Industrial and commercial energy storage power station control method and device, electronic equipment and storage medium

Abstract

The invention provides an industrial and commercial energy storage power station control method and device, electronic equipment and a storage medium, and belongs to the technical field of energy storage power stations, and the method comprises the steps: constructing a distributed data sharing network based on a block chain for an energy storage power station, and taking the operation state data of an energy storage station as node data corresponding to the energy storage station; distributing charging and discharging power for each node based on the power grid power demand and the node data; determining nodes meeting a first condition from all the nodes, so that each node meeting the first condition executes charging and discharging power adjustment operation; the charging and discharging power adjustment operation comprises the following steps: acquiring node data of a first adjacent node of the node, and determining a power distribution strategy based on the node data of the node and the node data of the first adjacent node; and adjusting the charging and discharging power of the node and the charging and discharging power of the first adjacent node based on a power distribution strategy. According to the invention, accurate control of the operation power of the energy storage power station can be realized through a more flexible control method.

Description

Technical Field

[0001] This application belongs to the technical field of energy storage power stations. More specifically, it relates to a control method and device for industrial and commercial energy storage power stations, an electronic device, and a storage medium. Background Art

[0002] With the rapid growth of industrial and commercial electricity demand and the expansion of the scale of renewable energy grid connection, energy storage power stations, as the core hubs of energy regulation, play a key role in balancing the power grid supply and demand and enhancing the consumption capacity of renewable energy. In actual operation, industrial and commercial energy storage power stations are composed of multiple distributed energy storage sites, which interact closely with the power grid. They need to respond to the requirements of the power grid for peak shaving, frequency modulation, etc., and also meet the demands of industrial and commercial users to reduce electricity costs.

[0003] However, traditional industrial and commercial energy storage power stations mostly adopt a centralized control architecture. The central controller collects the operation data of each energy storage site and uniformly distributes charge and discharge instructions. In this mode, the power distribution of energy storage sites highly depends on the central dispatching. There is not only a risk of single-point failure. Once the central dispatching fails, it will affect the stable operation of the energy storage power station and the power grid. Moreover, the unified control strategy easily causes some sites to be overloaded due to unreasonable power distribution, shortening the equipment life, or causing resource waste and reducing the overall energy storage efficiency. Therefore, a more flexible control method for energy storage power stations is needed to achieve precise control of the operation power of energy storage power stations. Summary of the Invention

[0004] The purpose of this application is to provide a control method and device for industrial and commercial energy storage power stations, an electronic device, and a storage medium, and to achieve precise control of the operation power of energy storage power stations through a more flexible control method.

[0005] In the first aspect of the embodiments of this application, a control method for industrial and commercial energy storage power stations is provided, including: Construct a blockchain-based distributed data sharing network for the energy storage power station, where each energy storage site in the energy storage power station is used as a node of the distributed data sharing network, and the operation status data of each energy storage site is used as the node data corresponding to the site; Allocate charge and discharge power to each node based on the power grid power demand and the node data of all nodes; Determine the nodes that meet the first condition from all nodes, so that each node that meets the first condition performs a charge and discharge power adjustment operation; the nodes that meet the first condition are the nodes whose remaining capacity is less than the first capacity threshold; Among them, the charge and discharge power adjustment operation includes: obtaining the node data of the first adjacent node of the node, and determining a power distribution strategy based on the node data of the node and the node data of the first adjacent node; adjusting the charge and discharge power of the node and the charge and discharge power of the first adjacent node based on the power distribution strategy, where the first adjacent node is an adjacent node that directly communicates with the node through a self-organizing network protocol.

[0006] In a second aspect of the embodiments of the present application, there is provided a control device for an industrial and commercial energy storage power station, including: A data sharing network construction module, configured to construct a distributed data sharing network based on a blockchain for the energy storage power station. Among them, each energy storage site in the energy storage power station serves as a node of the distributed data sharing network, and the operation status data of each energy storage site serves as the node data corresponding to the site; A global power distribution module, configured to allocate charge and discharge power to each node based on the power grid power demand and the node data of all nodes; A local power distribution module, configured to determine nodes that meet a first condition from all nodes, so that each node that meets the first condition performs a charge and discharge power adjustment operation; the nodes that meet the first condition are nodes whose remaining capacity is less than a first capacity threshold; Among them, the charge and discharge power adjustment operation includes: obtaining the node data of the first adjacent node of the node, and determining a power distribution strategy based on the node data of the node and the node data of the first adjacent node; adjusting the charge and discharge power of the node and the charge and discharge power of the first adjacent node based on the power distribution strategy, where the first adjacent node is an adjacent node that directly communicates with the node through a self-organizing network protocol.

[0007] In a third aspect of the embodiments of the present application, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned industrial and commercial energy storage power station control method are implemented.

[0008] In a fourth aspect of the embodiments of the present application, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned industrial and commercial energy storage power station control method are implemented.

[0009] The beneficial effects of the industrial and commercial energy storage power station control method, device, electronic device, and storage medium provided by the embodiments of the present application are as follows: The blockchain-based distributed data sharing network architecture provided by the embodiments of the present application can solve the single-point failure problem of traditional centralized control. Each energy storage site stores and interacts with data independently in the form of nodes. Even if some nodes fail, the system can still maintain operation through other nodes, greatly improving reliability. Different from the fixed power control of traditional centralization, the embodiments of the present application endow the energy storage power station with flexible power distribution capabilities through the blockchain distributed data sharing network and the self-organizing communication architecture. The embodiments of the present application support direct communication between nodes. Each node can obtain the data of adjacent nodes in real time. When the remaining capacity of a node is insufficient, it can quickly link adjacent nodes and dynamically adjust the power distribution strategy according to their operating states to avoid single-point overload or resource idleness and achieve efficient coordination among multiple sites. The precise power adjustment strategy provided by the embodiments of the present application can reduce battery loss caused by overcharging and over-discharging and extend the service life of energy storage devices. At the same time, by combining the power demand of the power grid to allocate charging and discharging power, it can better participate in the peak shaving and frequency modulation of the power grid and improve energy utilization efficiency. Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0011] Figure 1 It is a schematic flow chart of the industrial and commercial energy storage power station control method provided by an embodiment of the present application; Figure 2 It is a structural block diagram of the industrial and commercial energy storage power station control device provided by an embodiment of the present application; Figure 3 It is a schematic block diagram of the electronic device provided by an embodiment of the present application. Detailed Embodiments

[0012] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0013] To make the purpose, technical solutions, and advantages of the present application clearer, the following will be described through specific embodiments with reference to the drawings.

[0014] Please refer toFigure 1 , Figure 1 It is a schematic flowchart of a control method for an industrial and commercial energy storage power station provided by an embodiment of the present application. This method can be executed by an electronic device. Specifically, this method may include S101 to S103.

[0015] S101: Construct a blockchain-based distributed data sharing network for the energy storage power station. Among them, each energy storage site in the energy storage power station serves as a node of the distributed data sharing network, and the operation status data of each energy storage site serves as the node data corresponding to that energy storage site.

[0016] In this embodiment, the energy storage power station refers to an industrial and commercial energy storage power station, which is an overall system composed of multiple distributed energy storage sites, undertaking functions such as electric energy storage, regulation, and interaction with the power grid, with the goal of balancing user electricity demand and power grid supply. The blockchain-based distributed data sharing network is a decentralized data storage technology that synchronously records the operation data of each energy storage site in the form of encrypted blocks in all nodes, ensuring that the data is tamper-proof, traceable, and shared in real time. Regarding the energy storage site as a node means regarding each physically independent energy storage unit as a node in the blockchain network, and each node has the capabilities of data storage, communication, and autonomous decision-making. The operation status data may include basic parameters such as the remaining battery capacity, rated capacity, charge and discharge power thresholds, real-time status data such as the current charge and discharge power, battery temperature, and health status, and environmental data such as real-time electricity prices and grid demands.

[0017] In this embodiment, each energy storage site broadcasts the real-time operation data after encryption to the blockchain network, and all nodes synchronously update the ledger. For example, when the remaining capacity of a factory energy storage site is lower than 20%, its status data can be automatically synchronized to the adjacent commercial building energy storage node without relaying through a central server.

[0018] Nodes communicate directly through self-organizing network protocols such as an improved P2P protocol, and lightweight consensus algorithms such as the Byzantine fault tolerance algorithm are used to verify data consistency. For example, after receiving a warning of insufficient capacity, adjacent nodes respond quickly and negotiate a power adjustment strategy to avoid the communication delay of a centralized system.

[0019] In this embodiment, rules can be preset in advance, such as triggering adjacent node collaboration when the remaining capacity is lower than a threshold. The rules are encoded as smart contracts. When the node data meets the preset conditions, the contract is automatically activated, generates a power adjustment plan based on the adjacent node data, such as increasing the charging power of this node and reducing the discharging power of adjacent nodes, and broadcasts it to relevant devices through the blockchain for execution.

[0020] Exemplarily, assume that an industrial park cluster contains 3 energy storage sites: Factory A, Factory B, and Commercial Building C. Each site uploads the remaining capacity and current power to the blockchain in real time. For example, the remaining capacity of Factory A is 15% (the percentage is calculated relative to the total capacity), the remaining capacity of Factory B is 60%, and the remaining capacity of Commercial Building C is 40%; the discharging power of Factory A is 20 kW, the charging power of Factory B is 10 kW, and the discharging power of Commercial Building C is 15 kW.

[0021] The remaining capacity of Factory A is lower than 20%. It sends requests to adjacent Factory B and Commercial Building C through the self-organizing network. Factory B is currently charging and has sufficient capacity, so it can reduce the charging power by 10 kW and switch to supplying power to Factory A; the discharging power of Commercial Building C can be increased by 5 kW to share the load of Factory A. The smart contract verifies that the power of each node after adjustment does not exceed the limit. For example, the charging power of Factory B is not lower than the safety lower limit, and the instruction is automatically issued.

[0022] S102: Allocate charging and discharging power for each node based on the grid power demand and the node data of all nodes.

[0023] In this embodiment, the grid power demand refers to the charging and discharging instructions or real-time power interaction requirements sent by the grid to the energy storage power station, including power regulation during peak and valley periods, frequency modulation and peak shaving auxiliary services, demand management, etc. The node data refers to the operation status data of each energy storage site.

[0024] For the grid power demand, in this embodiment, the charging and discharging plans during peak and valley periods and the real-time power instructions for frequency modulation and peak shaving can be obtained from the power dispatching center in real time. Alternatively, in this embodiment, the historical load data, real-time operation data, electricity consumption characteristics of industrial and commercial users, and weather forecasts of the grid can also be integrated, and the power demand for the next 1-7 days can be predicted through a machine learning model. For example, the discharging demand during the peak air-conditioning load surge the next day can be predicted in combination with high-temperature weather.

[0025] This embodiment can analyze the grid demand. For example, the total discharging power required during the peak period is 1000 kW. Combining all node data (such as the sum of the remaining capacities of each site and the health status), tasks are allocated according to the principle of preferentially calling nodes with high capacity and high health status. For example, nodes with a remaining capacity of 80% and a health status of 90% are preferentially allowed to discharge more, and aging nodes are avoided from being overloaded.

[0026] This embodiment needs to consider the real-time power of the nodes and equipment constraints to ensure that the allocated charging and discharging power is within a safe range. For example, if a certain node is currently charging with a charging power of 20 kW and the grid demand switches to discharging, it first stops charging and then gradually increases the discharging power to avoid damage to the equipment caused by frequent switching. At the same time, combined with the real-time electricity price, nodes with low remaining capacity are preferentially charged during the valley period, and nodes with high capacity are preferentially allowed to discharge during the peak period to maximize the peak-valley arbitrage income.

[0027] Exemplarily, assume that a commercial park energy storage system includes 3 sites: Office Building A, Shopping Mall B, and Data Center C. The grid demand is a discharge power of 200 kW during peak hours (10:00 - 15:00) and a charge power of 150 kW during valley hours (23:00 - 7:00). The node data includes: Office Building A: remaining capacity 30%, state of health 90%, charge / discharge power 80 kW; Shopping Mall B: remaining capacity 50%, state of health 85%, charge / discharge power 50 kW; Data Center C: remaining capacity 20%, state of health 70%, charge / discharge power 100 kW.

[0028] The power distribution in this embodiment can be: during peak hours, give priority to increasing the discharge power of Shopping Mall B and reducing the discharge power of Office Building A and Data Center C; during valley hours, distribute the charging tasks according to the proportion of the remaining capacity, give priority to increasing the charging power of Office Building A and Data Center C and reducing the charging power of Shopping Mall B, ensuring that each node operates within the safe power range, and at the same time using low-cost valley electricity for energy storage.

[0029] S103: Determine the nodes that meet the first condition from all nodes, so that each node that meets the first condition performs a charge / discharge power adjustment operation; the nodes that meet the first condition are the nodes whose remaining capacity is less than the first capacity threshold; Among them, the charge / discharge power adjustment operation includes: obtaining the node data of the first adjacent node of this node, determining the power distribution strategy based on the node data of this node and the node data of the first adjacent node; adjusting the charge / discharge power of this node and the charge / discharge power of the first adjacent node based on the power distribution strategy, and the first adjacent node is the adjacent node that directly communicates with this node through the self-organizing network protocol.

[0030] In this embodiment, the first condition refers to the key threshold condition for triggering subsequent power adjustment, which is defined here as the remaining capacity being lower than the preset first capacity threshold. The remaining capacity refers to the current stored electricity of the energy storage site, such as a certain site having 30% remaining. The first capacity threshold is the preset minimum safe capacity, such as 20% or 30%, and the specific value is determined by the battery type and operating requirements. The charge / discharge power adjustment operation refers to the operation of each node that meets the above first condition to adjust and reallocate the power of itself and adjacent nodes. In this embodiment, the nodes that meet the first condition refer to the energy storage sites whose remaining capacity is lower than the preset safety threshold. Such nodes face the risk of over-discharge or cannot respond to subsequent grid demands due to insufficient power and need to be adjusted first. The first adjacent node refers to the neighboring energy storage site that directly communicates with the target node through the self-organizing network protocol. It can be geographically adjacent or adjacent in the network topology. The first adjacent node is the main object of collaborative adjustment. The self-organizing network protocol can be a wireless Mesh network protocol, an IPv6 low-rate wireless personal area network protocol, etc.

[0031] In this embodiment, by collecting the remaining capacity of each node in real time and comparing it with a preset first capacity threshold, the nodes with a remaining capacity lower than the first capacity threshold are screened out. Since these nodes have insufficient power, continued discharging may cause over-discharge (damage to the battery) or inability to respond to subsequent grid demands (such as sudden loads). Therefore, it is necessary to trigger power adjustment first, such as reducing discharging and increasing charging.

[0032] In this embodiment, when the remaining capacity of node A is 15%, which is less than the threshold of 20%, the first condition is triggered. At this time, node A can actively send a data request to the directly communicable adjacent node B through the self-organizing network. After receiving the request, node B encrypts and feeds back its real-time operating data, such as remaining capacity, current power, health status, etc. to node A. The whole process does not require a central server to relay, which can shorten the communication delay.

[0033] Node A can generate a power distribution strategy based on the node data of A and B and in combination with the grid power demand. For example, if A needs to increase charging to improve the remaining capacity, then B needs to reduce charging or increase discharging to release power resources; if A needs to reduce discharging to avoid over-discharge, then B needs to increase discharging to share the load of A to ensure the balance of the total grid power.

[0034] In this embodiment, the adjustment strategy needs to meet the equipment safety and economy. For example, B has a low charging cost during the valley period and preferentially releases low-price electricity. At the same time, it is ensured that the remaining capacity of A rises above the first capacity threshold after adjustment, and the remaining capacity of B is still higher than its own safety lower limit.

[0035] It can be concluded from the above that the blockchain-based distributed data sharing network architecture provided in this embodiment can solve the single-point failure problem of traditional centralized control. Each energy storage site stores and processes data independently in the form of nodes. Even if some nodes fail, the system can still operate through other nodes, greatly improving the reliability. This embodiment supports direct communication between nodes. Each node can obtain the data of adjacent nodes in real time. When the remaining capacity of a node is insufficient, it can quickly link adjacent nodes and dynamically adjust the power distribution strategy according to their operating states to avoid single-point overload or resource idleness, and achieve efficient cooperation between multiple sites. The accurate power adjustment strategy provided in this embodiment can reduce battery loss caused by overcharging and over-discharging, extend the service life of energy storage equipment; at the same time, by allocating charging and discharging power in combination with the grid power demand, it can better participate in grid peak shaving and frequency modulation, and improve energy utilization efficiency.

[0036] In an embodiment of the present application, each node that meets the first condition determines a power distribution strategy based on the node data of the node and the node data of the first adjacent node, including: Each node that meets the first condition determines the current charge-discharge state of the node based on the node data of the node, and determines the current charge-discharge state of the first adjacent node based on the node data of the first adjacent node. If the current charge-discharge state of the node is the charging state and the current charge-discharge state of the first adjacent node is the charging state, then each node that meets the first condition extracts the current charging power and the current remaining capacity of the node from the node data of the node, and extracts the current charging power and the current remaining capacity of the first adjacent node from the node data of the first adjacent node. Each node that meets the first condition determines a power distribution strategy based on the current charging power and the current remaining capacity of the node, and the current charging power and the current remaining capacity of the first adjacent node; the power distribution strategy is used to update the charging power of the node and the charging power of the first adjacent node.

[0037] In this embodiment, determining the power distribution strategy based on the current charging power and the current remaining capacity of the node, and the current charging power and the current remaining capacity of the first adjacent node includes: Calculating the power difference between the current charging power of the node and the current charging power of the first adjacent node, and calculating the capacity difference between the current remaining capacity of the node and the current remaining capacity of the first adjacent node. Determining a power adjustment value based on the power difference and the capacity difference. Generating a power distribution strategy based on the power adjustment value.

[0038] In this embodiment, the charge-discharge state refers to the current operating mode of the energy storage node, which is divided into a charging state (absorbing electric energy from the power grid or renewable energy) or a discharging state (releasing electric energy to the load or the power grid), and is the basic judgment condition for power adjustment. The current charging power refers to the real-time power value of the node in the charging state, which reflects the current rate of absorbing electric energy. The current remaining capacity refers to the current stored electric quantity of the node, which is the core parameter for judging whether adjustment is needed. The power difference refers to the difference between the current charging powers of two nodes, which reflects the imbalance degree of the charging rates of the two nodes. For example, if the charging power of node A is 30 kW and the charging power of node B is 50 kW, the difference is -20 kW. The capacity difference refers to the difference between the current remaining capacities of two nodes, which reflects the imbalance degree of the electric quantity reserves of the two nodes. The power adjustment value refers to the specific value for adjusting the charging powers of the two nodes. For example, A increases by 15 kW and B decreases by 15 kW. The goal is to reduce the differences in power and capacity through adjustment to achieve balanced charging. The power distribution strategy refers to adding the power adjustment value and the initial power of the node as the specific power of the node.

[0039] In this embodiment, when two adjacent nodes are both in the charging state, two core problems of charge balance and power coordination need to be solved. For charge balance, the node with a lower remaining capacity needs to increase its charge faster to avoid being unable to respond to the grid demand due to insufficient charge later; the node with a higher remaining capacity can appropriately reduce the charging speed and release the occupied resources. For power coordination, the total adjusted charging power needs to match the grid demand. For example, the total charging power during the valley period needs to remain stable. Therefore, an increase in the power of one node needs to be balanced by a decrease in the power of the other node to ensure that the total power at the grid connection point remains unchanged.

[0040] Exemplarily, in this embodiment, the current charging power and the remaining capacity are first extracted from the operation data of the node A with a lower remaining capacity and the first adjacent node B with a higher remaining capacity. For example, the current charging power of A is 20 kW and that of B is 50 kW, and the remaining capacity of A is 20% and that of B is 60%. The power difference (20 kW - 50 kW = -30 kW) and the capacity difference (20% - 60% = -40%) are calculated.

[0041] In this embodiment, the power adjustment value is determined based on the power difference and the capacity difference, specifically including: Based on the power difference and the capacity difference, the power adjustment value is determined through the first formula; The first formula is:

[0042] Where, is the power adjustment value, is the capacity adjustment coefficient, is the capacity difference weight, is the power difference weight, + = 1, is the capacity difference, is the power difference, is the preset rated power, and b is the power adjustment coefficient.

[0043] In this embodiment, is used to eliminate the dimension and standardize. If the power adjustment value is positive, it means an increase is needed; if it is negative, it means a decrease is needed; if it is 0, it means no adjustment is needed. The first formula determines the power adjustment direction and amplitude by quantifying the comprehensive influence of the power difference and the capacity difference. When the charging power of the node is lower than that of the adjacent node and the remaining capacity is less, the adjustment value is positive, prompting the node to increase the charging power; conversely, if the charging power of the node is higher than that of the adjacent node and the remaining capacity is less, is 0 and no adjustment is needed. The weight coefficient can be adjusted according to the scenario requirements. For example, when giving priority to ensuring capacity balance, , or increase when preferentially balancing power distribution , so as to realize the dynamic coordination of discharge power and remaining capacity between nodes.

[0044] In this embodiment, by dynamically analyzing the difference between the charging power and remaining capacity of adjacent nodes, the charging strategy can be accurately adjusted. Preferentially guarantee the charging demand of low-capacity nodes to prevent them from being unable to respond to grid dispatching due to insufficient power, improving the overall reliability of the system; limit the overcharging of high-capacity nodes, reduce the overcharging risk, and extend the battery life; through power complementary adjustment, maintain the total power at the grid connection point consistent with the grid demand, and avoid system risks caused by power fluctuations; combine the valley-peak electricity price mechanism to optimize the charging distribution, improve the utilization rate of low-price electricity, and reduce the electricity cost of industrial and commercial users.

[0045] In an embodiment of the present application, after determining the current charge-discharge state of the node based on the node data of the node and determining the current charge-discharge state of the first adjacent node based on the node data of the first adjacent node, it further includes: If the current charge-discharge state of the node is a discharge state and the current charge-discharge state of the first adjacent node is a discharge state, then extract the current discharge power and current remaining capacity of the node from the node data of the node, and extract the current discharge power and current remaining capacity of the first adjacent node from the node data of the first adjacent node; Determine a power distribution strategy based on the current discharge power and current remaining capacity of the node and the current discharge power and current remaining capacity of the first adjacent node; the power distribution strategy is used to update the discharge power of the node and the discharge power of the first adjacent node.

[0046] In this embodiment, determining a power distribution strategy based on the current discharge power and current remaining capacity of the node and the current discharge power and current remaining capacity of the first adjacent node specifically includes: Calculate the power difference between the current discharge power of the node and the current discharge power of the first adjacent node, and calculate the capacity difference between the current remaining capacity of the node and the current remaining capacity of the first adjacent node; Determine a power adjustment value based on the power difference and the capacity difference; Generate a power distribution strategy based on the power adjustment value.

[0047] In this embodiment, the current discharge power refers to the real-time power value of a node in the discharge state, which reflects the rate of electric energy release. When two adjacent nodes are both in the discharge state, this embodiment also needs to solve two problems: capacity balance and power coordination. For capacity balance, if a node with a low remaining capacity continues to discharge at a high power, it may cause over-discharge, so its discharge power needs to be reduced; a node with a high remaining capacity can appropriately increase its discharge power to share the load with its redundant power. For power coordination, the total adjusted discharge power needs to match the grid demand. Therefore, a decrease in the power of one node needs to be compensated by an increase in the power of another node to ensure that the total power at the grid connection point remains unchanged.

[0048] Exemplarily, in this embodiment, the current discharge power and the remaining capacity can be extracted from node C with a low remaining capacity and the first adjacent node D with a high remaining capacity, and the power difference and the capacity difference are calculated. Based on the principle that the lower the capacity, the smaller the discharge power, and combined with the power difference, the adjustment amount is determined through a preset rule. For example, if the capacity of C is 35% less than that of D (the percentage is calculated based on the total capacity), the discharge power of C can be reduced; if the discharge power of C is 15 kW higher than that of D, the discharge power of C can be reduced and the discharge power of D can be increased to obtain the final discharge power value that needs to be reduced. Before adjustment, it is also necessary to verify whether it exceeds the device limit to ensure that the node operates within a safe range after adjustment.

[0049] This embodiment can achieve the balanced control of adjacent discharge nodes through dynamic adjustment in both the capacity and power dimensions. This embodiment preferentially reduces the discharge power of low-capacity nodes to avoid over-discharge damage and improve the reliability of the device; this embodiment uses the redundant power of high-capacity nodes to share the load and reduce resource waste; this embodiment maintains the stability of the total discharge power through power complementarity to meet the high-load demand during peak periods and improve the grid coordination ability; this embodiment reduces the high-load operation time of a single point through balanced discharge, significantly extends the cycle life of the battery pack, and reduces the operation and maintenance cost. This embodiment is particularly applicable to the scenario where multiple sites in industrial and commercial parks discharge simultaneously, such as during the peak production period at noon or the concentrated air-conditioning load period, and is a key technology for the refined scheduling of distributed energy storage systems.

[0050] In an embodiment of the present application, charging and discharging powers are allocated to each node based on the grid power demand and the node data of all nodes, including: Determining the node priority based on the grid power demand and the node data of all nodes within a future target time period; the node priority is the power allocation priority of each node; Allocating charging and discharging powers to each node based on the power allocation priority, and the power allocation priority of a node is positively correlated with the charging and discharging power allocated to this node.

[0051] In this embodiment, the node data includes remaining capacity, health status, and real-time power; determining the node priority based on the grid power demand and the node data of all nodes includes: For each node among all nodes, calculate the power allocation priority of the node based on the grid power demand and the node data of the node through the priority calculation formula; Among them, the priority calculation formula is: ; Among them, is the power allocation priority of the i-th node, , and are weight coefficients, , is the fitness of the remaining capacity of the i-th node to the grid power demand, is the health status of the i-th node, is the normalization coefficient of the health status, is the power adjustment margin of the i-th node under the grid power demand; is calculated based on the remaining capacity of the i-th node and the grid power demand; is calculated based on the real-time power of the i-th node and the grid power demand.

[0052] Exemplarily, = ; Among them, is the remaining capacity of the i-th node, is the grid power demand, and m is the total number of nodes.

[0053] Exemplarily, assume that the discharge power demand of the grid in the next 1 hour is 200 kW, and there are two energy storage nodes.

[0054] The remaining capacity of Node 1 is 50%, the health status is 0.8, and the charge / discharge power is 100 kW; The remaining capacity of Node 2 is 30%, the health status is 0.9, and the charge / discharge power is 130 kW.

[0055] Let the weight coefficients =0.4, =0.3, =0.3, y = 50.

[0056] Calculate the fitness of the remaining capacity of Node 1 =50 / 200×m; Let m be 200, then =50; Calculate the power adjustment margin of Node 1 = ; among which, is the maximum discharge power allowed for the node. Suppose is 160, then = 60; Calculate the remaining capacity fitness of node 2 (2) = 30 / 200 × m = 30; Calculate the power adjustment margin of node 2 (2) = = 30; P(1) = 0.4 × 50 + 0.3 × 0.8 × 50 + 0.3 × 60 = 50; P(2) = 0.4 × 30 + 0.3 × 0.9 × 50 + 0.3 × 30 = 37.5.

[0057] 50 > 37.5, the priority of node 1 is higher than that of node 2. Allocate power according to the priority, and the discharge power of 120 kW can be allocated to node 1 and the discharge power of 80 kW can be allocated to node 2.

[0058] In this embodiment, the method for determining the power demand of the power grid in the future target time period includes: Obtain the power grid historical data, the real-time operation data of the power grid, the electricity consumption characteristics of industrial and commercial users, and the meteorological data in the future target time period; Predict the power demand of the power grid in the future target time period based on the power grid historical data, the real-time operation data of the power grid, the electricity consumption characteristics of industrial and commercial users, and the meteorological data in the future target time period.

[0059] In this embodiment, the power demand of the power grid refers to the charge and discharge power instructions of the power grid for the energy storage power station in the future target time period, including the peak discharge demand and the valley charge demand. The node priority is used to quantify the priority order of the nodes in power allocation. The higher the value, the more priority. It is calculated by weighted calculation of the remaining capacity fitness, the health state, and the power adjustment margin.

[0060] The power grid historical data refers to the operation data of the power grid in the past period of time, which is used to explore the electricity consumption rules and load patterns. The power grid historical data may include the historical load curve, such as the hourly electricity consumption power data in the past 1 - 3 years; the peak-valley period distribution, such as the peak and valley electricity consumption time periods on weekdays / weekends; the historical electricity price data, such as the historical fluctuations of time-of-use electricity prices and ladder electricity prices. By analyzing the power grid historical data, the electricity consumption periodicity can be identified, providing a trend reference for future power demand prediction.

[0061] Grid real-time operation data refers to the current real-time operation status data of the grid, reflecting the immediate supply-demand balance of the grid. Grid real-time operation data can include real-time power (total current power generation and load power of the grid), voltage and frequency, power quality indicators, renewable energy output (real-time power generation of photovoltaic and wind power), and grid connection point status (real-time power flow direction at the connection point between the energy storage power station and the grid). Grid real-time operation data can be used to correct the prediction model in real time. For example, when the grid frequency deviates from 50Hz, the charge and discharge power of the energy storage power station can be quickly adjusted to participate in frequency regulation.

[0062] The electricity consumption characteristics of industrial and commercial users refer to the electricity consumption behavior patterns and load characteristics of industrial and commercial users, which are used to refine the prediction of electricity demand. The electricity consumption characteristics of industrial and commercial users can include industry types, such as the electricity consumption patterns of different industries like manufacturing, commerce, and data centers; production plans, such as the start-stop times of factory equipment and the load fluctuation rules of production lines; special electricity consumption events, such as load changes during holiday overtime and equipment overhauls; historical electricity consumption data, such as the daily / hourly electricity consumption curves of users in the past 12 months; demand response characteristics, such as the sensitivity of users' electricity consumption adjustments to electricity price changes, etc. For example, the concentrated start-stop of production lines of manufacturing users will cause load mutations, and such instantaneous power demands can be predicted in advance through the electricity consumption characteristics of industrial and commercial users.

[0063] Meteorological data within the future target time period refers to the meteorological prediction data for the target prediction period, which directly affects electricity load and renewable energy output. Meteorological data can include temperature and humidity, irradiance, wind speed, precipitation probability, air pressure, and air quality, etc. For example, when predicting a high-temperature weather the next day, the air-conditioning load will increase by 20%, and it is necessary to schedule the energy storage power station to discharge during the peak period in advance to supplement the power supply gap of the grid.

[0064] In this embodiment, this embodiment combines the grid power demand with the node status (remaining capacity, health status, real-time power), and determines the charge and discharge priorities of the nodes through weighted calculation, solving the single-point failure and resource waste problems of traditional centralized control. Among them, nodes with a high remaining capacity are more suitable for responding to discharge demands, nodes with a high health status should give priority to bearing loads to extend the overall life, and nodes with a large power adjustment margin are more likely to flexibly adapt to grid demands. The three achieve multi-objective balance through weight allocation.

[0065] Exemplarily, this embodiment can extract and collect operation data such as historical load and current voltage frequency of the grid in real time through deployed sensors; this embodiment can obtain production plans and demand response data of industrial and commercial users through the user electricity management system; this embodiment can interface with the meteorological platform to obtain prediction data such as temperature and irradiance, and construct a multi-dimensional input data set.

[0066] In this embodiment, machine learning algorithms such as long short-term memory networks and random forests can be used to train a prediction model to identify electricity consumption patterns based on historical data, and combine real-time data and weather forecasts to correct the power demand curve for the next 1-7 days.

[0067] In this embodiment, the battery management system can be used to obtain and analyze the remaining capacity, health status, and real-time power of each node in real time; in this embodiment, the priority of each node is calculated according to a preset weight formula and the obtained data, and the weight can be dynamically adjusted according to the scenario.

[0068] In this embodiment, through blockchain smart contracts or distributed consensus algorithms, power commands can be automatically issued to each node according to the priority, supporting self-organizing communication and collaborative adjustment among nodes, such as low-capacity nodes triggering power compensation from adjacent nodes.

[0069] In this embodiment, the historical load curve, real-time operation data, user electricity consumption characteristics, and meteorological data of the power grid can be integrated, and a long short-term memory network can be used to predict the power demand during the future target period. For example, it is predicted that due to high temperatures during the peak period of the next day, the air-conditioning load will surge, and the power grid requires the energy storage system to discharge 600 kW.

[0070] If the power grid needs to be charged, the lower the remaining capacity of the node, the higher the fitness (priority for charging); if it needs to be discharged, the higher the remaining capacity of the node, the higher the fitness (priority for discharging). The priority of nodes with a low health status is automatically reduced to avoid accelerated aging due to overuse. The power adjustment margin refers to the farther the current power is from the maximum allowable value, the greater the power adjustment margin, and the higher the priority (easier to adjust the power).

[0071] In this embodiment, power can be allocated according to the priority of the nodes from high to low, and the nodes with higher priority obtain more charging and discharging power. For example, when the power grid needs to discharge, nodes with a remaining capacity of 80%, a health status of 90%, and a low current discharge power are given priority to discharge more, ensuring the efficient use of energy storage resources and the safety of equipment.

[0072] This embodiment predicts the power demand of the power grid through multi-source data fusion, calculates the priority by combining the remaining capacity, health status, and power adjustment margin of the nodes, and realizes the dynamic allocation of charging and discharging power. It can improve the accuracy of power allocation and the system response speed, avoid overcharging and over-discharging of equipment, extend the battery life, enhance the grid coordination ability, and optimize the utilization efficiency of energy storage resources.

[0073] Corresponding to the industrial and commercial energy storage power station control method in the above embodiment, Figure 2 This is the structural block diagram of the industrial and commercial energy storage power station control device provided by an embodiment of the present application. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. Refer to Figure 2 In this regard, the industrial and commercial energy storage power station control device 20 includes: a data sharing network construction module 21, a global power distribution module 22, and a local power distribution module 23.

[0074] Among them, the data sharing network construction module 21 is used to construct a blockchain-based distributed data sharing network for the energy storage power station. Among them, each energy storage site in the energy storage power station serves as a node of the distributed data sharing network, and the operation status data of each energy storage site serves as the node data corresponding to the site; The global power distribution module 22 is used to allocate charging and discharging power to each node based on the power demand of the power grid and the node data of all nodes; The local power distribution module 23 is used to determine the nodes that meet the first condition from all nodes. The first condition is that the remaining capacity is less than the first capacity threshold; The charging and discharging power adjustment operation includes: obtaining the node data of the first adjacent node of the node, determining a power distribution strategy based on the node data of the node and the node data of the first adjacent node; adjusting the charging and discharging power of the node and the charging and discharging power of the first adjacent node based on the power distribution strategy. The first adjacent node is an adjacent node that directly communicates with the node through the self-organizing network protocol.

[0075] In an embodiment of the present application, the local power distribution module 23 is specifically used to determine the current charging and discharging state of the node based on the node data of the node, and determine the current charging and discharging state of the first adjacent node based on the node data of the first adjacent node; If the current charging and discharging state of the node is the charging state and the current charging and discharging state of the first adjacent node is the charging state, then extract the current charging power and current remaining capacity of the node from the node data of the node, and extract the current charging power and current remaining capacity of the first adjacent node from the node data of the first adjacent node; Determine a power distribution strategy based on the current charging power and current remaining capacity of the node, and the current charging power and current remaining capacity of the first adjacent node; the power distribution strategy is used to update the charging power of the node and the charging power of the first adjacent node.

[0076] In an embodiment of the present application, the industrial and commercial energy storage power station control device 20 further includes: if the current charging and discharging state of the node is the discharging state and the current charging and discharging state of the first adjacent node is the discharging state, then extract the current discharging power and current remaining capacity of the node from the node data of the node, and extract the current discharging power and current remaining capacity of the first adjacent node from the node data of the first adjacent node; Determine a power distribution strategy based on the current discharging power and current remaining capacity of the node, and the current discharging power and current remaining capacity of the first adjacent node; the power distribution strategy is used to update the discharging power of the node and the discharging power of the first adjacent node.

[0077] In an embodiment of the present application, the local power distribution module 23 is further specifically configured to calculate the power difference between the current charging power of this node and the current charging power of the first adjacent node, and calculate the capacity difference between the current remaining capacity of this node and the current remaining capacity of the first adjacent node; determine a power adjustment value based on the power difference and the capacity difference; generate a power distribution strategy based on the power adjustment value.

[0078] In an embodiment of the present application, the global power distribution module 22 is specifically configured to determine the node priority based on the power demand of the power grid within the future target time period and the node data of all nodes; the node priority is the power distribution priority of each node; allocate charging and discharging power to each node based on the power distribution priority, and the power distribution priority of a node is positively correlated with the charging and discharging power allocated to this node.

[0079] In an embodiment of the present application, the node data includes the remaining capacity, health status, and real-time power; the global power distribution module 22 is further specifically configured to, for each node among all nodes, calculate the power distribution priority of this node based on the power demand of the power grid and the node data of this node through a priority calculation formula; wherein, the priority calculation formula is: ; wherein, is the power distribution priority of the i-th node, 、 and are weight coefficients, , is the fitness of the remaining capacity of the i-th node to the power demand of the power grid, is the health status of the i-th node, is the power adjustment margin of the i-th node under the power demand of the power grid; is calculated based on the remaining capacity of the i-th node and the power demand of the power grid; is calculated based on the real-time power of the i-th node and the power demand of the power grid.

[0080] In an embodiment of the present application, the determination method of the power demand of the power grid within the future target time period includes: obtaining the power grid historical data, the power grid real-time operation data, the electricity consumption characteristics of industrial and commercial users, and the meteorological data within the future target time period; predicting the power demand of the power grid within the future target time period based on the power grid historical data, the power grid real-time operation data, the electricity consumption characteristics of industrial and commercial users, and the meteorological data within the future target time period.

[0081] See Figure 3 , Figure 3Schematic block diagram of an electronic device provided by an embodiment of the present application. As Figure 3 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store computer programs, and the computer programs include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module in the above-mentioned device embodiments, such as Figure 2 shown, the functions of the data sharing network construction module 21, the global power distribution module 22, and the local power distribution module 23.

[0082] It should be understood that in the embodiments of the present application, the so-called processor 301 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0083] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0084] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information of node data.

[0085] In specific implementation, the processors 301, input devices 302, and output devices 303 described in the embodiments of the present application may execute the implementation manners described in the embodiments of the industrial and commercial energy storage power station control method provided by the embodiments of the present application, and may also execute the implementation manner of the electronic device 300 described in the embodiments of the present application, which will not be elaborated here.

[0086] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the methods of the above embodiments are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0087] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0088] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0089] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and modules can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0090] In several embodiments provided by this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling, direct coupling, or communication connection to each other can be an indirect coupling or communication connection through some interfaces or modules, or can also be an electrical, mechanical, or other form of connection.

[0091] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they can be located in one place, or can also be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this application.

[0092] In addition, in each embodiment of this application, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0093] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A control method for an industrial and commercial energy storage power station, characterized in that, Including: Construct a blockchain-based distributed data sharing network for an energy storage power station. Each energy storage site in the energy storage power station serves as a node of the distributed data sharing network, and the operation status data of each energy storage site serves as the node data corresponding to that energy storage site; Allocate charging and discharging power for each node based on the grid power demand and the node data of all nodes; Determine the nodes that meet the first condition from all nodes, so that each node that meets the first condition performs a charging and discharging power adjustment operation; the nodes that meet the first condition are the nodes whose remaining capacity is less than the first capacity threshold; Among them, the charging and discharging power adjustment operation includes: obtaining the node data of the first adjacent node of this node, determining a power allocation strategy based on the node data of this node and the node data of the first adjacent node; adjusting the charging and discharging power of this node and the charging and discharging power of the first adjacent node based on the power allocation strategy, and the first adjacent node is an adjacent node that directly communicates with this node through the self-organizing network protocol.

2. The control method of the industrial and commercial energy storage power station according to claim 1, wherein The allocation of charging and discharging power for each node based on the grid power demand and the node data of all nodes includes: Determine the node priority based on the grid power demand in the future target time period and the node data of all nodes; the node priority is the power allocation priority of each node; Allocate charging and discharging power for each node based on the power allocation priority, and the power allocation priority of a node is positively correlated with the allocated charging and discharging power of that node.

3. The control method of the industrial and commercial energy storage power station according to claim 2, wherein The node data includes remaining capacity, health status, and real-time power; The determination of the node priority based on the grid power demand and the node data of all nodes includes: For each node in all the nodes, calculate the power allocation priority of this node based on the grid power demand and the node data of this node through a priority calculation formula; Among them, the priority calculation formula is: ； Among them, is the power allocation priority of the i-th node, , and are weight coefficients, , is the fitness of the remaining capacity of the i-th node to the power demand of the power grid, is the health status of the i-th node, is the power adjustment margin of the i-th node under the power demand of the power grid; The above-mentioned is calculated based on the remaining capacity of the i-th node and the power demand of the power grid; is calculated based on the real-time power of the i-th node and the power demand of the power grid.

4. The control method of the industrial and commercial energy storage power station according to claim 2, wherein, The determination method of the grid power demand in the future target time period includes: Obtain grid historical data, grid real-time operation data, industrial and commercial user electricity consumption characteristics, and meteorological data in the future target time period; Predict the grid power demand in the future target time period based on the grid historical data, grid real-time operation data, industrial and commercial user electricity consumption characteristics, and meteorological data in the future target time period.

5. The control method of the industrial and commercial energy storage power station according to claim 1, characterized in that, The determination of the power allocation strategy based on the node data of this node and the node data of the first adjacent node includes: Determine the current charging and discharging state of this node based on the node data of this node, and determine the current charging and discharging state of the first adjacent node based on the node data of the first adjacent node; If the current charging and discharging state of this node is the charging state and the current charging and discharging state of the first adjacent node is the charging state, then extract the current charging power and current remaining capacity of this node from the node data of this node, and extract the current charging power and current remaining capacity of the first adjacent node from the node data of the first adjacent node; Determine a power distribution strategy based on the current charging power and current remaining capacity of this node, as well as the current charging power and current remaining capacity of the first adjacent node; the power distribution strategy is used to update the charging power of this node and the charging power of the first adjacent node.

6. The control method of the industrial and commercial energy storage power station according to claim 5, wherein, After determining the current charge and discharge state of this node based on the node data of this node and determining the current charge and discharge state of the first adjacent node based on the node data of the first adjacent node, it further includes: If the current charge and discharge state of this node is a discharge state and the current charge and discharge state of the first adjacent node is a discharge state, then extract the current discharge power and current remaining capacity of this node from the node data of this node, and extract the current discharge power and current remaining capacity of the first adjacent node from the node data of the first adjacent node; Determine a power distribution strategy based on the current discharge power and current remaining capacity of this node, as well as the current discharge power and current remaining capacity of the first adjacent node; the power distribution strategy is used to update the discharge power of this node and the discharge power of the first adjacent node.

7. The control method of the industrial and commercial energy storage power station according to claim 5, characterized in that The determining of the power distribution strategy based on the current charging power and current remaining capacity of this node, as well as the current charging power and current remaining capacity of the first adjacent node, includes: Calculate the power difference between the current charging power of this node and the current charging power of the first adjacent node, and calculate the capacity difference between the current remaining capacity of this node and the current remaining capacity of the first adjacent node; Determine a power adjustment value based on the power difference and the capacity difference; Generate the power distribution strategy based on the power adjustment value.

8. A control device for an industrial and commercial energy storage power station, characterized in that, It includes: A data sharing network construction module, used to construct a blockchain-based distributed data sharing network for an energy storage power station, where each energy storage site in the energy storage power station is used as a node of the distributed data sharing network, and the operation status data of each energy storage site is used as the node data corresponding to this energy storage site; A global power distribution module, used to allocate charge and discharge power to each node based on the power demand of the power grid and the node data of all nodes; A local power distribution module, used to determine the nodes that meet the first condition from all nodes, so that each node that meets the first condition performs a charge and discharge power adjustment operation; the nodes that meet the first condition are the nodes whose remaining capacity is less than the first capacity threshold; Wherein, the charge and discharge power adjustment operation includes: obtaining the node data of the first adjacent node of this node, determining a power distribution strategy based on the node data of this node and the node data of the first adjacent node; adjusting the charge and discharge power of this node and the charge and discharge power of the first adjacent node based on the power distribution strategy, and the first adjacent node is an adjacent node that directly communicates with this node through a self-organizing network protocol.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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