A high-load resource deployment method and system applied to a grid-connected virtual power plant

Abstract

The application discloses a kind of high load resource allocation method and system applied to grid-connected virtual power plant, it is related to power grid resource allocation technical field, including collection data and pre-processing, the data of pre-processing is combined with the demand of user body volume and divided grade, and according to grade, edge computing is called with level to handle resource allocation under high load power grid condition, after dispatching, data is transmitted to cloud for secondary distribution, according to the data of resource scheduling of grade is encrypted operation, the data of dispatching completion is encrypted storage and transmission to virtual power plant.The application can more accurately predict power grid demand, so as to optimize the allocation and use of resources, not only improve the efficiency of resource scheduling, but also enhance the flexibility and adaptability of power grid when facing demand fluctuation and change conditions.Especially in high load state, it can effectively manage and dispatch resources, reduce the risk of power grid overload, so as to improve the stability of overall power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid resource allocation technology, and in particular to a high-load resource allocation method and system applied to grid-connected virtual power plants. Background Technology

[0002] With the rapid development of smart grid technology, advanced information and communication technologies have been integrated, improving the efficiency, reliability, and sustainability of the power grid. With the widespread adoption of renewable energy, virtual grids, as an emerging power grid management system, are becoming an important way to solve the integration and optimization of distributed energy resources. Furthermore, virtual grids, through high levels of digitization and automation, achieve more flexible and efficient power grid operation, utilizing advanced technologies such as edge computing, cloud computing, big data analytics, and machine learning to cope with the ever-changing demands of the power grid.

[0003] However, existing technologies still have shortcomings in terms of grid resource management and dispatch efficiency under high load conditions, especially in terms of the accuracy of resource allocation, the ability to cope with demand fluctuations, and the overall stability of the system. Further optimization and innovation are needed for existing technologies. Furthermore, the integration of emerging renewable energy and distributed energy resources makes grid operation more complex. These resources are often unevenly distributed and significantly affected by environmental factors such as weather, making grid management more difficult. Traditional centralized grid management methods are gradually proving insufficient in terms of efficiency and flexibility. Summary of the Invention

[0004] In view of the problems existing in the high-load resource allocation methods and systems applied to grid-connected virtual power plants, this invention is proposed.

[0005] Therefore, the problem that this invention aims to solve is that resources are usually unevenly distributed and greatly affected by environmental factors such as weather, which leads to difficulties in power grid management and resource allocation under high load conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a high-load resource allocation method applied to a grid-connected virtual power plant, comprising: collecting and preprocessing data; classifying the preprocessed data into levels based on user volume requirements, and using edge computing to call resources at the same level according to the level to handle resource allocation under high-load grid conditions; transmitting the scheduled data to the cloud for secondary allocation; encrypting the resource scheduling data according to the level, and encrypting and storing the scheduled data and transmitting it to the virtual power plant.

[0008] As a preferred embodiment of the high-load resource allocation method applied to grid-connected virtual power plants described in this invention, the classification is based on the characteristics of user energy consumption demand, historical electricity data, real-time grid load, and predicted future grid demand. The classification is then based on these scores, expressed by the following formula:

[0009] S i =a*E i +b*H i +c*(L / F)

[0010] Where Ei represents energy demand; Hi represents historical electricity consumption data; L represents real-time grid load; and F represents predicted future grid demand.

[0011] As a preferred embodiment of the high-load resource allocation method applied to grid-connected virtual power plants described in this invention, the following is defined: peer-to-peer (PTP) refers to resource allocation between peers based on a scoring system, i.e., the first level allocates resources to the first level, and the second level allocates resources to the second level. If special circumstances exist, such as the first level's resources being insufficient for current scheduling, or the scheduling distance being too far leading to excessive resource consumption, a maximum distance is set to ensure that the allocated resource is the optimal choice. The maximum distance is the maximum distance at which resources can be scheduled while ensuring grid stability and efficiency. This maximum distance is calculated using a mathematical model that considers grid load, transmission loss, resource type and priority, grid topology, user electricity consumption patterns, and weather factors.

[0012] As a preferred embodiment of the high-load resource allocation method applied to grid-connected virtual power plants described in this invention, the calculation of the mathematical model includes:

[0013] The calculation of grid load and transmission loss is as follows:

[0014] Leff = L × (1 - α × d)

[0015] Where d is the distance, L eff It is the effective grid load after considering transmission loss, where L is the grid load and α is the transmission loss coefficient;

[0016] Resource type and priority factors are represented as follows:

[0017]

[0018]

[0019] Among them, w i and v i It is the weight, T i and P i These represent resources of different types and priorities.

[0020] Topology of power grid topology factor Represented as,

[0021] Topology factor =g(Topology)

[0022] g(Topology) = β1 × N com +β2×D path +β3×R path

[0023] Where, N com D represents the number of connections. path R represents the diversity of paths. path The redundancy of the path is represented by β1, β2, and β3, which are weights determined based on the characteristics of the power grid.

[0024] User electricity consumption patterns and weather factors are represented as follows:

[0025] U factor =h(U)

[0026] W fator = k(W)×Ppeak+γ

[0027] h(U) = γ1 × Ppeak + γ2 × P off-peak

[0028] k(W) = δ1×T + δ2×H + δ3×V

[0029] Where Ppeak represents the electricity demand during peak hours, P off-peak The values ​​represent electricity demand during off-peak hours. γ1 and γ2 are weights determined based on user behavior pattern analysis. T is temperature, H is humidity, V is wind speed, and δ1, δ2, and δ3 are weights determined based on climate data analysis.

[0030] The calculation of the combined farthest distance is expressed as follows:

[0031] Dmax = Leff × T facotor ×P facotor ×Topology facotor ×U facotor ×W facotor .

[0032] As a preferred embodiment of the high-load resource allocation method applied to a grid-connected virtual power plant as described in this invention, the same-level call further includes dividing the second level into high priority, medium priority, and low priority. If the resource call of the first level does not meet the actual needs of the level, or the call distance exceeds the maximum distance, the call is extended downwards to the high priority of the second level, but only one level is extended downwards, that is, the first level does not call the medium priority of the second level. If the high priority of the second level does not meet the actual needs of the level, or the call distance exceeds the maximum distance, the call is extended downwards to the medium priority of the second level. At this time, it should be noted that the medium priority resources called do not participate in the above-mentioned call of the first level to the high priority of the second level. If the medium priority of the second level does not meet the actual needs of the level, or the call distance exceeds the maximum distance, the call is extended downwards to the low priority of the second level. At this time, the low priority resources called do not participate in the call to the high priority, but only call the resources of the current level and the next level.

[0033] As a preferred embodiment of the high-load resource allocation method applied to grid-connected virtual power plants described in this invention, the secondary allocation refers to transmitting the data after the call to the cloud for secondary detection and call; the resource sufficiency rate is set as the ratio between the actual available resources and the demand; based on historical data, one or more resource sufficiency rates are determined as reference points according to levels; considering the safety and stability of power grid operation, big data analysis technology is applied in the cloud to formulate a secondary call strategy; and considering the overall efficiency, stability and long-term impact of the power grid, the scheduling strategy formulated in the cloud is quickly distributed to the edge computing nodes through a secure channel to control the relevant equipment to perform secondary scheduling, and the execution effect is monitored in real time.

[0034] As a preferred embodiment of the high-load resource allocation method applied to a grid-connected virtual power plant according to the present invention, the encryption includes: performing encryption operations on different levels according to the level division; during the acquisition process, the first-level data is encrypted using a digital signature to confirm that the source of the data is trustworthy; simultaneously, for all levels of data, SSL / TLS protocol encryption is applied during the scheduling process to ensure the confidentiality and integrity of the data during scheduling; AES or RSA encryption is used to complete the data storage of the first and second levels, with RSA encryption used for the first-level data and AES encryption used for the second-level data; the encrypted data is stored in the virtual power plant; and the third-level data is not encrypted and is stored at the corresponding edge computing point.

[0035] Secondly, embodiments of the present invention provide a high-load resource allocation system for a grid-connected virtual power plant, comprising: a collection module for collecting data from various nodes and sensors and performing preprocessing; an allocation module for allocating resources under high-load grid conditions; and a communication and storage module for communicating the allocated data to the virtual power plant and storing the relevant data.

[0036] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the steps of the above-described high-load resource allocation method applied to a grid-connected virtual power plant.

[0037] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any of the steps of the above-described high-load resource allocation method applied to a grid-connected virtual power plant.

[0038] The beneficial effects of this invention are that by introducing advanced algorithms and data analysis, it can more accurately predict grid demand, thereby optimizing resource allocation and utilization. This not only improves the efficiency of resource scheduling but also enhances the grid's flexibility and adaptability in the face of demand fluctuations and changing conditions. Especially under high load conditions, it can effectively manage and schedule resources, reducing the risk of grid overload and thus improving the overall stability of the grid. Simultaneously, in the event of emergencies or extreme loads, this method can rapidly adjust resource allocation, ensuring the continuity and reliability of grid operation, which is of great significance for supporting sustainable energy development and reducing environmental impact. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0040] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0041] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0044] Example 1

[0045] Reference Figure 1 This is the first embodiment of the present invention, which provides a method for detecting and analyzing power distribution network faults under extreme weather conditions, including:

[0046] S1: Collect and preprocess data.

[0047] Parameter data of the power grid under high load conditions are collected from various nodes and sensors. Sensor edge computing refers to integrating a micro edge computing unit into each sensor, which processes the raw data collected by the sensor once.

[0048] The collected data is preprocessed, cleaned, and noise and outliers are removed to ensure the accuracy and reliability of the data. Preliminary analysis of the data is performed to identify key trends and patterns and ensure the real-time nature of the data so that the system can respond based on the latest information.

[0049] S2: The pre-processed data is classified into levels based on the user's needs, and edge computing is used to handle resource allocation under high-load power grid conditions according to the level.

[0050] Under high load conditions, the user resource classification of the virtual power grid is based on multiple factors, including user energy consumption demand, historical electricity consumption data, real-time grid load conditions, and predicted future grid demand. The defining characteristic data includes:

[0051] Energy demand (Ei): The average energy demand per user.

[0052] Historical electricity consumption data (Hi): The user's electricity consumption patterns and electricity consumption over a certain period of time in the past.

[0053] Real-time grid load (L): The current total load of the grid.

[0054] Forecasting future grid demand (F): Future demand forecasts based on historical and current data.

[0055] A comprehensive scoring formula is defined as follows:

[0056] S i =a*E i +b*H i +c*(L / F)

[0057] Among them, a, b, and c are weighting factors. By combining historical data for simulation, different weight combinations are set to observe the resource allocation effect and power grid stability under different weight combinations. The weight combination that performs best in the simulation is selected as the basis for practical application.

[0058] Preferably, a, b, and c are not fixed and need to be dynamically adjusted based on the scoring and feature data. If the energy demand is too high, such as in public places like hospitals, then a should be increased relative to the application baseline. If the ratio of real-time grid load and predicted future grid demand decreases, such as during school holidays, then c should be decreased accordingly. The same applies to other situations.

[0059] Based on the scores, the top 30% are initially positioned in the first level, 30% to 80% are set in the second level, and the remaining 20% ​​are set in the third level.

[0060] Since energy consumption needs are considered during the classification stage, if the first level calls the resources of the second level, it may cause the second level to be unable to call them. Furthermore, the energy consumption needs of the first level are greater than those of the second level, resulting in excessive energy loss when calling resources. Therefore, this invention adopts the same-level calling principle for resource calls, that is, the first level calls the resources of the first level.

[0061] Furthermore, this invention addresses resource scheduling under high load conditions. Special circumstances may arise, such as the inability of first-level resources to meet current scheduling needs, or excessive scheduling distances leading to excessive resource consumption. To address these issues, a threshold is set to prevent excessive resource consumption. The maximum distance is defined as the maximum distance at which resources can be scheduled while ensuring grid stability and efficiency. A mathematical model is constructed to calculate this distance, taking into account grid load, transmission loss, resource type and priority, grid topology, user electricity consumption patterns, and weather factors.

[0062] The calculations for grid load and transmission loss are as follows:

[0063] Leff = L × (1 - α × d)

[0064] Where d is the distance, Leff is the effective grid load after considering transmission loss, L is the grid load, and α is the transmission loss coefficient.

[0065] Resource type and priority factors are represented as follows:

[0066]

[0067]

[0068] Among them, w i and v j It is the weight, T iand P i These represent resources of different types and priorities.

[0069] Topology of power grid topology factor Represented as,

[0070] Topology factor =g(Topology)

[0071] g(Topology) = β1 × N com +β2×D path +β3×R path

[0072] Where, N com D represents the number of connections. path R represents the diversity of paths. path The redundancy of the path is represented by β1, β2, and β3, which are weights determined based on the characteristics of the power grid.

[0073] User electricity consumption patterns and weather factors are represented as follows:

[0074] U factor =h(U)

[0075] W fator = k(W)×Ppeak+γ

[0076] h(U) = γ1 × Ppeak + γ2 × P off-peak

[0077] k(W) = δ1×T + δ2×H + δ3×V

[0078] Where Ppeak represents the electricity demand during peak hours, P off-peak The values ​​represent electricity demand during off-peak hours. γ1 and γ2 are weights determined based on user behavior pattern analysis. T is temperature, H is humidity, V is wind speed, and δ1, δ2, and δ3 are weights determined based on climate data analysis.

[0079] The calculation of the combined farthest distance is expressed as follows:

[0080] Dmax = Leff × T facotor ×P facotor ×Topology facotor ×U facotor ×W facotor

[0081] Furthermore, since the scheduling resources of the first and third levels are insufficient, while the scheduling resources of the second level are abundant, the resources of the second level are further divided into high priority, medium priority, and low priority, corresponding to the top 30%, 30% to 70%, and the remaining 30% of the second level, respectively. This invention adopts a peer-to-peer (PTP) principle. If the first-level resource call does not meet the actual needs of that level, or the call distance exceeds the maximum distance, the call is extended downwards to the high priority of the second level, but only one level is called downwards; that is, the first level does not call the medium priority of the second level. If the high priority of the second level does not meet the actual needs of that level, or the call distance exceeds the maximum distance, the call is extended downwards to the medium priority of the second level. In this case, it should be noted that the medium priority resources called do not participate in the first level's call to the high priority of the second level. If the medium priority of the second level does not meet the actual needs of that level, or the call distance exceeds the maximum distance, the call is extended downwards to the low priority of the second level. In this case, the low priority resources called do not participate in the call to the high priority, and the same principle applies downwards, only calling resources of the current level and the next level.

[0082] S3: Transmit the scheduled data to the cloud for secondary distribution.

[0083] Since resource calls only include calls to resources at the current level and the next level, if the calls to resources at the current level and the next level are still insufficient to meet the requirements for resource calls at the current level, the data after the call will be transmitted to the cloud for secondary detection and calling.

[0084] The resource sufficiency rate is set as the ratio between actual available resources and demand. Based on historical data, one or more resource sufficiency rates are determined according to levels as reference points. Considering the safety and stability of power grid operation, cloud-based big data analytics is used to formulate secondary dispatch strategies, taking into account the overall efficiency, stability, and long-term impact of the power grid. The dispatch strategies formulated in the cloud are rapidly distributed to edge computing nodes through a secure channel to control relevant equipment for secondary dispatch, and the execution effect is monitored in real time.

[0085] S4: Encrypt the resource scheduling data according to the level, encrypt and store the completed scheduling data, and transmit it to the virtual power plant.

[0086] Encryption operations are performed on different levels according to the classification. During the acquisition process, for first-level data, public equipment or other important locations should use digital signatures to ensure that the received data has not been tampered with and to confirm that the source of the data is trustworthy. At the same time, encryption operations should be performed on data of all levels during the scheduling process to ensure the confidentiality and integrity of the data during the scheduling process. Due to the high-load resource scheduling, the SSL / TLS protocol is used to encrypt the data in the scheduling process of this invention to prevent the data from being intercepted or tampered with during transmission.

[0087] Furthermore, AES or RSA encryption is used to store the data. This refers to the data at the first and second levels. Since the first level data is significantly more important than the second level data and is smaller in size, RSA encryption is used for the first level data and AES encryption is used for the second level data. The encrypted data is then stored in a virtual power plant. Since the third level data is smaller in size and significantly less important than the first two levels, no encryption is performed, and it is stored at the corresponding edge computing point to facilitate resource scheduling for the third level.

[0088] In summary, this invention, by introducing advanced algorithms and data analysis, can more accurately predict grid demand, thereby optimizing resource allocation and utilization. This not only improves the efficiency of resource scheduling but also enhances the grid's flexibility and adaptability in the face of demand fluctuations and changing conditions. Especially under high load conditions, it can effectively manage and schedule resources, reducing the risk of grid overload and thus improving overall grid stability. Furthermore, in the event of emergencies or extreme loads, this method can rapidly adjust resource allocation, ensuring the continuity and reliability of grid operation, which is of great significance for supporting sustainable energy development and reducing environmental impact.

[0089] Example 2

[0090] Reference Figure 1 Based on the first embodiment, this embodiment further provides a high-load resource allocation system applied to a grid-connected virtual power plant, including:

[0091] The data collection module is used to collect and preprocess data from various nodes and sensors.

[0092] The allocation module is used to allocate resources under high-load power grid conditions.

[0093] The communication and storage module is used to communicate the allocated data to the virtual power plant and store the relevant data.

[0094] This embodiment also provides a computer device applicable to the high-load resource allocation method for grid-connected virtual power plants, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the high-load resource allocation method for grid-connected virtual power plants as proposed in the above embodiment.

[0095] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0096] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the high-load resource allocation method for grid-connected virtual power plants as proposed in the above embodiments.

[0097] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0098] Example 3

[0099] Based on the previous two embodiments, this embodiment provides a high-load resource allocation method for grid-connected virtual power plants. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0100] This invention outperforms existing technologies in terms of accuracy of resource allocation, ability to cope with demand fluctuations, overall system stability, and ability to handle emergencies or extreme loads. By introducing advanced algorithms and data analysis, it can more accurately predict grid demand, thereby optimizing resource allocation and utilization and improving resource scheduling efficiency, as shown in Table 1.

[0101] Table 1: Advantages Comparison Table

[0102]

[0103] This invention, through a comprehensive scoring formula and multi-dimensional data analysis, can more accurately handle resource allocation under high-load grid conditions, improving the accuracy of resource allocation and the stability of grid operation. These advantages are particularly evident under high-load conditions, effectively reducing the risk of grid overload and coping with demand fluctuations and changes, as illustrated in Table 2 through a more detailed data comparison.

[0104] Table 2 Data Comparison Table

[0105] Resource scheduling efficiency 70％ 90％ Response time (seconds) 30 seconds 10 seconds Average resource utilization rate 65％ 85％ Stability (failure rate) 10％ 3％ Demand forecast accuracy 75％ 95％ Adaptive adjustment capability generally high

[0106] This invention, through advanced algorithms and data analysis, significantly improves resource scheduling efficiency from 70% to 90%, and reduces response time from 30 seconds to 10 seconds, demonstrating faster response capabilities under high load conditions. Furthermore, average resource utilization also increases from 65% to 85%, indicating more efficient resource use.

[0107] In terms of system stability, the failure rate was reduced from 10% to 3%, enhancing the reliability of the power grid. The accuracy of demand forecasting was also improved from 75% to 95%, further demonstrating its superiority in resource management and forecasting.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A high-load resource allocation method applied to a grid-connected virtual power plant, characterized in that: include, Collect and preprocess data; The pre-processed data is categorized into levels based on user volume requirements, and edge computing is used to call the same level according to the level to handle resource allocation under high-load power grid conditions. Peer-to-peer (PTP) refers to the sharing of resources between peers based on their rating levels. That is, the first level shares resources with the first level, the second level shares resources with the second level, and the third level shares resources with the third level. If the resources of the first level cannot meet the current scheduling, or if the scheduling distance is too far and causes excessive consumption of scheduling resources, then the farthest distance is set to ensure that the resource called is the optimal choice. The maximum distance mentioned is the maximum distance at which resources can be dispatched under the premise of ensuring grid stability and efficiency. It is calculated by constructing a mathematical model that combines grid load, transmission loss, resource type and priority, grid topology, user electricity consumption patterns and weather factors. The same-level call also includes dividing the second level into high priority, medium priority, and low priority. If the resource call of the first level does not meet the actual needs of the level, or the call distance exceeds the maximum distance, the call is extended downward to the high priority of the second level, but only one level is extended downward. That is, the first level does not call the medium priority of the second level. If the high priority of the second level does not meet the actual needs of the level, or the call distance exceeds the maximum distance, the call is extended downward to the medium priority of the second level. At this time, it should be noted that the medium priority resources called do not participate in the first level's call to the high priority of the second level. If the medium priority of the second level does not meet the actual needs of the level, or the call distance exceeds the maximum distance, the call is extended downward to the low priority of the second level. At this time, the low priority resources called do not participate in the call to the high priority, and only the resources of the current level and the next level are called. The scheduled data is transmitted to the cloud for secondary distribution. The data for resource scheduling is encrypted according to the level, and the data after scheduling is completed is encrypted, stored, and transmitted to the virtual power plant.

2. The high-load resource allocation method applied to a grid-connected virtual power plant as described in claim 1, characterized in that: The grading is based on user energy consumption demand, historical electricity data, real-time grid load conditions, and predicted future grid demand. The grading is determined by a score, expressed as follows: Where Ei represents energy demand; Hi represents historical electricity consumption data; L represents real-time grid load; and F represents predicted future grid demand.

3. The high-load resource allocation method applied to a grid-connected virtual power plant as described in claim 2, characterized in that: The calculations of the mathematical model include, The calculation of grid load and transmission loss is as follows: Where d is the distance, This is the effective grid load after considering transmission losses, where L is the grid load. It is the transmission loss coefficient; Resource type and priority factors are represented as follows: in, and It is the weight, T i and P i These represent resources of different types and priorities. Impact on power grid topology Represented as, in, Indicates the number of connections. Indicating the diversity of paths, Indicates the redundancy of the path. , , The weights are determined based on the characteristics of the power grid; User electricity consumption patterns and weather factors are represented as follows: in, This indicates the electricity demand during peak hours. This indicates electricity demand during off-peak hours. and The weights are determined based on user behavior pattern analysis; T is temperature, H is humidity, V is wind speed, and δ1, δ2, and δ3 are weights determined based on climate data analysis. The calculation of the combined farthest distance is expressed as follows: 。 4. The high-load resource allocation method applied to a grid-connected virtual power plant as described in claim 3, characterized in that: The secondary allocation refers to transmitting the data after the call to the cloud for secondary detection and call. The resource sufficiency rate is set as the ratio between actual available resources and demand. Based on historical data, one or more resource sufficiency rates are determined as reference points according to different levels. Considering the safety and stability of power grid operation, cloud-based big data analysis technology is used to formulate secondary dispatch strategies. Taking into account the overall efficiency, stability and long-term impact of the power grid, the scheduling strategies formulated in the cloud are quickly distributed to edge computing nodes through a secure channel to control relevant equipment to perform secondary scheduling, and the execution effect is monitored in real time.

5. The high-load resource allocation method applied to a grid-connected virtual power plant as described in claim 4, characterized in that: The encryption includes, Encryption operations are performed on different levels according to the classification. During the acquisition process, the first level data is encrypted with digital signature to confirm that the source of the data is trustworthy. At the same time, for all levels of data, SSL / TLS protocol encryption is applied during the scheduling process to ensure the confidentiality and integrity of the data during the scheduling process. AES or RSA encryption is used to complete the data storage of the first and second levels. RSA encryption is used for the first level data and AES encryption is used for the second level data. The encrypted data is stored in the virtual power plant. The third level data is not encrypted and is stored at the corresponding edge computing point.

6. A high-load resource allocation system for a grid-connected virtual power plant, based on the high-load resource allocation method for a grid-connected virtual power plant as described in any one of claims 1 to 5, characterized in that: include, The data collection module is used to collect and preprocess data from various nodes and sensors. The allocation module is used to allocate resources under high-load power grid conditions. The communication and storage module is used to communicate the allocated data to the virtual power plant and store the relevant data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the high-load resource allocation method for grid-connected virtual power plants as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the high-load resource allocation method for grid-connected virtual power plants as described in any one of claims 1 to 5.

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