method based on high-resolution numerical weather forecast in the above text, and will not be repeated here.

[0033] In one embodiment, a power distribution network system based on high-resolution numerical weather forecast is provided, comprising: a meteorological data detection module, a meteorological prediction module, a photovoltaic power generation equipment control module and a data storage and processing module, wherein: A meteorological data detection module is provided in a preset first area and is used to obtain meteorological monitoring data and sampling monitoring data; The meteorological forecast module is used to store and run the preset meteorological forecast model, and calculate the preset key meteorological parameter change data based on the input meteorological monitoring data; Photovoltaic power generation equipment control module, used to control photovoltaic power generation equipment, and store and run preset power generation prediction models, and calculate predicted power generation data based on input preset key meteorological parameter change data; The data storage and processing module is used to store the previous electricity consumption statistics and predicted power generation data of the target power supply area, calculate the power supply gap / redundancy data based on the predicted power generation data and the previous electricity consumption statistics, and store the sampled monitoring data to calculate the change rate of the sampled monitoring data, and determine whether the change rate of the sampled monitoring data is outside the preset change rate range.

[0034] Each module in the above-mentioned power distribution network system based on high-resolution numerical weather forecast can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0035] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for power distribution network based on high-resolution numerical weather forecasting is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0036] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0037] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented: Step A: Acquire meteorological monitoring data in a preset first area where the photovoltaic power generation equipment is located, and input the meteorological monitoring data into a preset meteorological prediction model to calculate preset key meteorological parameter change data.

[0038] Step B: Substituting the preset key meteorological parameter change data into a preset power generation prediction model to output predicted power generation data.

[0039] Step C: performing difference calculation between the predicted power generation data and the previous power consumption statistical data of the target power supply area to calculate the power supply gap / redundancy data of the target power supply area.

[0040] Step D: Monitor some or all of the key meteorological parameters in the preset second area in real time to obtain sampled monitoring data, calculate the change rate of the sampled monitoring data and determine whether the change rate of the sampled monitoring data is outside the preset change rate range.

[0041] Step E: If yes, the meteorological monitoring data in the preset first area is obtained again to calculate the power supply gap / redundancy data of the target power supply area; the preset second area is an area located in the preset first area and is greatly affected by the surrounding climate.

[0042] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: Step F: Determine whether the power supply gap / redundancy data is gap type data or redundant type data.

[0043] Step G: If the power supply gap / redundancy data is gap type data, obtain the used power stock data of the preset power storage device, and determine whether the used power stock data is higher than the power supply gap / redundancy data; if so, set the preset power storage device to power supply mode before the power supply gap occurs to supplement the power supply gap.

[0044] Step H: If the power supply gap / redundancy data is redundant data, obtain the unused power stock data of the preset power storage device, and determine whether the unused power stock data is higher than the power supply gap / redundancy data; if so, set the preset power storage device to power storage mode before the power supply gap occurs to recover power supply redundancy.

[0045] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer-readable storage medium is executed by a processor on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0046] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer can execute the corresponding content of the aforementioned method embodiment.

[0047] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0048] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0049] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for power distribution network based on high-resolution numerical weather forecast, characterized in that: The method comprises: Acquire meteorological monitoring data in a preset first area where the photovoltaic power generation equipment is located, and input the meteorological monitoring data into a preset meteorological prediction model to calculate preset key meteorological parameter change data; Substituting the preset key meteorological parameter change data into a preset power generation prediction model to output predicted power generation data; The predicted power generation data is calculated with the previous power consumption statistical data of the target power supply area to obtain the power supply gap / redundancy data of the target power supply area; Performing real-time monitoring of some or all of the key meteorological parameters in the preset second area to obtain sampled monitoring data, calculating the change rate of the sampled monitoring data, and determining whether the change rate of the sampled monitoring data is outside a preset change rate range; If so, the meteorological monitoring data in the preset first area is obtained again to calculate the power supply gap / redundancy data of the target power supply area; the preset second area is an area located in the preset first area and is greatly affected by the surrounding climate.

2. The method for power distribution network based on high-resolution numerical weather forecast according to claim 1, characterized in that: The specific steps of obtaining the meteorological monitoring data in the preset first area where the photovoltaic power generation equipment is located, and inputting the meteorological monitoring data into the preset meteorological prediction model to calculate the preset key meteorological parameter change data include: Acquiring real-time meteorological monitoring data within a preset first area at a first resolution; The real-time meteorological monitoring data is input into a preset meteorological forecasting model for calculation to obtain a first meteorological forecast result.

3. The method for power distribution network based on high-resolution numerical weather forecast according to claim 2, characterized in that: The specific steps of real-time monitoring of some or all of the key meteorological parameters in the preset second area to obtain sampled monitoring data include: The sampling monitoring data within the preset second area is obtained with a preset second resolution, and the relationship between the preset second resolution and the preset first resolution satisfies the following relationship: Q1*S1 / S2≥Q2≥Q1, wherein S1 is the area of ​​the preset first area, Q1 is the resolution of the preset first area, S2 is the area of ​​the preset second area, and Q2 is the resolution of the preset second area.

4. The method for power distribution network based on high-resolution numerical weather forecast according to claim 3, characterized in that: The predicted power generation data is the data of the power generation of the photovoltaic power generation equipment changing with time in a certain period of time in the future, and the past power consumption statistical data is the data of the daily power consumption of the target power supply area changing with time; The specific steps of performing difference calculation between the predicted power generation data and the previous power consumption statistical data of the target power supply area to calculate the power supply gap / redundancy data of the target power supply area include: The predicted power generation data is subtracted from the previous power consumption statistics and then integrated to calculate the power supply gap / redundancy data.

5. The method for power distribution network based on high-resolution numerical weather forecast according to any one of claims 1 to 4, characterized in that: Also includes: Determine whether the power supply gap / redundancy data is gap type data or redundant type data; If the power supply gap / redundancy data is gap type data, then the used power stock data of the preset power storage device is obtained, and it is determined whether the used power stock data is higher than the power supply gap / redundancy data; If so, the preset power storage device is set to the power supply mode before the power supply gap occurs to supplement the power supply gap.

6. The method for power distribution network based on high-resolution numerical weather forecast according to claim 5, characterized in that: Also includes: If the power supply gap / redundancy data is redundant data, then the unused power stock data of the preset power storage device is obtained, and it is determined whether the unused power stock data is higher than the power supply gap / redundancy data; If so, the preset power storage device is set to the power storage mode before the power supply gap occurs to recover the power supply redundancy.

7. An electric energy distribution network system based on high-resolution numerical weather forecast, characterized in that: The system comprises: A meteorological data detection module is provided in a preset first area and is used to obtain meteorological monitoring data and sampling monitoring data; The meteorological forecast module is used to store and run the preset meteorological forecast model, and calculate the preset key meteorological parameter change data based on the input meteorological monitoring data; Photovoltaic power generation equipment control module, used to control photovoltaic power generation equipment, and store and run preset power generation prediction models, and calculate predicted power generation data based on input preset key meteorological parameter change data; The data storage and processing module is used to store the previous electricity consumption statistics and predicted power generation data of the target power supply area, calculate the power supply gap / redundancy data based on the predicted power generation data and the previous electricity consumption statistics, and store the sampled monitoring data to calculate the change rate of the sampled monitoring data, and determine whether the change rate of the sampled monitoring data is outside the preset change rate range.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the electric energy distribution network method based on high-resolution numerical weather forecasting described in any one of claims 1 to 6 are implemented.

9. 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 energy distribution network method based on high-resolution numerical weather forecasting described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the electric energy distribution network method based on high-resolution numerical weather forecasting described in any one of claims 1 to 6 are implemented.

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