Mass resource access edge method and system suitable for gridding power grid

Abstract

The invention discloses a mass resource access edge method suitable for a gridding power grid, and relates to the technical field of power grids, and the method comprises the steps: dividing the gridding power grid into a plurality of hierarchical nested sub-grids, and configuring an edge calculation node for each sub-grid; acquiring an original resource data set in the sub-grids through edge computing nodes; analyzing the original resource data set by using a multi-factor weighted scoring algorithm to generate a second resource access priority list; obtaining a resource access scheme of each sub-grid according to the second resource access priority list and the real-time load state of the sub-grid; and constructing a distributed collaborative decision-making mechanism, and coordinating resource access schemes of adjacent levels of sub-grids to complete massive resource access. According to the method, the power grid is gridded and divided into the multi-level nested sub-grids, so that the resource access pressure of each region is effectively dispersed, and efficient access and management of resources are realized through optimized deployment of the edge computing nodes.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a method and system for accessing a massive amount of resources to an edge of a gridded power grid. Background Art

[0002] In recent years, as the scale of power grids continues to expand, the number of devices in the transmission and distribution network has also increased, and the requirements for production management systems have become increasingly stringent. The provincial transmission and distribution network needs to manage millions of devices, and these devices have complex electrical topological relationships, which makes management difficult and error-prone, making it difficult to effectively manage each device.

[0003] At present, the functional structure and technical characteristics of the power system are changing, and the operation and control of the power grid are facing new challenges. Edge computing, as one of the key technologies of the Internet of Things, can provide data services at the edge of the system and improve the efficiency of system operation. However, the existing power grid control system usually uploads power data to the cloud platform for processing, resulting in low processing efficiency. In order to improve data processing efficiency and reduce the dependence on uploading data to the cloud, efficient processing should be achieved at the source of data, thereby shortening the system response time, reducing data transmission and network occupancy, and reducing processing costs and energy consumption. Summary of the invention

[0004] In view of the complexity and low efficiency of existing power grid resource management, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to achieve efficient access and management of resources in the power grid and improve the system response speed and overall operating efficiency.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides an edge method for massive resource access applicable to a gridded power grid, which includes dividing the gridded power grid into multiple hierarchical nested sub-grids, each sub-grid being configured with an edge computing node; collecting the original resource data set in the sub-grid through the edge computing node; the original resource data set includes distributed power generation resource data, power load data and energy storage equipment data; using a multi-factor weighted scoring algorithm to analyze the original resource data set and generate a second resource access priority list; according to the second resource access priority list and the real-time load status of the sub-grid, obtaining a resource access plan for each sub-grid; constructing a distributed collaborative decision-making mechanism to coordinate the resource access plans of adjacent hierarchical sub-grids to complete massive resource access.

[0008] As a preferred solution of the method for accessing the edge of massive resources in a gridded power grid according to the present invention, the gridded power grid is divided into multiple hierarchical nested sub-grids, including the following steps: obtaining and preprocessing the geographic information data set and the load distribution data set of the target power grid to generate a preprocessed data set; based on the preprocessed data set, the target power grid is divided into an initial hierarchical sub-grid set using an adaptive grid partitioning algorithm; the load imbalance index of each sub-grid is calculated, and the sub-grid whose load imbalance index exceeds the first preset threshold is recursively subdivided to generate a multi-level nested sub-grid structure; each sub-grid is configured with an edge computing node, including the following steps: calculating the required number of edge computing nodes; determining the optimal location set of the edge computing nodes in each sub-grid; deploying the edge computing nodes in the optimal location set to complete the edge computing configuration of the sub-grid.

[0009] As a preferred solution of the method for accessing the edge of massive resources applicable to the gridded power grid described in the present invention, wherein: using an adaptive grid division algorithm to divide the target power grid into an initial hierarchical sub-grid set includes: digitizing a geographic information data set to generate a comprehensive geographic information data set; identifying high load density areas based on a load distribution data set; determining an initial grid division granularity based on the distribution characteristics of the high load density areas; applying a quadtree structure to divide the target power grid based on the initial grid division granularity to form a candidate sub-grid set; calculating a load distribution balance index for each candidate sub-grid; optimizing the geographic boundaries of each candidate sub-grid based on the load distribution balance index; generating an initial hierarchical sub-grid division scheme, the initial hierarchical sub-grid division scheme including the geographic boundaries and load characteristic data of each sub-grid.

[0010] As a preferred solution of the method for accessing the edge of massive resources applicable to the gridded power grid described in the present invention, wherein: generating a multi-level nested sub-grid structure includes the following steps: constructing a load imbalance index calculation model; using the load imbalance index calculation model, calculating the load imbalance index of each sub-grid; marking the sub-grid whose load imbalance index exceeds the first preset threshold as an object to be subdivided; for each object to be subdivided, executing a recursive subdivision process: applying an adaptive grid division algorithm to generate the next level of sub-grids, and calculating the load imbalance index of the newly generated sub-grid; if the load imbalance index of the newly generated sub-grid is lower than the first preset threshold, retaining the sub-grid; if the load imbalance index of the newly generated sub-grid exceeds the first preset threshold and does not reach the preset maximum number of levels, re-executing the recursive subdivision process; repeating the recursive subdivision process until the load imbalance indexes of all sub-grids are lower than the first preset threshold or reach the predetermined maximum number of levels; based on the recursive subdivision results, generating a multi-level nested sub-grid structure.

[0011] As a preferred solution of the method for accessing the edge of massive resources in a gridded power grid according to the present invention, generating a second resource access priority list includes constructing a multi-dimensional evaluation index system; adopting an adaptive weight allocation mechanism based on a combination of an entropy weight method and a hierarchical analysis method to dynamically adjust the weight of each evaluation index according to the characteristics of the sub-grid; calculating the resource evaluation score of the first resource data in the sub-grid; generating a first resource access priority list based on the resource evaluation score, combined with the resource type and the grid level; introducing a grid topology constraint factor, correcting the first resource access priority list, and obtaining a second resource access priority list; outputting and verifying the second resource access priority list.

[0012] As a preferred solution of the method for accessing the edge of a massive resource applicable to a gridded power grid according to the present invention, the adaptive weight allocation mechanism includes an adaptive fusion function, and the specific formula is as follows:

[0013] W i =α·W oi +(1-α)·W si

[0014] Among them, W i is the final weight of the i-th evaluation index, W oi is the objective weight of the i-th evaluation index calculated by the entropy weight method, W si is the subjective weight of the i-th evaluation index calculated by the hierarchical analysis method, α is the adaptive adjustment factor, and the specific formula is as follows:

[0015] α=β·(1-e -γ·D )

[0016] Among them, β is the scaling factor, γ is the sensitivity coefficient, and D is the sub-grid feature deviation.

[0017] As a preferred solution of the method for accessing the edge of a massive resource applicable to a gridded power grid described in the present invention, wherein: correcting the first resource access priority list includes calculating the electrical distance from each resource access point to a key load point to generate a distance matrix; designing a grid topology constraint factor based on the distance matrix; performing weighted calculation on the grid topology constraint factor and the comprehensive evaluation score in the first resource access priority list to obtain a corrected evaluation score; and performing a second sorting of the resources using a quick sorting algorithm based on the corrected evaluation score.

[0018] In the second aspect, an embodiment of the present invention provides a massive resource access edge system suitable for a gridded power grid, which includes a sub-grid division module for dividing the gridded power grid into multiple levels of nested sub-grids, each sub-grid being configured with an edge computing node; a data acquisition module for collecting the original resource data set in the sub-grid through the edge computing node; a list generation module for analyzing the original resource data set using a multi-factor weighted scoring algorithm to generate a second resource access priority list; a plan generation module for obtaining a resource access plan for each sub-grid based on the second resource access priority list and the real-time load status of the sub-grid; and a coordination module for building a distributed collaborative decision-making mechanism to coordinate the resource access plans of adjacent level sub-grids to complete massive resource access.

[0019] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the method for accessing the edge of massive resources applicable to a gridded power grid as described in the first aspect of the present invention are implemented.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the method for accessing the edge of massive resources applicable to a gridded power grid as described in the first aspect of the present invention are implemented.

[0021] The beneficial effects of the present invention are as follows: the present invention realizes the multi-level nested sub-grid structure of the power grid through adaptive grid division and recursive subdivision algorithm, effectively improving the degree of refinement of power grid resource management. The load-aware edge computing node optimization deployment is adopted to reduce data transmission delay, improve system response speed and resource utilization efficiency. The adaptive weight allocation mechanism and multi-factor weighted scoring algorithm are used to dynamically adjust the weights of each evaluation index to ensure the accuracy and rationality of resource access priority. Through the distributed collaborative decision-making mechanism and distributed consensus algorithm, the conflict problem of resource access in multi-level power grids is solved, the globally optimal resource access plan is realized, and the stability and economy of the overall operation of the power grid are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of the framework for the edge access method for massive resources applicable to gridded power grids.

[0024] Figure 2 A flow chart is designed for multiple levels of nested sub-grids of the massive resource access edge method applicable to gridded power grids.

[0025] Figure 3 A computer device diagram for a massive resource access edge approach applicable to a gridded power grid. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0028] 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0029] Example 1

[0030] Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, and provides a method for accessing the edge of a massive resource applicable to a gridded power grid, comprising:

[0031] S1: Divide the grid power grid into multiple levels of nested sub-grids, and configure edge computing nodes in each sub-grid.

[0032] Specifically, the following steps are included:

[0033] S1.1: Acquire and preprocess the geographic information dataset and load distribution dataset of the target power grid to generate a preprocessed dataset.

[0034] Among them, the geographic information data set includes but is not limited to factors such as road layout, functional area division, and population flow characteristics, and the load distribution data set includes historical load data and real-time load data.

[0035] S1.2: Based on the preprocessed data set, the target power grid is divided into an initial level subgrid set using an adaptive grid partitioning algorithm.

[0036] Specifically, the preprocessed geographic information data set is digitized to generate a comprehensive geographic information data set; based on the load distribution data set, a density clustering algorithm is used to identify high load density areas; according to the distribution characteristics of the high load density areas, the initial grid division granularity is determined; a quadtree structure is applied to divide the target power grid based on the initial grid division granularity to form a set of candidate subgrids; the load distribution balance index of each candidate subgrid is calculated; a boundary optimization algorithm is used to optimize the boundary of each candidate subgrid according to the load distribution balance index; and an initial hierarchical subgrid division scheme is generated, including the geographic boundary and load characteristic data of each subgrid.

[0037] S1.3: Calculate the load imbalance index of each sub-grid, perform recursive subdivision on the sub-grid whose load imbalance index exceeds a first preset threshold, and generate a multi-level nested sub-grid structure.

[0038] Specifically, a load imbalance index calculation model is constructed; the load imbalance index of each sub-grid is calculated using the load imbalance index calculation model; the sub-grid whose load imbalance index exceeds a first preset threshold is marked as an object to be subdivided; for each object to be subdivided, a recursive subdivision process is performed: an adaptive grid division algorithm is applied to generate the next level of sub-grids, and the load imbalance index of the newly generated sub-grid is calculated. If the load imbalance index of the newly generated sub-grid is lower than the first preset threshold, the sub-grid is retained; if its load imbalance index exceeds the first preset threshold and does not reach the preset maximum number of levels, the recursive subdivision process is re-executed on the sub-grid; the recursive subdivision process is repeated until the load imbalance indexes of all sub-grids are lower than the first preset threshold or reach the preset maximum number of levels; based on the recursive subdivision results, a final multi-level nested sub-grid structure is generated.

[0039] It should be noted that the load imbalance index calculation model comprehensively considers the spatial differences and temporal fluctuations of load distribution. The specific formula is as follows:

[0040] LBI=λ1·SD+λ2·TV

[0041] in,

[0042]

[0043] Among them, LBI load imbalance index, λ1 and λ2 are weight coefficients, SD is the spatial unevenness of load distribution in the subgrid, TV is the temporal variation of load in the subgrid, L k is the load value of the kth node in the subgrid, is the average load of all nodes in the subgrid, N is the total number of nodes in the subgrid, is the average load of the subgrid at time point t, are the maximum, minimum and average values ​​of the subgrid average load respectively.

[0044] Preferably, the present invention innovatively and comprehensively considers the spatial differences and temporal fluctuations of load distribution by introducing a load imbalance index calculation model, ensuring that the sub-grid division of the power grid is more accurate and dynamically adaptable. The sub-grid structure is optimized by recursive subdivision, which effectively reduces the management complexity and load imbalance risk of high-load areas, ensuring the stability and efficiency of the system under different load conditions. Compared with the traditional static grid division method, the present invention can better adapt to the dynamic changes of the power grid load, and improve the dispatch flexibility and overall operation performance of the power grid resources.

[0045] S1.4: Calculate the number of edge computing nodes required based on the area and resource density of each sub-grid.

[0046] S1.5: Use a load-aware adaptive deployment algorithm to determine the optimal location set of edge computing nodes within each sub-grid.

[0047] Specifically, a sub-grid load distribution model is constructed, which takes into account load density, load fluidity and load time-varying characteristics; based on the sub-grid load distribution model, a set of candidate edge computing node locations is generated; the candidate edge computing node location set is initially scored, and the scoring indicators include coverage, load balancing and network delay; the particle swarm optimization algorithm is applied to iteratively optimize the edge computing node locations based on the initial scoring; according to the optimization results, the position combination with the highest score is selected as the optimal location set of the edge computing nodes.

[0048] Preferably, the present invention uses a particle swarm optimization algorithm to iteratively optimize the edge computing node location. This method can efficiently search a large-scale solution space while taking load perception into consideration to find a better node deployment solution. Compared with traditional fixed-position deployment or simple greedy algorithms, this solution can better adapt to complex load distribution situations and improve resource utilization efficiency and system performance.

[0049] S1.6: Deploy edge computing nodes at the optimal location set to complete the edge computing configuration of the sub-grid.

[0050] S1.7: Initialize configuration and performance testing of the deployed edge computing nodes and generate performance reports.

[0051] S2: Collect the original resource data set within the sub-grid through the edge computing node.

[0052] It should be noted that the original resource data set includes distributed generation resource data, power load data and energy storage equipment data.

[0053] S3: Analyze the original resource data set using a multi-factor weighted scoring algorithm to generate a second resource access priority list.

[0054] Specifically, the following steps are included:

[0055] S3.1: Preprocess the original resource dataset.

[0056] Specifically, preprocessing includes data normalization, outlier detection, and data completion.

[0057] S3.2: Construct a multi-dimensional evaluation index system.

[0058] Specifically, the evaluation indicators in the multidimensional evaluation index system include power generation stability, electricity demand urgency, energy storage capacity utilization, environmental friendliness, etc.

[0059] S3.3: Adopt an adaptive weight allocation mechanism based on the combination of entropy weight method and hierarchical analysis method to dynamically adjust the weight of each evaluation index according to the characteristics of the sub-grid.

[0060] Preferably, the objective weight of each evaluation index is calculated using the entropy weight method; a judgment matrix is ​​constructed based on the hierarchical analysis method to calculate the subjective weight of each evaluation index; an adaptive fusion function is designed to dynamically fuse the objective weight and the subjective weight. The specific formula is as follows:

[0061] W i =α·W oi +(1-α)·W si

[0062] Among them, W i is the final weight of the i-th evaluation index, W oi is the objective weight of the i-th evaluation index calculated by the entropy weight method, W si is the subjective weight of the i-th evaluation index calculated by the hierarchical analysis method, α is the adaptive adjustment factor, and the specific formula is as follows:

[0063] α=β·(1-e -γ·D )

[0064] Among them, β is the scaling factor (used to control the maximum value of α), γ is the sensitivity coefficient (used to adjust the response speed of α to D), and D is the sub-grid feature deviation. The specific formula is as follows:

[0065]

[0066] Among them, F i is the actual value of the i-th evaluation index of the current sub-grid, F sta,i is the expected value of the i-th evaluation index of the current sub-grid, and n is the number of evaluation indicators.

[0067] Furthermore, according to the load characteristics and resource distribution of the sub-grid, the parameters of the adaptive fusion function are adjusted; the final weights of each evaluation index are calculated using the adaptive fusion function; the final weights are normalized to form an evaluation index weight vector suitable for the current sub-grid.

[0068] S3.4: Calculate the resource evaluation score of the first resource data in the subgrid using the fuzzy comprehensive evaluation method.

[0069] S3.5: Generate a first resource access priority list based on the resource evaluation score, combined with the resource type and grid level.

[0070] Specifically, a resource type characteristic matrix is ​​constructed to quantify the influence of different resource types on each evaluation index; the resource evaluation score is multiplied by the resource type characteristic matrix to obtain a type weighted score; a grid level influence function is established to calculate the importance coefficient of each subgrid in the multi-level structure; the type weighted score is adjusted using the grid level influence function to obtain a comprehensive evaluation score; according to the comprehensive evaluation score, the quick sorting algorithm is used to preliminarily sort the resources; if there are resources with a comprehensive evaluation score, secondary sorting indicators (such as resource capacity, response speed, etc.) are introduced for subdivision and sorting; a first resource access priority list is generated, including resource identification, comprehensive evaluation score and preliminary resource access order.

[0071] S3.6: Introduce a grid topology constraint factor, modify the first resource access priority list, and obtain a second resource access priority list.

[0072] Specifically, the electrical distance from each resource access point to the key load point is calculated to generate a distance matrix; the grid topology constraint factor is designed based on the distance matrix to quantify the impact of the topological structure on resource access; the grid topology constraint factor and the comprehensive evaluation score in the first resource access priority list are weighted to obtain a revised evaluation score; according to the revised evaluation score, the resources are sorted secondary using a quick sorting algorithm; if there are resources with the same revised evaluation score, their relative order in the first resource access priority list is maintained; a second resource access priority list is generated, including resource identifiers, revised evaluation scores, and final resource access order.

[0073] S3.7: Output and verify the second resource access priority list and evaluate its impact on grid stability and efficiency.

[0074] S4: Obtain a resource access plan for each sub-grid according to the second resource access priority list and the real-time load status of the sub-grid.

[0075] Specifically, the edge computing node obtains the second resource access priority list and the real-time load status data of the sub-grid; establishes a dynamic time window model, and determines the resource access time window based on the real-time load status data of the sub-grid and the resource access characteristics; constructs a multi-objective optimization function, which includes load balance, grid stability and economic indicators; adopts a particle swarm optimization algorithm, with the multi-objective optimization function as the goal, to calculate the resource access sequence and the initial power allocation plan within each time window; according to the real-time load status data of the sub-grid, the initial power allocation plan is optimized through an adaptive adjustment mechanism to generate an optimal power allocation plan; the optimal power allocation plan is piecewise linearized to generate a discrete resource access instruction sequence; the edge computing node generates a resource access plan for each sub-grid according to the discrete resource access instruction sequence, wherein the resource access plan includes resource access timing and power allocation.

[0076] S5: Build a distributed collaborative decision-making mechanism to coordinate the resource access plans of adjacent-level subgrids and complete the access of massive resources.

[0077] Specifically, a hierarchical communication network is established to connect the edge computing nodes of sub-grids at different levels; the resource access plan and load status information of each sub-grid are obtained through the hierarchical communication network; a multi-level resource access coordination model is constructed based on the resource access plan and load status information; the multi-level resource access coordination model is used to identify resource access conflicts between adjacent sub-grids; dynamic negotiation rules based on game theory are designed for resource access conflicts; a distributed consensus algorithm is applied to coordinate the resource access plans of each sub-grid on the basis of following the dynamic negotiation rules; a globally optimal massive resource access plan is generated through distributed iterative optimization, and the massive resource access plan is sent to each sub-grid through the hierarchical communication network.

[0078] Preferably, the present invention combines a distributed consensus algorithm with a multi-level coordination model, and effectively handles complex resource access issues in large-scale, multi-level power grids through adaptive conflict detection and dynamic negotiation rules.

[0079] Furthermore, the present embodiment also provides a massive resource access edge system suitable for a gridded power grid, including a sub-grid division module, which is used to divide the gridded power grid into multiple levels of nested sub-grids, each sub-grid being configured with an edge computing node; a data acquisition module, which is used to collect the original resource data set in the sub-grid through the edge computing node; a list generation module, which is used to analyze the original resource data set using a multi-factor weighted scoring algorithm to generate a second resource access priority list; a plan generation module, which is used to obtain a resource access plan for each sub-grid based on the second resource access priority list and the real-time load status of the sub-grid; and a coordination module, which is used to build a distributed collaborative decision-making mechanism, coordinate the resource access plans of adjacent level sub-grids, and complete massive resource access.

[0080] This embodiment also provides a computer device, which is suitable for the case of a method for accessing the edge of massive resources in a gridded power grid, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method for accessing the edge of massive resources in a gridded power grid as proposed in the above embodiment.

[0081] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. 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, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0082] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing edge access of massive resources applicable to a gridded power grid as proposed in the above embodiment is implemented.

[0083] In summary, the present invention realizes the multi-level nested sub-grid structure of the power grid through adaptive grid division and recursive subdivision algorithm, which effectively improves the refinement of power grid resource management. The load-aware edge computing node optimization deployment is adopted to reduce data transmission delay, improve system response speed and resource utilization efficiency. The adaptive weight allocation mechanism and multi-factor weighted scoring algorithm are used to dynamically adjust the weights of each evaluation index to ensure the accuracy and rationality of resource access priority. Through distributed collaborative decision-making mechanism and distributed consensus algorithm, the conflict problem of resource access in multi-level power grids is solved, the global optimal resource access plan is realized, and the stability and economy of the overall operation of the power grid are improved.

[0084] Example 2

[0085] Reference Figure 1 to Figure 3 , which is the second embodiment of the present invention, and this embodiment provides a method for accessing the edge of massive resources suitable for a grid-based power grid. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0086] In a simulation experiment conducted on a provincial power grid, an area covering about 120,000 square kilometers and including 5 prefecture-level cities was selected as the target power grid. The distributed energy penetration rate of this power grid reached 30%, and the peak power load was about 20GW. The experiment used real geographic information data and one year of historical load data. The experiment set the initial grid division granularity to 5kmx5km, the maximum number of recursive levels to 4, and the load imbalance index threshold to 0.3. The weight coefficients α and β were set to 0.6 and 0.4 respectively, the population size of the particle swarm optimization algorithm was 100, and the maximum number of iterations was 500. In the multi-objective optimization function, the weights of load balance, power grid stability, and economy were 0.4, 0.4, and 0.2, respectively.

[0087] The experiment first cleaned and standardized the load data for one year, generated a load time series with a 15-minute granularity, and digitized the geographic information data to form a comprehensive geographic information data set containing elements such as roads and functional areas. Based on these pre-processed data, the adaptive grid division algorithm was applied to complete the initial hierarchical division and obtain 478 sub-grids. After recursive subdivision, a 4-layer nested structure containing 1,253 sub-grids was finally formed. According to the characteristics of the sub-grids, it was calculated that 1,876 edge computing nodes needed to be deployed. The optimal location was determined by the load-aware adaptive deployment algorithm, and an average of 1.5 edge nodes were configured for each sub-grid.

[0088] In the resource evaluation and priority sorting stage, 2,345,678 pieces of original resource data were scored with multi-factor weighting to generate a preliminary resource access priority list. After the grid topology constraint factor was introduced, 23.7% of the resource priorities were adjusted, forming the final resource access priority list. Based on the real-time load status and priority list, the resource access plan for each subgrid was generated by the particle swarm optimization algorithm. During the peak load period, an average of 387.5 distributed resources were accessed every 15 minutes, with a total access power of 2.73GW.

[0089] In order to coordinate resource access, a 4-layer communication network was built, and 267 cross-level resource access conflicts were identified. A distributed consensus algorithm was applied for coordination, and after an average of 6.3 iterations, the convergence of the global resource access solution was achieved. The experimental results show that compared with the traditional fixed-granularity partitioning, the adaptive multi-level partitioning reduces the average load imbalance index in the high-load area from 0.42 to 0.28, an increase of 33.3%. The average load utilization of the edge nodes after optimized deployment reached 78.6%, an increase of 22.4% over the random deployment, and the average network delay was reduced by 31.7%, as shown in Table 1.

[0090] Table 1 Comparison index table of the present invention and the traditional method

[0091] Comparison indicators The present invention Traditional methods Average load imbalance index in high-load areas 0.28 0.42 Average load utilization of edge nodes 78.6％ 64.2％ Average network delay (ms) 45 65.8 Average response time for resource access (s) 2.3 2.8 Grid Stability Index 0.92 0.82 Load difference between adjacent subgrids 15％ 21％ Load balancing of the entire network 0.87 0.73

[0092] Table 1 clearly shows the significant advantages of the solution of the present invention over the existing technical solutions in multiple key performance indicators. From load balancing, resource utilization to network performance, the solution of the present invention has achieved significant improvements. In particular, in terms of load balancing in high-load areas, edge node utilization, network latency, and decision-making efficiency, the improvement is more than 10%. These data fully demonstrate the excellent performance of the present invention in the access management of massive distributed resources in large-scale power grids, which not only improves the flexibility and stability of the system, but also brings considerable economic benefits.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for accessing the edge of a massive resource suitable for a grid-based power grid, characterized by: include, Divide the grid power grid into multiple levels of nested sub-grids, and configure edge computing nodes in each sub-grid; Collecting the original resource data set in the subgrid through the edge computing node; the original resource data set includes distributed power generation resource data, power load data and energy storage equipment data; Analyzing the original resource data set using a multi-factor weighted scoring algorithm to generate a second resource access priority list; Obtaining a resource access plan for each subgrid according to the second resource access priority list and the real-time load status of the subgrid; Build a distributed collaborative decision-making mechanism to coordinate the resource access plans of adjacent-level sub-grids and complete the access of massive resources.

2. The method for accessing the edge of a massive resource applicable to a grid-based power grid as claimed in claim 1, characterized in that: The step of dividing the gridded power grid into multiple levels of nested subgrids comprises the following steps: Acquire and preprocess the geographic information data set and load distribution data set of the target power grid to generate a preprocessed data set; Based on the preprocessed data set, the target power grid is divided into an initial level subgrid set using an adaptive grid partitioning algorithm; Calculating a load imbalance index of each subgrid, performing recursive subdivision on the subgrid whose load imbalance index exceeds a first preset threshold, and generating a multi-level nested subgrid structure; Each sub-grid configuration edge computing node includes the following steps: Calculate the number of edge computing nodes required; Determine the optimal set of locations for edge computing nodes within each subgrid; The edge computing nodes are deployed at the optimal location set to complete the edge computing configuration of the sub-grid.

3. The method for accessing the edge of a massive resource suitable for a grid-based power grid as claimed in claim 2, characterized in that: The method of dividing the target power grid into an initial level subgrid set by using an adaptive grid division algorithm includes: Digitally process geographic information datasets to generate comprehensive geographic information datasets; Based on the load distribution dataset, identify high load density areas; Determining an initial grid division granularity according to the distribution characteristics of the high load density area; Applying a quadtree structure, dividing the target power grid based on the initial grid division granularity to form a candidate subgrid set; Calculate the load distribution balance index of each candidate subgrid; Optimizing the geographic boundary of each candidate subgrid according to the load distribution balance index; An initial hierarchical subgrid partitioning scheme is generated, wherein the initial hierarchical subgrid partitioning scheme includes geographic boundaries and load characteristic data of each subgrid.

4. The method for accessing the edge of a massive resource suitable for a grid-based power grid as claimed in claim 2, characterized in that: The generating of the multi-level nested sub-grid structure comprises the following steps: Construct a load imbalance index calculation model; Calculating the load imbalance index of each subgrid using the load imbalance index calculation model; Marking the subgrid whose load imbalance index exceeds a first preset threshold as an object to be subdivided; For each object to be subdivided, perform the recursive subdivision process: Apply the adaptive meshing algorithm to generate the next level of sub-grids and calculate the load imbalance index of the newly generated sub-grids; If the load imbalance index of the newly generated subgrid is lower than the first preset threshold, the subgrid is retained; If the load imbalance index of the newly generated subgrid exceeds the first preset threshold value and does not reach the preset maximum number of levels, the recursive subdivision process is re-executed; Repeating the recursive subdivision process until the load imbalance index of all subgrids is lower than a first preset threshold or reaches a predetermined maximum number of levels; Based on the recursive subdivision results, a multi-level nested sub-grid structure is generated.

5. The method for accessing the edge of a massive resource applicable to a grid-based power grid as claimed in claim 1, characterized in that: Generating the second resource access priority list includes: Construct a multi-dimensional evaluation index system; An adaptive weight allocation mechanism based on the combination of entropy weight method and hierarchical analysis method is used to dynamically adjust the weight of each evaluation index according to the characteristics of the sub-grid; Calculate a resource evaluation score of the first resource data in the subgrid; Based on the resource evaluation score, combined with resource type and grid level, generate a first resource access priority list; Introducing a grid topology constraint factor, modifying the first resource access priority list, and obtaining a second resource access priority list; The second resource access priority list is output and verified.

6. The method for accessing the edge of a massive resource applicable to a grid-based power grid as claimed in claim 5, characterized in that: The adaptive weight allocation mechanism includes an adaptive fusion function, and the specific formula is as follows: W i =α·W oi +(1-a)·W si Among them, W i is the final weight of the i-th evaluation index, W oi is the objective weight of the i-th evaluation index calculated by the entropy weight method, W si is the subjective weight of the i-th evaluation index calculated by the hierarchical analysis method, α is the adaptive adjustment factor, and the specific formula is as follows: α＝β·(1-e -γ·D ) Among them, β is the scaling factor, γ is the sensitivity coefficient, and D is the sub-grid feature deviation.

7. The method for accessing the edge of a massive resource applicable to a grid-based power grid as claimed in claim 5, characterized in that: The modifying of the first resource access priority list comprises: Calculate the electrical distance from each resource access point to the key load point and generate a distance matrix; Designing a grid topology constraint factor based on the distance matrix; Performing weighted calculation on the grid topology constraint factor and the comprehensive evaluation score in the first resource access priority list to obtain a modified evaluation score; According to the modified evaluation scores, the resources are re-sorted using a quick sorting algorithm.

8. A massive resource access edge system applicable to a gridded power grid, based on a massive resource access edge method applicable to a gridded power grid according to any one of claims 1 to 7, characterized in that: Also includes, The subgrid partitioning module is used to divide the gridded power grid into multiple levels of nested subgrids, each of which is configured with an edge computing node; A data collection module, used to collect the original resource data set in the sub-grid through the edge computing node; A list generation module, used to analyze the original resource data set using a multi-factor weighted scoring algorithm to generate a second resource access priority list; A solution generation module, used to obtain a resource access solution for each sub-grid according to the second resource access priority list and the real-time load status of the sub-grid; The coordination module is used to build a distributed collaborative decision-making mechanism, coordinate the resource access plans of adjacent-level sub-grids, and complete the access of massive resources.

9. 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 method for accessing the edge of a massive resource applicable to a gridded power grid as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for accessing the edge of a massive resource applicable to a gridded power grid as described in any one of claims 1 to 7 are implemented.

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