Distributed photovoltaic operation energy efficiency analysis method and system based on edge computing

Abstract

The application discloses a distributed photovoltaic operation energy efficiency analysis method and system based on edge computing, and relates to the field of photovoltaic power stations.The distributed photovoltaic operation energy efficiency analysis method based on edge computing comprises the following steps: connecting a distributed photovoltaic node as an edge node with a corresponding energy storage node to construct a plurality of energy storage-photovoltaic edge networks; performing energy efficiency analysis, acquiring the interaction energy efficiency indexes of each photovoltaic node and the corresponding energy storage node through edge computing, including the charging and discharging energy conversion efficiency, the scheduling instruction response speed and the energy transmission line loss; calculating a plurality of interaction energy efficiency consistency indexes corresponding to the plurality of energy storage-photovoltaic edge networks; acquiring a marked energy storage-photovoltaic edge network; and performing edge node reconstruction to acquire a plurality of energy storage-photovoltaic edge reconstruction networks.The technical problems of inaccuracy and untimely response existing in the prior art distributed photovoltaic operation energy efficiency analysis are solved, the accuracy and timeliness of analysis are improved, and the technical effect of improving energy efficiency consistency is achieved.

Description

A Method and System for Distributed Photovoltaic Energy Efficiency Analysis Based on Edge Computing Technical Field

[0001] This application relates to the field of photovoltaic power plants, and in particular to a method and system for analyzing the energy efficiency of distributed photovoltaic operation based on edge computing. Background Technology

[0002] The efficient operation of distributed photovoltaic (PV) systems is crucial for sustainable energy utilization and stable power grid operation. Currently, centralized data acquisition and analysis methods are mainly used for energy efficiency analysis of distributed PV systems. This method involves setting up a unified monitoring center in the distributed PV system to collect operational data from various PV nodes and energy storage devices, and then using a centralized algorithm model to evaluate and analyze the overall energy efficiency. However, due to the widespread distribution and numerous nodes in distributed PV systems, the centralized method requires significant communication resources to ensure data real-time performance and integrity during data transmission. This not only increases communication costs but also easily leads to data transmission delays and losses, thus affecting the accuracy and timeliness of energy efficiency analysis. Furthermore, the centralized processing method places extremely high demands on the computing power of the monitoring center. In large-scale distributed PV system scenarios, computational bottlenecks can easily occur, making it difficult to achieve efficient management and optimization of numerous edge nodes.

[0003] Currently, distributed photovoltaic (PV) operation energy efficiency analysis suffers from technical problems such as inaccuracy and untimely response. Summary of the Invention

[0004] This application provides a method and system for energy efficiency analysis of distributed photovoltaic (PV) operation based on edge computing. It employs techniques such as acquiring distributed PV nodes, connecting them as edge nodes to corresponding energy storage nodes to construct multiple energy storage-PV edge networks, performing energy efficiency analysis on these networks, using edge computing to obtain the interaction energy efficiency index between PV nodes and energy storage nodes in each network, calculating the interaction energy efficiency consistency index for each energy storage-PV edge network based on the interaction energy efficiency index, marking specific energy storage-PV edge networks based on the interaction energy efficiency consistency index, reconstructing the edge nodes of the marked energy storage-PV edge networks, and finally obtaining multiple reconstructed energy storage-PV edge networks. These techniques solve the technical problems of inaccuracy and untimely response in existing distributed PV operation energy efficiency analysis, achieving the technical effect of improving the accuracy and timeliness of operation energy efficiency analysis, thereby enhancing the energy efficiency consistency of the entire system.

[0005] This application provides a method for energy efficiency analysis of distributed photovoltaic (PV) operation based on edge computing, comprising: acquiring distributed PV nodes; connecting the distributed PV nodes as edge nodes with corresponding energy storage nodes to construct multiple energy storage-PV edge networks; performing energy efficiency analysis on the multiple energy storage-PV edge networks, obtaining the interaction energy efficiency index between each PV node and its corresponding energy storage node in each energy storage-PV network through edge computing, wherein the interaction energy efficiency index includes charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss; calculating multiple interaction energy efficiency consistency indices corresponding to the multiple energy storage-PV edge networks based on the interaction energy efficiency index between each PV node and its corresponding energy storage node; acquiring marked energy storage-PV edge networks according to the multiple interaction energy efficiency consistency indices; and reconstructing the edge nodes of the marked energy storage-PV edge networks to obtain multiple energy storage-PV edge reconstruction networks.

[0006] In a possible implementation, the following process is performed: according to the plurality of interactive energy efficiency consistency indicators, a labeled energy storage-photovoltaic edge network is obtained, wherein the labeled energy storage-photovoltaic edge network is an energy storage-photovoltaic edge network among the plurality of energy storage-photovoltaic edge networks whose interactive energy efficiency consistency indicators are less than a preset interactive energy efficiency consistency indicator threshold; wherein the preset interactive energy efficiency consistency indicator threshold is configured by analyzing the return success distribution probability of parallel energy storage call request samples.

[0007] In a possible implementation, the preset interactive energy efficiency consistency index threshold is configured by analyzing the return success distribution probability of parallel energy storage call request samples. The method includes: analyzing the parallel energy storage call request samples of each of the multiple energy storage-PV edge networks, wherein the parallel energy storage call request samples are energy storage call request instructions sent simultaneously by all PV nodes in the energy storage-PV edge network to the energy storage nodes; predicting the request return result of the corresponding energy storage node according to the parallel energy storage call request samples, wherein the request return result includes the energy storage call return success distribution probability under each number of parallel energy storage call requests; obtaining multiple request return results corresponding to the multiple energy storage-PV edge networks; and configuring the preset interactive energy efficiency consistency index threshold using the multiple return success distribution probabilities corresponding to the multiple request return results.

[0008] In a possible implementation, a preset interactive energy efficiency consistency index threshold is configured using the multiple success distribution probabilities corresponding to the multiple request return results. The method includes: analyzing the peak number of parallel energy storage call requests of the parallel energy storage call request sample; obtaining the average probability of successful energy storage call return under the peak number of parallel energy storage call requests; mapping the average probability of successful energy storage call return to the interactive energy efficiency consistency index; and configuring the preset interactive energy efficiency consistency index threshold.

[0009] In a possible implementation, the following process is performed: using a mapping relationship to map the average probability of successful return of the energy storage call to an interactive energy efficiency consistency index, wherein the mapping relationship is a mapping between the probability of successful return of the energy storage call and the interactive energy efficiency consistency index.

[0010] In a possible implementation, the distributed photovoltaic (PV) nodes are connected as edge nodes to corresponding energy storage nodes to construct multiple energy storage-PV edge networks. The method includes: acquiring multiple energy storage nodes; collecting the distribution locations of the distributed PV nodes and the multiple energy storage nodes; performing spatial distance clustering based on the distribution locations of the distributed PV nodes and the multiple energy storage nodes, using the multiple energy storage nodes as clustering nodes to obtain a first node clustering result; collecting historical energy storage demand indicators of the distributed PV nodes; updating the first node clustering result with total demand constraints based on the historical energy storage demand indicators to obtain a second node clustering result; and constructing multiple energy storage-PV edge networks based on the connection relationships between edge nodes and corresponding energy storage nodes in the second node clustering result.

[0011] In a possible implementation, the following processing is performed: Based on the interaction energy efficiency index between each photovoltaic node and its corresponding energy storage node, multiple interaction energy efficiency consistency indices are calculated for each of the multiple energy storage-photovoltaic edge networks. Calculating the interaction energy efficiency consistency index for an energy storage-photovoltaic edge network includes: performing scale interval mapping on the charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss to obtain a normalized interaction energy efficiency index between each photovoltaic node and its corresponding energy storage node; performing weight calculation on the normalized interaction energy efficiency index to obtain N node interaction energy efficiency indices between each photovoltaic node and its corresponding energy storage node, where N is the number of photovoltaic nodes; and calculating the standard deviation of the N node interaction energy efficiency indices to output the interaction energy efficiency consistency index.

[0012] In a possible implementation, the edge node reconstruction of the labeled energy storage-photovoltaic edge network includes: identifying abnormal photovoltaic nodes in the labeled energy storage-photovoltaic edge network; determining candidate energy storage nodes based on the distribution locations of the abnormal photovoltaic nodes and multiple energy storage nodes; and performing similarity reconstruction on the candidate energy storage nodes based on the interaction energy efficiency index of the abnormal photovoltaic nodes to obtain the reconstructed energy storage-photovoltaic edge network corresponding to the labeled energy storage-photovoltaic edge network.

[0013] In a possible implementation, the following process is performed: after obtaining the reconstructed energy storage-photovoltaic edge network corresponding to the marked energy storage-photovoltaic edge network, it is determined whether the interaction energy efficiency consistency index of the reconstructed energy storage-photovoltaic edge network is less than the preset interaction energy efficiency consistency index threshold; if the interaction energy efficiency consistency index of the reconstructed energy storage-photovoltaic edge network is less than the preset interaction energy efficiency consistency index threshold, the multiple energy storage-photovoltaic edge networks are globally reconstructed based on the preset interaction energy efficiency consistency index threshold to obtain multiple energy storage-photovoltaic edge reconstructed networks.

[0014] This application also provides a distributed photovoltaic (PV) operation energy efficiency analysis system based on edge computing, comprising: an energy storage-PV edge network construction module, used to acquire distributed PV nodes, connect the distributed PV nodes as edge nodes with corresponding energy storage nodes, and construct multiple energy storage-PV edge networks; an interaction energy efficiency index calculation module, used to perform energy efficiency analysis on the multiple energy storage-PV edge networks, and obtain the interaction energy efficiency index between each PV node and the corresponding energy storage node in each energy storage-PV network through edge computing, wherein the interaction energy efficiency index includes charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss; an interaction energy efficiency consistency index calculation module, used to calculate multiple interaction energy efficiency consistency indices corresponding to the multiple energy storage-PV edge networks based on the interaction energy efficiency index between each PV node and the corresponding energy storage node; an energy storage-PV edge network marking module, used to obtain marked energy storage-PV edge networks according to the multiple interaction energy efficiency consistency indices; and an edge node reconstruction module, used to reconstruct the edge nodes of the marked energy storage-PV edge networks to obtain multiple reconstructed energy storage-PV edge networks.

[0015] The proposed method and system for distributed photovoltaic (PV) operation energy efficiency analysis based on edge computing, as described in this application, firstly acquires distributed PV nodes, connects these nodes as edge nodes to their corresponding energy storage nodes, and constructs multiple energy storage-PV edge networks. Then, it performs energy efficiency analysis on these multiple energy storage-PV edge networks, obtaining the interaction energy efficiency indicators between each PV node and its corresponding energy storage node in each energy storage-PV network through edge computing. These interaction energy efficiency indicators include charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss. Based on the interaction energy efficiency indicators between each PV node and its corresponding energy storage node, it calculates multiple interaction energy efficiency consistency indicators for each of the multiple energy storage-PV edge networks. Next, it acquires marked energy storage-PV edge networks according to these multiple interaction energy efficiency consistency indicators. Finally, it reconstructs the edge nodes of the marked energy storage-PV edge networks to obtain multiple reconstructed energy storage-PV edge networks. This achieves the technical effect of improving the accuracy and timeliness of operational energy efficiency analysis, thereby enhancing the energy efficiency consistency of the entire system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 is a flowchart illustrating the distributed photovoltaic operation energy efficiency analysis method based on edge computing provided in an embodiment of this application.

[0018] Figure 2 is a schematic diagram of the structure of the distributed photovoltaic operation energy efficiency analysis system based on edge computing provided in the embodiment of this application.

[0019] Figure labeling: Energy storage-photovoltaic edge network construction module 10, interactive energy efficiency index calculation module 20, interactive energy efficiency consistency index calculation module 30, energy storage-photovoltaic edge network labeling module 40, edge node reconfiguration module 50. Detailed Implementation

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides a method for analyzing the energy efficiency of distributed photovoltaic systems based on edge computing, as shown in Figure 1. The method includes:

[0024] Step S100: Obtain distributed photovoltaic nodes, connect the distributed photovoltaic nodes as edge nodes with the corresponding energy storage nodes, and construct multiple energy storage-photovoltaic edge networks.

[0025] Specifically, distributed photovoltaic (PV) nodes refer to the specific equipment units in a distributed PV power generation system that convert solar energy into electrical energy. These are the locations of PV panel arrays and their associated inverters and other equipment, distributed across different geographical locations. Energy storage nodes are the locations of equipment or systems used to store electrical energy, such as lithium-ion battery energy storage systems and lead-acid battery energy storage systems. Their function is to store electrical energy when there is a surplus in PV power generation and release it when there is a shortage, thus balancing power supply and demand.

[0026] In this application, distributed photovoltaic (PV) nodes are treated as edge nodes, located at the edge of the network, close to the data source, enabling preliminary data processing and transmission. Specifically, the data acquisition module of the distributed PV monitoring system collects real-time operational data from the PV nodes, such as power generation, voltage, and current, to determine their location and status, thus acquiring information about these nodes. The data acquisition module is built upon a sensor network, for example, by installing voltage, current, and power sensors on each distributed PV node. For energy storage nodes, their location and status information are obtained through the energy storage system's monitoring platform. Wireless or wired communication technologies are used to connect the distributed PV nodes to their corresponding energy storage nodes. During the connection process, the correspondence between PV nodes and energy storage nodes is determined based on factors such as geographical location and power matching, constructing multiple independent energy storage-PV edge networks.

[0027] In one possible implementation, the distributed photovoltaic nodes are connected as edge nodes to corresponding energy storage nodes to construct multiple energy storage-photovoltaic edge networks. Step S100 further includes step S110, acquiring multiple energy storage nodes and collecting the distribution locations of the distributed photovoltaic nodes and the multiple energy storage nodes. Specifically, the location and number of energy storage nodes are determined through an energy storage system management platform or on-site surveys. The energy storage system management platform records relevant information about deployed energy storage devices, including installation location and device model. On-site surveys involve physically inspecting and recording information about newly added or un-entered energy storage nodes. GPS positioning modules are installed on each distributed photovoltaic node and energy storage node using Global Positioning System (GPS) technology. These modules can acquire the latitude and longitude coordinates of the nodes in real time and transmit the data to the central data processing system via wireless communication.

[0028] Step S120: Based on the distribution locations of the distributed photovoltaic nodes and the multiple energy storage nodes, spatial distance clustering is performed using the multiple energy storage nodes as clustering nodes to obtain the first node clustering result. Specifically, spatial distance clustering is a method of classifying data objects based on their spatial distance, grouping objects that are close together into the same category and objects that are far apart into different categories. A distance-based clustering algorithm, such as the K-means clustering algorithm, is used. The energy storage nodes are used as cluster centers, and the K value is equal to the number of energy storage nodes. The Euclidean distance formula is used to calculate the spatial distance between each distributed photovoltaic node and each energy storage node, and each distributed photovoltaic node is assigned to the cluster represented by the nearest energy storage node. This process is repeated until the clustering result converges, that is, the node assignment no longer changes, and the first node clustering result is obtained. Each group in the first node clustering result contains one energy storage node and several distributed photovoltaic nodes that are close to that energy storage node.

[0029] Step S130: Collect historical energy storage demand indicators of the distributed photovoltaic nodes, and update the total demand constraint of the first node clustering results according to the historical energy storage demand indicators to obtain the second node clustering results. Specifically, through the monitoring platform of the distributed photovoltaic system, collect the power generation data, electricity load data, and energy storage system charging and discharging data of each distributed photovoltaic node over a period of time, such as one year. Based on these data, calculate the historical energy storage demand indicators of each node, such as average daily energy storage demand and maximum energy storage demand. For each cluster in the first node clustering results, calculate the total historical energy storage demand of all distributed photovoltaic nodes in that cluster. Adjust the node allocation within the cluster according to the actual energy storage capacity and operating constraints of the energy storage nodes, such as the charging and discharging power limit and remaining power limit of the energy storage system. If the total historical energy storage demand of a certain cluster exceeds the processing capacity of the corresponding energy storage node, some distributed photovoltaic nodes are redistributed to other energy storage node clusters to ensure that the total energy storage demand of each cluster is within the capacity of the energy storage nodes, thereby obtaining the second node clustering results.

[0030] Step S140: Based on the connection relationships between edge nodes and corresponding energy storage nodes in the second node clustering results, construct multiple energy storage-PV edge networks. Specifically, based on the second node clustering results, determine the connection relationships between each distributed PV node (i.e., edge node) and its corresponding energy storage node. This can be achieved by establishing a node mapping table to record the cluster to which each edge node belongs and the corresponding energy storage node information. Using wired or wireless communication technologies, physically and communicatively connect the distributed PV nodes and their corresponding energy storage nodes according to the determined connection relationships. For wired connections, Ethernet cables, optical fibers, or other transmission media can be used; for wireless connections, communication protocols such as Wi-Fi, ZigBee, and 4G / 5G can be used. Simultaneously, configure corresponding communication modules and edge computing devices on each node to realize data transmission and interaction between nodes, as well as local data processing and decision control, thereby constructing multiple independent energy storage-PV edge networks.

[0031] Step S200: Perform energy efficiency analysis on the multiple energy storage-photovoltaic edge networks. Obtain the interaction energy efficiency index between each photovoltaic node and the corresponding energy storage node in each energy storage-photovoltaic network through edge computing. The interaction energy efficiency index includes charge and discharge energy conversion efficiency, dispatch command response speed and energy transmission line loss.

[0032] Specifically, in each energy storage-PV edge network, energy metering devices are installed at energy storage nodes to measure the input energy and stored energy when the PV node charges the energy storage node, as well as the output energy and released energy when the energy storage node discharges to other loads. Using edge computing devices, such as embedded microprocessors, the charge / discharge energy conversion efficiency is calculated according to the formulas: charging efficiency = (energy stored at energy storage node / input energy at PV node) × 100%, and discharging efficiency = (energy released at energy storage node / stored energy at energy storage node) × 100%. The edge computing devices are pre-configured with a time recording function for sending and receiving scheduling commands. When the upper-level control system sends scheduling commands, such as charging or discharging commands, to the energy storage-PV edge network, the edge computing device records the command sending time; after receiving the command, the energy storage node or PV node sends back confirmation information, and the edge computing device records the receiving time. The response time of the scheduling command is obtained by calculating the time difference between the two, thus measuring the response speed. Voltage and current sensors are installed at key locations on the transmission lines in the energy storage-PV edge network to collect voltage and current data at both ends of the line in real time. The edge computing device uses the collected data and the power calculation formula to calculate the input and output power of the line, where the line loss power = input power - output power. It then calculates the energy transmission line loss based on the line's operating time.

[0033] Step S300: Based on the interaction energy efficiency index between each photovoltaic node and the corresponding energy storage node, calculate multiple interaction energy efficiency consistency indices corresponding to the multiple energy storage-photovoltaic edge networks.

[0034] Specifically, using the standard deviation calculation method in statistics, for each energy storage-PV edge network, interaction energy efficiency index data between all PV nodes and their corresponding energy storage nodes are collected, including charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss. The average of these data is calculated; for example, for charge / discharge energy conversion efficiency, the average value = (sum of charge / discharge energy conversion efficiencies of all nodes) / number of nodes. The standard deviation of each interaction energy efficiency index is calculated using the standard deviation formula, reflecting the dispersion of the data relative to the average value. The standard deviations of multiple interaction energy efficiency indices for each energy storage-PV edge network are then combined, for example, using a weighted method based on the reciprocal of the standard deviation, to obtain the interaction energy efficiency consistency index for the network. The interaction energy efficiency consistency index measures the similarity or consistency of the interaction energy efficiency between each PV node and its corresponding energy storage node in an energy storage-PV edge network. A larger index value indicates that the interaction energy efficiency of each node in the network is closer, and the system operation is more stable and balanced; a smaller index value indicates that the interaction energy efficiency of each node differs significantly, and the system may have operational inconsistencies. The weights can be set based on the importance of each interactive energy efficiency indicator to the overall energy efficiency of the system.

[0035] In one possible implementation, based on the interaction energy efficiency index between each photovoltaic node and its corresponding energy storage node, multiple interaction energy efficiency consistency indices corresponding to the multiple energy storage-photovoltaic edge networks are calculated, and an interaction energy efficiency consistency index corresponding to an energy storage-photovoltaic edge network is calculated. Step S300 further includes step S310, which performs scale interval mapping on the charge-discharge energy conversion efficiency, scheduling command response speed, and energy transmission line loss to obtain the normalized interaction energy efficiency index between each photovoltaic node and its corresponding energy storage node. Specifically, for the charge-discharge energy conversion efficiency, its value ranges from 0 to 1, where 0 represents no energy conversion and 1 represents no energy loss during energy conversion. According to the actual application scenario and industry standards, a scale interval can be set, for example, 0.8-1 is divided into the excellent interval, 0.6-0.8 is the good interval, 0.4-0.6 is the average interval, and 0-0.4 is the poor interval. The response speed of dispatch commands can be measured by response time, which can be set within a range. For example, 0-100ms is excellent, 100-200ms is good, 200-500ms is average, and above 500ms is poor. Energy transmission line losses are expressed as a percentage, with a set range. For example, 0-2% is excellent, 2-5% is good, 5-10% is average, and above 10% is poor.

[0036] A linear mapping method is used to map the actual value of each indicator to a range of 0-1. For example, for charge / discharge energy conversion efficiency, if the actual value is x, the lower limit of the excellent range is a1=0.8, and the upper limit is b1=1, then the mapped value is y1=(x-a1) / (b1-a1). For dispatch command response speed, if the actual response time is t, and the upper limit of the excellent range is a2=100ms, then the mapped value is y2=1-t / a2. For energy transmission line loss, if the actual loss is l, and the upper limit of the excellent range is a3=2%, then the mapped value is y3=1-l / a3.

[0037] Step S320: Weight calculation is performed on the normalized interactive energy efficiency indicators to obtain N node interactive energy efficiency indicators for each photovoltaic node and its corresponding energy storage node, where N is the number of photovoltaic nodes. Specifically, based on the degree of influence of each indicator on the interactive energy efficiency, a weight value is assigned to each indicator to reflect the importance of different indicators when comprehensively calculating the node interactive energy efficiency indicators. The weights can be determined using expert experience, the analytic hierarchy process (AHP), or the entropy weight method. Taking the entropy weight method as an example, normalized interactive energy efficiency indicator data for multiple photovoltaic nodes and their corresponding energy storage nodes are collected, and the entropy value of each indicator is calculated. The larger the entropy value, the less information the indicator provides, and the smaller its weight; conversely, the smaller the entropy value, the larger its weight. The weight of each indicator is calculated based on the entropy value.

[0038] For each photovoltaic (PV) node and its corresponding energy storage node, the normalized interaction energy efficiency index is multiplied by its corresponding weight and then summed to obtain the interaction energy efficiency index for that node. This calculation is performed on all N PV nodes to obtain N node interaction energy efficiency indices for each PV node and its corresponding energy storage node.

[0039] Step S330: Calculate the standard deviation of the N node interaction energy efficiency indicators to output the interaction energy efficiency consistency index. Specifically, first, calculate the average of the N node interaction energy efficiency indicators; then, calculate the square of the difference between each node's interaction energy efficiency indicator and the average; next, calculate the average of these squared differences, i.e., the variance; finally, take the square root of the variance to obtain the standard deviation. The standard deviation is an indicator that measures the dispersion of a set of data, reflecting the degree of deviation between the data and the average, and is used to measure the magnitude of the differences among the N node interaction energy efficiency indicators. The smaller the standard deviation, the smaller the dispersion of the N node interaction energy efficiency indicators, meaning the more consistent the interaction energy efficiency among the nodes, and the larger the value of the interaction energy efficiency consistency index. Conversely, the larger the standard deviation, the smaller the value of the interaction energy efficiency consistency index, indicating a greater difference in interaction energy efficiency among the nodes. A mapping relationship between the standard deviation and the interaction energy efficiency consistency index can be established, such as using the reciprocal of the standard deviation as the interaction energy efficiency consistency index.

[0040] Step S400: According to the plurality of interactive energy efficiency consistency indicators, obtain a labeled energy storage-photovoltaic edge network. The labeled energy storage-photovoltaic edge network is an energy storage-photovoltaic edge network among the plurality of energy storage-photovoltaic edge networks whose interactive energy efficiency consistency indicators are less than a preset interactive energy efficiency consistency indicator threshold. The preset interactive energy efficiency consistency indicator threshold is configured by analyzing the return success distribution probability of parallel energy storage call request samples.

[0041] Specifically, a preset threshold for the interactive energy efficiency consistency index serves as the criterion for determining which energy storage-PV edge networks will be flagged. This threshold is configured by analyzing the success probability distribution of parallel energy storage call request samples. The process is as follows: In a real-world operating environment, a large amount of data related to parallel energy storage call requests is collected, including the request issuance time, request parameters, return results, and return times. Statistical analysis is performed on the collected sample data to calculate the probability of successful return for energy storage call requests under different values ​​of the interactive energy efficiency consistency index. For example, the range of interactive energy efficiency consistency index values ​​can be divided into certain intervals, and the proportion of successful returns within each interval to the total number of requests is calculated to obtain the success probability distribution. Based on the analysis results of the success probability distribution, an interactive energy efficiency consistency index value that guarantees a high success probability is selected as the threshold. For example, if the success probability of energy storage call requests reaches over 90% when the interactive energy efficiency consistency index is greater than or equal to a certain value, then this value can be used as the preset interactive energy efficiency consistency index threshold.

[0042] Based on the above screening criteria, multiple energy storage-PV edge networks were evaluated one by one. For each network, the calculated interaction energy efficiency consistency index of the energy storage-PV edge network was compared with the set interaction energy efficiency consistency index threshold. If the interaction energy efficiency consistency index is less than the interaction energy efficiency consistency index threshold, the network is marked as an energy storage-PV edge network that needs attention or optimization; if the index is greater than or equal to the interaction energy efficiency consistency index threshold, the network is not marked.

[0043] In one possible implementation, the preset interactive energy efficiency consistency index threshold is configured by analyzing the return success distribution probability of parallel energy storage call request samples. Step S400 further includes step S410, analyzing the parallel energy storage call request samples of each of the multiple energy storage-PV edge networks. The parallel energy storage call request samples are energy storage call request instructions sent simultaneously from all PV nodes in the energy storage-PV edge network to the energy storage node. Specifically, a parallel energy storage call request sample refers to the data set consisting of energy storage call request instructions sent simultaneously from multiple PV nodes to the energy storage node in the energy storage-PV edge network. For example, in a specific energy storage-PV edge network, there are 5 PV nodes. At the same time, 3 of these PV nodes send energy storage call requests to their corresponding energy storage nodes. These request instructions and their related information constitute a parallel energy storage call request sample.

[0044] In a real-world operating environment, a monitoring system for the energy storage-photovoltaic edge network collects samples of parallel energy storage request originations generated in each network. The collected data includes, but is not limited to, the request's issuance time, type, parameters, and the identifier of the photovoltaic node from which the request originated. These parallel energy storage request samples are then categorized and organized according to the different energy storage-photovoltaic edge networks. For example, all parallel energy storage request samples from network A are grouped into one category, those from network B into another, and so on.

[0045] Step S420: Predict the request return results of the corresponding energy storage nodes according to the parallel energy storage call request samples. The request return results include the probability distribution of successful energy storage call returns for each number of parallel energy storage call requests. Specifically, for each energy storage-PV edge network, using the collected parallel energy storage call request sample data and historical operating conditions, predict the probability distribution of successful energy storage call returns for the network under different numbers of parallel energy storage call requests. First, based on the collected request sample data, statistically analyze the request return situation under different numbers of requests. For example, statistically analyze the number of successfully returned requests when 3, 5, and 10 PV nodes simultaneously send energy storage call requests to the energy storage node. Use statistical methods or regression analysis to model and analyze these statistical data, and predict the probability distribution of successful energy storage call request returns under new, unprecedented request numbers.

[0046] Step S430: Obtain multiple request return results corresponding to the multiple energy storage-PV edge networks. Specifically, following the method in step S420, the same analysis and prediction operations are performed on each energy storage-PV edge network. Since different energy storage-PV edge networks have different operating scales, number of nodes, equipment performance, etc., the number of parallel energy storage call requests they correspond to will also differ. For each network, by analyzing its historical parallel energy storage call request samples, the probability distribution of successful energy storage call returns under different request numbers is predicted, thereby obtaining the request return results corresponding to each network. These results include the probability distribution of successful returns for each network under its respective different request numbers.

[0047] Step S440: Configure a preset interaction energy efficiency consistency index threshold using the multiple success probability distributions corresponding to the multiple request return results. Specifically, analyze the success probability distribution data of all networks to find an interaction energy efficiency consistency index value that can guarantee a high success probability in most networks. For example, a target can be set, such as hoping that the success probability of energy storage call requests reaches more than 90%. Then, in the success probability distribution data of all networks, find an interaction energy efficiency consistency index value such that when the interaction energy efficiency consistency index is less than this value, the success probability of energy storage call requests in most networks, such as more than 80% of the networks, can reach or exceed 90%. Use the determined interaction energy efficiency consistency index value as the preset interaction energy efficiency consistency index threshold to screen multiple energy storage-photovoltaic edge networks.

[0048] In one possible implementation, a preset interactive energy efficiency consistency index threshold is configured using the probability distribution of multiple successful responses corresponding to the multiple request return results. Step S440 further includes step S441, analyzing the peak number of parallel energy storage call requests in the parallel energy storage call request samples. Specifically, the peak number of parallel energy storage call requests is the maximum number of photovoltaic nodes simultaneously sending energy storage call requests in all samples among the collected sample data. The parallel energy storage call request sample data is collected through the monitoring system of the energy storage-photovoltaic edge network, and the number of photovoltaic nodes simultaneously sending requests in each sample is counted. For example, for a certain energy storage-photovoltaic edge network, in the collected sample data, sample 1 has 3 photovoltaic nodes simultaneously sending requests, sample 2 has 5 photovoltaic nodes simultaneously sending requests, sample 3 has 2 photovoltaic nodes simultaneously sending requests, and sample 4 has 7 photovoltaic nodes simultaneously sending requests. Then the peak number of parallel energy storage call requests for this network is 7. This statistical analysis is performed on all energy storage-photovoltaic edge networks to obtain the peak number of parallel energy storage call requests for each network.

[0049] Step S442: Obtain the average probability of successful energy storage call returns under the peak number of parallel energy storage call requests. Specifically, for each energy storage-PV edge network, under its peak number of parallel energy storage call requests, the ratio of the number of successful energy storage call requests in historical data to the total number of requests is calculated to obtain the probability of successful energy storage call returns for that network under that peak number. For example, if a network has 100 parallel requests when the peak number of parallel energy storage call requests is 8, and 85 of them are successful, then the probability of successful energy storage call returns for that network under that peak number is 85%. This statistical analysis is performed on all energy storage-PV edge networks, and then the average of these success probabilities is calculated to obtain the average probability of successful energy storage call returns.

[0050] Step S443 involves mapping the average probability of successful energy storage call return to an interactive energy efficiency consistency index, and configuring a preset threshold for the interactive energy efficiency consistency index. This mapping relationship is a mapping between the probability of successful energy storage call return and the interactive energy efficiency consistency index. Specifically, the mapping relationship between the probability of successful energy storage call return and the interactive energy efficiency consistency index is established through extensive experimental data, historical data analysis, or theoretical derivation. For example, by collecting data on the probability of successful energy storage call return under different interactive energy efficiency consistency indices for different energy storage-PV edge networks, analyzing their changing patterns, and plotting a probability-index curve, the correspondence between the two can be determined. Based on the established mapping relationship, the average probability of successful energy storage call return obtained in step S442 is substituted to find the corresponding interactive energy efficiency consistency index value. For example, if the established mapping relationship shows that when the probability of a successful return of an energy storage call is 90%, the corresponding interactive energy efficiency consistency index value is 0.8, and it is desired that the probability of a successful return of an energy storage call request reaches a certain high level, such as above 90%, then this corresponding interactive energy efficiency consistency index value of 0.8 can be used as the preset interactive energy efficiency consistency index threshold.

[0051] Step S500: Reconstruct the edge nodes of the marked energy storage-photovoltaic edge network to obtain multiple energy storage-photovoltaic edge reconstruction networks.

[0052] Specifically, for a labeled energy storage-PV edge network, the interaction energy efficiency data of each PV node and energy storage node are analyzed. Based on this data, the matching relationship between PV nodes and energy storage nodes is redefined to improve the network's energy efficiency consistency and enhance the overall system performance. Based on the results of the node rematching, the communication links in the network are adjusted. If the existing communication links cannot meet the new node connection requirements, the communication equipment is replaced or a new communication protocol is adopted to ensure stable and efficient data transmission and command interaction between the edge nodes.

[0053] In one possible implementation, the marked energy storage-photovoltaic edge network undergoes edge node reconstruction. Step S500 further includes step S510, identifying abnormal photovoltaic nodes in the marked energy storage-photovoltaic edge network. Specifically, from the monitoring system of the marked energy storage-photovoltaic edge network, interaction energy efficiency index data between all photovoltaic nodes and their corresponding energy storage nodes are collected, including charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss. Statistical methods are used to set normal ranges, for example, calculating the average and standard deviation of each interaction energy efficiency index, and setting the normal range by adding or subtracting twice the standard deviation from the average. For charge / discharge energy conversion efficiency, if the average is 85% and the standard deviation is 5%, the normal range can be set to 75%-95%; for dispatch command response speed, if the average response time is 150ms and the standard deviation is 30ms, the normal range can be set to 90ms-210ms; for energy transmission line loss, if the average loss is 3% and the standard deviation is 1%, the normal range can be set to 1%-5%. The interaction energy efficiency index of each photovoltaic node is compared with the set normal range. If a certain interactive energy efficiency index of a photovoltaic node exceeds the normal range, the node is judged as an abnormal photovoltaic node.

[0054] Step S520: Based on the distribution locations of the abnormal photovoltaic nodes and the multiple energy storage nodes, candidate energy storage nodes are determined. Specifically, using GPS technology, the geographical location information of the abnormal photovoltaic nodes and all energy storage nodes, including latitude and longitude coordinates, is obtained. The Euclidean distance formula is used to calculate the spatial distance between the abnormal photovoltaic nodes and each energy storage node. Based on the magnitude of the spatial distance, several energy storage nodes that are relatively close to the abnormal photovoltaic nodes are selected as candidate energy storage nodes. The number of nodes selected can be set according to the actual situation, for example, selecting 3-5 of the closest energy storage nodes as candidates. At the same time, considering other factors, such as the remaining capacity of the energy storage nodes, the candidate energy storage nodes are further screened and ranked. For example, if an energy storage node is close to an abnormal photovoltaic node, but its remaining capacity is close to the upper limit and cannot meet the energy storage needs of the abnormal photovoltaic node, then the node can be excluded from the candidate list.

[0055] Step S530: Based on the interaction energy efficiency indicators of the abnormal photovoltaic node, similarity reconstruction is performed on the candidate energy storage nodes to obtain the reconstructed energy storage-photovoltaic edge network corresponding to the marked energy storage-photovoltaic edge network. Specifically, for each candidate energy storage node, its similarity with the abnormal photovoltaic node in various interaction energy efficiency indicators is calculated. Cosine similarity, Euclidean similarity, or other methods can be used for calculation. Taking cosine similarity as an example, the similarity value ranges from -1 to 1; the closer the value is to 1, the more similar the two nodes are in the interaction energy efficiency indicators. Based on the calculated similarity, the candidate energy storage node with the highest similarity is selected and re-matched with the abnormal photovoltaic node. The abnormal photovoltaic node is removed from the original energy storage-photovoltaic edge network and connected to the newly matched energy storage node. Simultaneously, the network's node mapping table and communication connection relationships are updated to ensure stable and efficient data transmission and command interaction between the edge nodes. By reconstructing similarity, abnormal photovoltaic nodes and new energy storage nodes are better matched in terms of interactive energy efficiency, thereby improving the energy efficiency consistency of the entire labeled energy storage-photovoltaic edge network and obtaining the reconstructed energy storage-photovoltaic edge network.

[0056] In one possible implementation, step S500 further includes step S540, after obtaining the reconstructed energy storage-photovoltaic edge network corresponding to the marked energy storage-photovoltaic edge network, determining whether the interaction energy efficiency consistency index of the reconstructed energy storage-photovoltaic edge network is less than the preset interaction energy efficiency consistency index threshold. Specifically, after completing the edge node reconstruction of the marked energy storage-photovoltaic edge network and obtaining the reconstructed energy storage-photovoltaic edge network, the interaction energy efficiency consistency index of the reconstructed network is recalculated. The calculation method is the same as the method used in step S300 to calculate the interaction energy efficiency consistency index of the energy storage-photovoltaic edge network. The calculated interaction energy efficiency consistency index of the reconstructed energy storage-photovoltaic edge network is compared with the preset interaction energy efficiency consistency index threshold. If the interaction energy efficiency consistency index of the reconstructed energy storage-photovoltaic edge network is greater than or equal to the preset interaction energy efficiency consistency index threshold, it indicates that the reconstructed network has met the expected requirements in terms of energy efficiency consistency; if it is less than the threshold, it indicates that the reconstructed network still has problems in terms of energy efficiency consistency.

[0057] Step S550: If the interaction energy efficiency consistency index of the reconstructed energy storage-PV edge network is less than the preset interaction energy efficiency consistency index threshold, a global reconstruction is performed on the multiple energy storage-PV edge networks based on the preset interaction energy efficiency consistency index threshold to obtain multiple reconstructed energy storage-PV edge networks. Specifically, local reconstruction only adjusts abnormal PV nodes in the marked energy storage-PV edge network, which may ignore the mutual influence and coordination relationships between multiple energy storage-PV edge networks in the entire distributed PV system. Therefore, if the interaction energy efficiency consistency index of the reconstructed energy storage-PV edge network is less than the preset interaction energy efficiency consistency index threshold, a global reconstruction is required.

[0058] With a preset threshold for interactive energy efficiency consistency as the objective, the optimization goal of global reconfiguration is determined by integrating the collaborative operation of multiple energy storage-PV edge networks. For example, while ensuring that the energy efficiency consistency index of each network is not lower than the threshold, the goal is to maximize the rational allocation and efficient utilization of energy storage resources throughout the system. Based on the global optimization goal, a clustering algorithm is used to redetermine the correspondence between PV nodes and energy storage nodes. After reconfiguration, the interactive energy efficiency consistency index of multiple energy storage-PV edge networks is recalculated and compared with the preset threshold to verify the reconfiguration effect, until the interactive energy efficiency consistency index of all networks reaches or exceeds the threshold. Based on the results of node reallocation, the communication links in the network are adjusted to ensure stable and efficient data transmission and command interaction between edge nodes.

[0059] This application's embodiments employ techniques such as acquiring distributed photovoltaic (PV) nodes, connecting them as edge nodes to corresponding energy storage nodes, constructing multiple energy storage-PV edge networks, performing energy efficiency analysis on these energy storage-PV edge networks, using edge computing to obtain the interaction energy efficiency index between PV nodes and energy storage nodes in each network, calculating the interaction energy efficiency consistency index for each energy storage-PV edge network based on the interaction energy efficiency index, marking specific energy storage-PV edge networks based on the interaction energy efficiency consistency index, reconstructing edge nodes in the marked energy storage-PV edge networks, and finally obtaining multiple reconstructed energy storage-PV edge networks. These techniques solve the technical problems of inaccuracy and untimely response in existing distributed PV operation energy efficiency analysis, achieving the technical effect of improving the accuracy and timeliness of operation energy efficiency analysis, thereby improving the energy efficiency consistency of the entire system.

[0060] In the preceding text, a method for analyzing the energy efficiency of distributed photovoltaic (PV) operation based on edge computing according to an embodiment of the present invention was described in detail with reference to FIG1. ​​Next, a system for analyzing the energy efficiency of distributed PV operation based on edge computing according to an embodiment of the present invention will be described with reference to FIG2.

[0061] The edge computing-based distributed photovoltaic (PV) operation energy efficiency analysis system according to embodiments of the present invention addresses the technical problems of inaccuracy and untimely response in existing distributed PV operation energy efficiency analysis, thereby improving the accuracy and timeliness of operation energy efficiency analysis and ultimately enhancing the energy efficiency consistency of the entire system. The edge computing-based distributed PV operation energy efficiency analysis system includes: an energy storage-PV edge network construction module 10, an interactive energy efficiency index calculation module 20, an interactive energy efficiency consistency index calculation module 30, an energy storage-PV edge network marking module 40, and an edge node reconstruction module 50.

[0062] The energy storage-photovoltaic edge network construction module 10 is used to acquire distributed photovoltaic nodes, connect the distributed photovoltaic nodes as edge nodes with corresponding energy storage nodes, and construct multiple energy storage-photovoltaic edge networks; the interaction energy efficiency index calculation module 20 is used to perform energy efficiency analysis on the multiple energy storage-photovoltaic edge networks, and obtain the interaction energy efficiency index between each photovoltaic node and the corresponding energy storage node in each energy storage-photovoltaic network through edge computing, wherein the interaction energy efficiency index includes charge and discharge energy conversion efficiency, dispatch command response speed and energy transmission line loss; the interaction energy efficiency consistency index calculation module 30 is used to calculate multiple interaction energy efficiency consistency indices corresponding to the multiple energy storage-photovoltaic edge networks according to the interaction energy efficiency index between each photovoltaic node and the corresponding energy storage node; the energy storage-photovoltaic edge network marking module 40 is used to obtain marked energy storage-photovoltaic edge networks according to the multiple interaction energy efficiency consistency indices; the edge node reconstruction module 50 is used to reconstruct the edge nodes of the marked energy storage-photovoltaic edge networks and obtain multiple energy storage-photovoltaic edge reconstruction networks.

[0063] The detailed description of the specific configuration of the energy storage-photovoltaic edge network marking module 40 is explained as follows: As mentioned above, the energy storage-photovoltaic edge network marking module 40 may further include: obtaining marked energy storage-photovoltaic edge networks according to the plurality of interactive energy efficiency consistency indicators, wherein the marked energy storage-photovoltaic edge networks are energy storage-photovoltaic edge networks whose interactive energy efficiency consistency indicators are less than a preset interactive energy efficiency consistency indicator threshold among the plurality of energy storage-photovoltaic edge networks, wherein the preset interactive energy efficiency consistency indicator threshold is configured by analyzing the return success distribution probability of parallel energy storage call request samples.

[0064] The preset interactive energy efficiency consistency index threshold is configured by analyzing the return success distribution probability of parallel energy storage call request samples. The energy storage-photovoltaic edge network marking module 40 may further include: a parallel energy storage call request sample analysis unit for analyzing the parallel energy storage call request samples of each of the multiple energy storage-photovoltaic edge networks, wherein the parallel energy storage call request samples are energy storage call request instructions sent simultaneously by all photovoltaic nodes in the energy storage-photovoltaic edge network to the energy storage node; a request return result prediction unit for predicting the request return result of the corresponding energy storage node according to the parallel energy storage call request samples, wherein the request return result includes the energy storage call return success distribution probability under each number of parallel energy storage call requests; a request return result acquisition unit for acquiring multiple request return results corresponding to the multiple energy storage-photovoltaic edge networks; and a preset interactive energy efficiency consistency index threshold configuration unit for configuring the preset interactive energy efficiency consistency index threshold using the multiple return success distribution probabilities corresponding to the multiple request return results.

[0065] Specifically, the preset interactive energy efficiency consistency index threshold is configured using the multiple success distribution probabilities corresponding to the multiple request return results. The preset interactive energy efficiency consistency index threshold configuration unit may further include: a peak parallel energy storage call request quantity analysis subunit for analyzing the peak parallel energy storage call request quantity of the parallel energy storage call request sample; an energy storage call return success mean probability acquisition subunit for acquiring the energy storage call return success mean probability under the peak parallel energy storage call request quantity; and an interactive energy efficiency consistency index mapping subunit for mapping the energy storage call return success mean probability to an interactive energy efficiency consistency index and configuring the preset interactive energy efficiency consistency index threshold.

[0066] The interactive energy efficiency consistency index mapping subunit may further include: using a mapping relationship to map the average probability of successful return of the energy storage call to the interactive energy efficiency consistency index, wherein the mapping relationship is the mapping between the probability of successful return of the energy storage call and the interactive energy efficiency consistency index.

[0067] The detailed configuration of the energy storage-photovoltaic edge network construction module 10 is explained as follows: As mentioned above, the distributed photovoltaic nodes are connected to the corresponding energy storage nodes as edge nodes to construct multiple energy storage-photovoltaic edge networks. The energy storage-photovoltaic edge network construction module 10 may further include: a distribution location acquisition unit for acquiring multiple energy storage nodes, acquiring the distribution locations of the distributed photovoltaic nodes and the multiple energy storage nodes; a spatial distance clustering unit for performing spatial distance clustering based on the distribution locations of the distributed photovoltaic nodes and the multiple energy storage nodes, using the multiple energy storage nodes as clustering nodes, to obtain a first node clustering result; a total demand constraint update unit for acquiring historical energy storage demand indicators of the distributed photovoltaic nodes, updating the total demand constraint of the first node clustering result according to the historical energy storage demand indicators, and obtaining a second node clustering result; and an energy storage-photovoltaic edge network construction unit for constructing multiple energy storage-photovoltaic edge networks based on the connection relationship between the edge nodes and the corresponding energy storage nodes in the second node clustering result.

[0068] The detailed description of the specific configuration of the interactive energy efficiency consistency index calculation module 30 is as follows: As mentioned above, based on the interactive energy efficiency index of each photovoltaic node and its corresponding energy storage node, multiple interactive energy efficiency consistency indices corresponding to the multiple energy storage-photovoltaic edge networks are calculated, and an interactive energy efficiency consistency index corresponding to one energy storage-photovoltaic edge network is calculated. The interactive energy efficiency consistency index calculation module 30 may further include: a scale interval mapping unit for performing scale interval mapping on the charge and discharge energy conversion efficiency, scheduling command response speed, and energy transmission line loss to obtain the normalized interactive energy efficiency index of each photovoltaic node and its corresponding energy storage node; a weight calculation unit for performing weight calculation on the normalized interactive energy efficiency index to obtain N node interactive energy efficiency indices of each photovoltaic node and its corresponding energy storage node, where N is the number of photovoltaic nodes; and a standard deviation calculation unit for calculating the standard deviation of the N node interactive energy efficiency indices and outputting the interactive energy efficiency consistency index.

[0069] The detailed description of the specific configuration of the edge node reconstruction module 50 is explained as follows: As described above, the edge node reconstruction module 50 may further include: an abnormal photovoltaic node identification unit for identifying abnormal photovoltaic nodes in the marked energy storage-photovoltaic edge network; a candidate energy storage node determination unit for determining candidate energy storage nodes based on the distribution location of the abnormal photovoltaic nodes and the distribution location of multiple energy storage nodes; and a similarity reconstruction unit for performing similarity reconstruction on the candidate energy storage nodes based on the interaction energy efficiency index of the abnormal photovoltaic nodes to obtain the reconstructed energy storage-photovoltaic edge network corresponding to the marked energy storage-photovoltaic edge network.

[0070] The edge node reconfiguration module 50 may further include: after obtaining the reconfigured energy storage-photovoltaic edge network corresponding to the marked energy storage-photovoltaic edge network, determining whether the interaction energy efficiency consistency index of the reconfigured energy storage-photovoltaic edge network is less than the preset interaction energy efficiency consistency index threshold; if the interaction energy efficiency consistency index of the reconfigured energy storage-photovoltaic edge network is less than the preset interaction energy efficiency consistency index threshold, performing global reconfiguration on the multiple energy storage-photovoltaic edge networks based on the preset interaction energy efficiency consistency index threshold to obtain multiple energy storage-photovoltaic edge reconfiguration networks.

[0071] The edge computing-based distributed photovoltaic energy efficiency analysis system provided in this invention can execute the edge computing-based distributed photovoltaic energy efficiency analysis method provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the method.

[0072] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0073] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for analyzing the energy efficiency of distributed photovoltaic systems based on edge computing, characterized in that, The method includes: acquiring distributed photovoltaic (PV) nodes; connecting the distributed PV nodes as edge nodes to corresponding energy storage nodes to construct multiple energy storage-PV edge networks; performing energy efficiency analysis on the multiple energy storage-PV edge networks; obtaining the interaction energy efficiency index between each PV node and its corresponding energy storage node in each energy storage-PV network through edge computing, wherein the interaction energy efficiency index includes charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss; calculating multiple interaction energy efficiency consistency indices corresponding to the multiple energy storage-PV edge networks based on the interaction energy efficiency indices between each PV node and its corresponding energy storage node; and calculating the interaction energy efficiency consistency indices corresponding to the multiple energy storage-PV edge networks based on the interaction energy efficiency indices between each PV node and its corresponding energy storage node. Calculating the interaction energy efficiency consistency index for an energy storage-PV edge network involves: performing scale interval mapping on the charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss to obtain a normalized interaction energy efficiency index for each PV node and its corresponding energy storage node; calculating the weights of the normalized interaction energy efficiency index to obtain N node interaction energy efficiency indices for each PV node and its corresponding energy storage node, where N is the number of PV nodes; calculating the standard deviation of the N node interaction energy efficiency indices to output the interaction energy efficiency consistency index; obtaining a labeled energy storage-PV edge network according to the multiple interaction energy efficiency consistency indices; and reconstructing the edge nodes of the labeled energy storage-PV edge network to obtain multiple reconstructed energy storage-PV edge networks.

2. The method for analyzing the energy efficiency of distributed photovoltaic power generation based on edge computing as described in claim 1, characterized in that, According to the multiple interactive energy efficiency consistency indicators, a labeled energy storage-photovoltaic edge network is obtained. The labeled energy storage-photovoltaic edge network is the energy storage-photovoltaic edge network among the multiple energy storage-photovoltaic edge networks whose interactive energy efficiency consistency indicators are less than a preset interactive energy efficiency consistency indicator threshold. The preset interactive energy efficiency consistency indicator threshold is configured by analyzing the return success distribution probability of parallel energy storage call request samples.

3. The method for analyzing the energy efficiency of distributed photovoltaic systems based on edge computing as described in claim 2, characterized in that, The preset interactive energy efficiency consistency index threshold is configured by analyzing the return success distribution probability of parallel energy storage call request samples. The method includes: analyzing the parallel energy storage call request samples of each of the multiple energy storage-PV edge networks, wherein the parallel energy storage call request samples are energy storage call request instructions sent simultaneously by all PV nodes in the energy storage-PV edge network to the energy storage nodes; predicting the request return result of the corresponding energy storage node according to the parallel energy storage call request samples, wherein the request return result includes the energy storage call return success distribution probability under each number of parallel energy storage call requests; obtaining multiple request return results corresponding to the multiple energy storage-PV edge networks; and configuring the preset interactive energy efficiency consistency index threshold using the multiple return success distribution probabilities corresponding to the multiple request return results.

4. The method for analyzing the energy efficiency of distributed photovoltaic power generation based on edge computing as described in claim 3, characterized in that, The method for configuring a preset interactive energy efficiency consistency index threshold by utilizing the multiple success distribution probabilities corresponding to the multiple request return results includes: analyzing the peak number of parallel energy storage call requests of the parallel energy storage call request sample; obtaining the average probability of successful energy storage call return under the peak number of parallel energy storage call requests; mapping the average probability of successful energy storage call return to the interactive energy efficiency consistency index; and configuring a preset interactive energy efficiency consistency index threshold.

5. The method for analyzing the energy efficiency of distributed photovoltaic systems based on edge computing as described in claim 4, characterized in that, The average probability of successful return of energy storage call is mapped to the interactive energy efficiency consistency index using a mapping relationship, wherein the mapping relationship is the mapping between the probability of successful return of energy storage call and the interactive energy efficiency consistency index.

6. The method for analyzing the energy efficiency of distributed photovoltaic systems based on edge computing as described in claim 1, characterized in that, The method involves connecting the distributed photovoltaic (PV) nodes as edge nodes with corresponding energy storage nodes to construct multiple energy storage-PV edge networks. The method includes: acquiring multiple energy storage nodes; collecting the distribution locations of the distributed PV nodes and the multiple energy storage nodes; performing spatial distance clustering based on the distribution locations of the distributed PV nodes and the multiple energy storage nodes, using the multiple energy storage nodes as clustering nodes to obtain a first node clustering result; collecting historical energy storage demand indicators of the distributed PV nodes; updating the first node clustering result with total demand constraints based on the historical energy storage demand indicators to obtain a second node clustering result; and constructing multiple energy storage-PV edge networks based on the connection relationships between edge nodes and corresponding energy storage nodes in the second node clustering result.

7. The method for analyzing the energy efficiency of distributed photovoltaic power generation based on edge computing as described in claim 4, characterized in that, The method for reconstructing edge nodes of the labeled energy storage-photovoltaic edge network includes: identifying abnormal photovoltaic nodes in the labeled energy storage-photovoltaic edge network; determining candidate energy storage nodes based on the distribution locations of the abnormal photovoltaic nodes and multiple energy storage nodes; and performing similarity reconstruction on the candidate energy storage nodes based on the interaction energy efficiency index of the abnormal photovoltaic nodes to obtain the reconstructed energy storage-photovoltaic edge network corresponding to the labeled energy storage-photovoltaic edge network.

8. The method for analyzing the energy efficiency of distributed photovoltaic power generation based on edge computing as described in claim 7, characterized in that, After obtaining the reconstructed energy storage-photovoltaic edge network corresponding to the marked energy storage-photovoltaic edge network, it is determined whether the interaction energy efficiency consistency index of the reconstructed energy storage-photovoltaic edge network is less than the preset interaction energy efficiency consistency index threshold. If the interaction energy efficiency consistency index of the reconstructed energy storage-photovoltaic edge network is less than the preset interaction energy efficiency consistency index threshold, the multiple energy storage-photovoltaic edge networks are globally reconstructed based on the preset interaction energy efficiency consistency index threshold to obtain multiple energy storage-photovoltaic edge reconstructed networks.

9. A distributed photovoltaic operation energy efficiency analysis system based on edge computing, characterized in that, The system is used to implement the distributed photovoltaic operation energy efficiency analysis method based on edge computing as described in any one of claims 1-8. The system includes: an energy storage-photovoltaic edge network construction module, used to acquire distributed photovoltaic nodes, connect the distributed photovoltaic nodes as edge nodes to corresponding energy storage nodes, and construct multiple energy storage-photovoltaic edge networks; an interaction energy efficiency index calculation module, used to perform energy efficiency analysis on the multiple energy storage-photovoltaic edge networks, and obtain the interaction energy efficiency index between each photovoltaic node and its corresponding energy storage node in each energy storage-photovoltaic network through edge computing, wherein the interaction energy efficiency index includes charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss; and an interaction energy efficiency consistency index calculation module, used to calculate multiple interaction energy efficiency consistency indices corresponding to the multiple energy storage-photovoltaic edge networks based on the interaction energy efficiency index between each photovoltaic node and its corresponding energy storage node. The interactive energy efficiency consistency index calculation module includes: a scale interval mapping unit for performing scale interval mapping on the charge / discharge energy conversion efficiency, dispatch command response speed, and energy transmission line loss to obtain a normalized interactive energy efficiency index for each photovoltaic node and its corresponding energy storage node; a weight calculation unit for performing weight calculation on the normalized interactive energy efficiency index to obtain N node interactive energy efficiency indices for each photovoltaic node and its corresponding energy storage node, where N is the number of photovoltaic nodes; a standard deviation calculation unit for calculating the standard deviation of the N node interactive energy efficiency indices to output the interactive energy efficiency consistency index; an energy storage-photovoltaic edge network marking module for obtaining a marked energy storage-photovoltaic edge network according to the multiple interactive energy efficiency consistency indices; and an edge node reconstruction module for reconstructing the marked energy storage-photovoltaic edge network to obtain multiple energy storage-photovoltaic edge reconstruction networks.

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