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

Abstract

The present application provides a control method and device for an industrial and commercial energy storage power station, an electronic device, and a storage medium, which belongs to the technical field of energy storage power stations. The method includes: building a distributed data sharing network based on blockchain for the energy storage power station, using the operating status data of the energy storage site as the node data corresponding to the energy storage site; allocating charge and discharge power to each node based on the power demand of the grid and the node data; determining a node that meets the first condition from all nodes, so that each node that meets the first condition performs a charge and discharge power adjustment operation; the charge and discharge power adjustment operation includes: obtaining the node data of the first adjacent node of the node, determining a power allocation strategy based on the node data of the node and the node data of the first adjacent node; and adjusting the charge and discharge power of the node and the charge and discharge power of the first adjacent node based on the power allocation strategy. The present application can achieve precise control of the operating power of the energy storage power station through a more flexible control method.

Description

Technical Field

[0001] The present application belongs to the technical field of energy storage power stations, and more specifically, relates to control methods and devices, electronic equipment, and storage media for industrial and commercial energy storage power stations. Background Art

[0002] With the rapid growth of industrial and commercial electricity demand and the expansion of renewable energy grid integration, energy storage power stations, as core hubs for energy regulation, play a key role in balancing grid supply and demand and improving renewable energy absorption capacity. In actual operation, industrial and commercial energy storage power stations are composed of multiple distributed energy storage sites, which interact closely with the grid. They must respond to the grid's peak load and frequency regulation needs while also meeting the demands of industrial and commercial users to reduce electricity costs.

[0003] However, traditional commercial and industrial energy storage power stations often utilize a centralized control architecture, where a central controller collects operational data from each energy storage site and uniformly distributes charging and discharging instructions. In this model, power distribution at each energy storage site is highly dependent on central dispatch, which not only presents the risk of single-point failure, but also impacts the stable operation of the energy storage station and the power grid if central dispatch fails. Furthermore, a unified control strategy can easily lead to overloads at some sites due to irrational power distribution, shortening equipment lifespan, wasting resources, and reducing overall energy storage efficiency. Therefore, a more flexible energy storage power station control method is needed to achieve precise control of the station's operating power. 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, electronic equipment, and storage media, so as to achieve precise control of the operating power of energy storage power stations through a more flexible control method.

[0005] A first aspect of an embodiment of the present application provides a method for controlling an industrial and commercial energy storage power station, comprising:

[0006] Constructing a blockchain-based distributed data sharing network for energy storage power stations, wherein each energy storage site in the energy storage power station serves as a node in the distributed data sharing network, and the operating status data of each energy storage site serves as the node data corresponding to the energy storage site;

[0007] Allocate charging and discharging power to each node based on the grid power demand and node data of all nodes;

[0008] Determining a node that satisfies a first condition from all nodes, so that each node that satisfies the first condition performs a charge and discharge power adjustment operation; the node that satisfies the first condition is a node whose remaining capacity is less than a first capacity threshold;

[0009] Among them, the charging and discharging power adjustment operation includes: obtaining the node data of the first adjacent node of the node, and determining the power allocation 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 allocation strategy, where the first adjacent node is an adjacent node that directly communicates with the node through a self-organizing network protocol.

[0010] A second aspect of the embodiments of the present application provides an industrial and commercial energy storage power station control device, comprising:

[0011] A data sharing network construction module is used to build a distributed data sharing network based on blockchain for the energy storage power station, wherein each energy storage site in the energy storage power station serves as a node of the distributed data sharing network, and the operating status data of each energy storage site serves as the node data corresponding to the energy storage site;

[0012] The global power allocation module 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;

[0013] a local power allocation module, configured to determine a node satisfying a first condition from all nodes, so that each node satisfying the first condition performs a charge and discharge power adjustment operation; the node satisfying the first condition is a node whose remaining capacity is less than a first capacity threshold;

[0014] Among them, the charging and discharging power adjustment operation includes: obtaining the node data of the first adjacent node of the node, and determining the power allocation 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 allocation strategy, where the first adjacent node is an adjacent node that directly communicates with the node through a self-organizing network protocol.

[0015] In a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned industrial and commercial energy storage power station control method when executing the computer program.

[0016] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores 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.

[0017] The beneficial effects of the industrial and commercial energy storage power station control method and device, electronic device, and storage medium provided in the embodiments of the present application are as follows: the distributed data sharing network architecture based on blockchain provided in the embodiments of the present application can solve the single point failure problem of traditional centralized control. Each energy storage site independently stores and exchanges data in the form of nodes. Even if some nodes fail, the system can still maintain operation through other nodes, greatly improving reliability. Unlike traditional centralized fixed power control, the embodiments of the present application give energy storage power stations flexible power allocation capabilities through a blockchain distributed data sharing network and a self-organizing communication architecture. The embodiments of the present application support direct communication between nodes. Each node can obtain data from adjacent nodes in real time. When the remaining capacity of a node is insufficient, it quickly links with adjacent nodes and dynamically adjusts the power allocation strategy based on its operating status to avoid single-point overload or idle resources, thereby achieving efficient collaboration among multiple sites. The precise power adjustment strategy provided in the embodiments of the present application can reduce battery loss caused by overcharging and over-discharging, and extend the service life of energy storage equipment. At the same time, by allocating charging and discharging power in combination with the power demand of the power grid, it can better participate in the peak and frequency regulation of the power grid and improve energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A flow chart of a control method for an industrial and commercial energy storage power station provided in one embodiment of the present application;

[0020] Figure 2 A structural block diagram of an industrial and commercial energy storage power station control device provided in one embodiment of the present application;

[0021] Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0022] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may 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 obscuring the description of the present application with unnecessary detail.

[0023] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0024] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for controlling an industrial and commercial energy storage power station according to an embodiment of the present application. The method may be executed by an electronic device. Specifically, the method may include S101 to S103.

[0025] S101: Construct a distributed data sharing network based on blockchain for the energy storage power station, wherein each energy storage site in the energy storage power station serves as a node of the distributed data sharing network, and the operating status data of each energy storage site serves as the node data corresponding to the energy storage site.

[0026] In this embodiment, the energy storage power station refers to an industrial and commercial energy storage power station. An industrial and commercial energy storage power station is an overall system composed of multiple distributed energy storage sites. It undertakes the functions of electric energy storage, regulation, and interaction with the power grid. The goal is to balance user electricity demand and power grid supply. The distributed data sharing network based on blockchain is a decentralized data storage technology that records the operating data of each energy storage site in the form of encrypted blocks in all nodes, ensuring that the data cannot be tampered with, is traceable, and is shared in real time. Using energy storage sites as nodes means that each physically independent energy storage unit is used as a node in the blockchain network. Each node has data storage, communication, and autonomous decision-making capabilities. The operating status data can include basic parameters such as the remaining battery capacity, rated capacity, and charge and discharge power thresholds, real-time status data such as current charge and discharge power, battery temperature, and health status, and environmental data such as real-time electricity prices and grid demand.

[0027] In this embodiment, each energy storage site encrypts and broadcasts real-time operating data to the blockchain network, and all nodes synchronize the updated ledger. For example, when the remaining capacity of a factory energy storage site falls below 20%, its status data can be automatically synchronized to the adjacent commercial building energy storage node without having to pass through a central server.

[0028] Nodes communicate directly through self-organizing network protocols such as the improved P2P protocol, and use lightweight consensus algorithms such as the Byzantine Fault Tolerance algorithm to verify data consistency. For example, when adjacent nodes receive an early warning of insufficient capacity, they quickly respond and negotiate power adjustment strategies, avoiding the communication delays of centralized systems.

[0029] This embodiment can pre-set rules, such as triggering adjacent node collaboration when the remaining capacity is lower than a threshold, and encode the rules into smart contracts. When the node data meets the preset conditions, the contract is automatically activated and generates a power adjustment plan based on the adjacent node data, such as increasing the charging power of the current node and reducing the discharge power of adjacent nodes. The plan is then broadcast to relevant devices through the blockchain for execution.

[0030] For example, assume an industrial park cluster includes three energy storage sites: Factory A, Factory B, and Commercial Building C. Each site uploads its remaining capacity and current power to the blockchain in real time. For example, Factory A's remaining capacity is 15% (percentages are calculated relative to total capacity), Factory B's remaining capacity is 60%, and Commercial Building C's remaining capacity is 40%. Factory A's discharge power is 20kW, Factory B's charging power is 10kW, and Commercial Building C's discharge power is 15kW.

[0031] Factory A's remaining capacity falls below 20%. It sends a request to the neighboring Factory B and Commercial Building C via the self-organizing network. Factory B, currently charging and with sufficient capacity, can reduce its charging power by 10kW and supply power to Factory A. Commercial Building C can increase its discharge power by 5kW to share Factory A's load. After verification of the adjustment, the smart contract ensures that the power of each node remains within the safety limit. If Factory B's charging power does not fall below the safety limit, the smart contract automatically sends the command.

[0032] S102: Allocate charging and discharging power to each node based on the power demand of the power grid and the node data of all nodes.

[0033] In this embodiment, grid power demand refers to the charging and discharging instructions or real-time power interaction requirements issued by the grid to the energy storage power station, including power regulation during peak and valley periods, frequency and peak regulation ancillary services, demand management, etc. Node data refers to the operating status data of each energy storage site.

[0034] Regarding grid power demand, this embodiment can obtain real-time charging and discharging plans for peak and valley periods, as well as real-time power instructions for frequency and peak regulation, from the power dispatch center. Alternatively, this embodiment can integrate historical grid load data, real-time operational data, electricity consumption characteristics of industrial and commercial users, and weather forecasts to use machine learning models to predict power demand for the next 1-7 days. For example, combining high-temperature weather to predict the discharge demand for air conditioning load surges the next day.

[0035] This embodiment can analyze grid demand. For example, if the total discharge power required during peak periods is 1000kW, it can combine all node data (such as the total remaining capacity and health status of each site) to prioritize nodes with high capacity and high health. For example, nodes with 80% remaining capacity and 90% health status can be prioritized for more discharge to avoid overloading severely aged nodes.

[0036] This embodiment takes into account real-time node power and device constraints to ensure that the allocated charging and discharging power remains within a safe range. For example, if a node is currently charging at 20kW and the grid demand shifts to discharging, charging is stopped and then the discharging power is gradually increased to avoid damage to the device caused by frequent switching. Furthermore, based on real-time electricity prices, charging is prioritized during off-peak periods for nodes with low remaining capacity, while discharging is prioritized during peak periods for nodes with high capacity, maximizing peak-valley arbitrage profits.

[0037] For example, assume a commercial park energy storage system consists of three sites: office building A, shopping mall B, and data center C. Grid demand is 200kW of discharge power during peak hours (10:00 AM - 3:00 PM) and 150kW of charging power during off-peak hours (11:00 PM - 7:00 AM). Node data includes: Office Building A: 30% remaining capacity, 90% health, 80kW charge / discharge power; Shopping Mall B: 50% remaining capacity, 85% health, 50kW charge / discharge power; Data Center C: 20% remaining capacity, 70% health, 100kW charge / discharge power.

[0038] The power allocation in this embodiment can be as follows: during peak periods, the discharge power of shopping mall B is prioritized to be increased, while the discharge power of office building A and data center C is reduced; during off-peak periods, charging tasks are allocated according to the remaining capacity ratio, with priority given 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 a safe power range while utilizing low-cost off-peak electricity for energy storage.

[0039] S103: Determine a node that meets a first condition from all nodes, so that each node that meets the first condition performs a charge and discharge power adjustment operation; the node that meets the first condition is a node whose remaining capacity is less than a first capacity threshold;

[0040] Among them, the charging and discharging power adjustment operation includes: obtaining the node data of the first adjacent node of the node, and determining the power allocation 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 allocation strategy, where the first adjacent node is an adjacent node that directly communicates with the node through a self-organizing network protocol.

[0041] In this embodiment, the first condition refers to the key threshold condition that triggers 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 amount of electricity currently stored at the energy storage site, such as 30% remaining at a certain site. The first capacity threshold is a pre-set minimum safety capacity, such as 20% or 30%, and the specific value is determined by the battery type and operating requirements. The charge and discharge power adjustment operation refers to the operation of each node that meets the above-mentioned first condition to adjust and redistribute the power of itself and adjacent nodes. In this embodiment, the node that meets the first condition refers to the energy storage site 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 a neighboring energy storage site that directly communicates with the target node through a self-organizing network protocol. It can be geographically adjacent or network topology adjacent. 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-speed wireless personal area network protocol, etc.

[0042] This embodiment collects the remaining capacity of each node in real time and compares it with a preset first capacity threshold to screen out nodes with remaining capacity below the first capacity threshold. These nodes are low on power. Continued discharge could lead to over-discharge (damage to the battery) or inability to respond to subsequent grid demands (such as sudden loads). Therefore, power adjustments, such as reducing discharge or increasing charging, are prioritized.

[0043] In this embodiment, the first condition is triggered when node A's remaining capacity reaches 15%, which is less than the 20% threshold. At this point, node A can proactively send a data request to a neighboring node B, with which it can directly communicate, via the self-organizing network. Upon receiving the request, node B encrypts and feeds back its real-time operating data, such as remaining capacity, current power, and health status, to node A. This entire process eliminates the need for central server relay, reducing communication latency.

[0044] Node A can generate a power allocation strategy based on the data from nodes A and B, combined with the grid's power requirements, such as the need to maintain a stable total power at the grid connection point. If A needs to increase charging to increase remaining capacity, B needs to reduce charging or increase discharging to free up power resources. If A needs to reduce discharging to avoid overdischarge, B needs to increase discharging to share A's load and ensure total grid power balance.

[0045] In this embodiment, the adjustment strategy must meet the requirements of equipment safety and economy. For example, since B has a low charging cost during off-peak hours, low-priced electricity is released first. At the same time, it is ensured that after adjustment, the remaining capacity of A returns to above the first capacity threshold, and the remaining capacity of B is still higher than its own safety lower limit.

[0046] From the above, it can be concluded that the distributed data sharing network architecture based on blockchain provided by this embodiment can solve the single point failure problem of traditional centralized control. Each energy storage site independently stores and processes data in the form of a node. Even if some nodes fail, the system can still maintain operation through other nodes, greatly improving reliability. This embodiment supports direct communication between nodes. Each node can obtain data from adjacent nodes in real time. When the remaining capacity of a node is insufficient, it quickly links with adjacent nodes and dynamically adjusts the power allocation strategy according to its operating status to avoid single-point overload or idle resources, thereby achieving efficient collaboration among multiple sites. The precise power adjustment strategy provided by this embodiment can reduce battery loss caused by overcharging and over-discharging, and extend the service life of energy storage equipment. At the same time, the charging and discharging power is allocated in combination with the power demand of the power grid, which can better participate in the peak and frequency regulation of the power grid and improve energy utilization efficiency.

[0047] In one embodiment of the present application, each node that meets the first condition determines a power allocation strategy based on node data of the node and node data of a first adjacent node, including:

[0048] Each node that meets the first condition determines a current charge and discharge state of the node based on the node data of the node, and determines a current charge and discharge state of the first adjacent node based on the node data of the first adjacent node;

[0049] If the current charge and discharge state of the node is the charging state and the current charge and 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;

[0050] Each node that meets the first condition determines a power allocation strategy based on the current charging power and current remaining capacity of the node, as well as the current charging power and current remaining capacity of the first adjacent node; the power allocation strategy is used to update the charging power of the node and the charging power of the first adjacent node.

[0051] In this embodiment, the power allocation strategy is determined 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, including:

[0052] 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;

[0053] determining a power adjustment value based on the power difference and the capacity difference;

[0054] A power allocation policy is generated based on the power adjustment value.

[0055] In this embodiment, the charge / discharge state refers to the current operating mode of the energy storage node, which can be categorized as either charging (absorbing energy from the grid or renewable energy) or discharging (releasing energy to the load or the grid). This state serves as the primary criterion for determining power adjustment. The current charging power refers to the real-time power value of the node in the charging state, reflecting the current rate of energy absorption. The current remaining capacity refers to the amount of energy currently stored at the node and is the key parameter for determining whether adjustment is necessary. The power difference refers to the difference in the current charging power between two nodes, reflecting the degree of imbalance in their charging rates. For example, if node A has a charging power of 30 kW and node B has a charging power of 50 kW, the difference is -20 kW. The capacity difference refers to the difference in the current remaining capacity between the two nodes, reflecting the degree of imbalance in their energy reserves. The power adjustment value refers to the specific value by which the charging power of the two nodes needs to be adjusted, such as increasing node A by 15 kW and decreasing node B by 15 kW. The goal is to achieve balanced charging by reducing the power and capacity differences. The power allocation strategy adds the power adjustment value to the node's initial power to determine the node's specific power.

[0056] In this embodiment, when two adjacent nodes are both charging, two core issues must be addressed: power balancing and power coordination. Regarding power balancing, nodes with low remaining capacity need to increase their power more quickly to avoid being unable to respond to grid demand due to insufficient power. Nodes with high remaining capacity can appropriately reduce their charging speed to free up occupied resources. Regarding power coordination, the adjusted total charging power must match grid demand. For example, during off-peak periods, the total charging power must remain stable. Therefore, any power increase at one node must be balanced by a power reduction at another node to ensure that the total power at the grid connection point remains constant.

[0057] Exemplarily, this embodiment first extracts the current charging power and remaining capacity from the operating data of node A with low remaining capacity and the first adjacent node B with high remaining capacity. For example, if the current charging power of A is 20kW and the current charging power of B is 50kW, and if the remaining capacity of A is 20% and the remaining capacity of B is 60%, the power difference (20kW-50kW=-30kW) and the capacity difference (20%-60%=-40%) are calculated.

[0058] In this embodiment, determining the power adjustment value based on the power difference and the capacity difference specifically includes:

[0059] Determining a power adjustment value based on the power difference and the capacity difference using a first formula;

[0060] The first formula is:

[0061]

[0062] in, is the power adjustment value, is the capacity adjustment factor, 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.

[0063] In this embodiment, Used to eliminate dimension and standardize. If the power adjustment value is a positive number, it means that it needs to be increased. If the power adjustment value is a negative number, it means that it needs to be reduced. If the power adjustment value is 0, it means that no adjustment is required. The first formula determines the power adjustment direction and amplitude by quantifying the combined impact of the power difference and the capacity difference. When the charging power of a node is lower than that of the adjacent node and the remaining capacity is less, the adjustment value If is positive, the node is prompted to increase its charging power; on the contrary, if the node's charging power is higher than that of the adjacent nodes and the remaining capacity is less, If it is 0, no adjustment is required. The weight coefficient can be adjusted according to the needs of the scene, for example, it can be increased when ensuring capacity balance first. , or increase when balancing power distribution is preferred , thereby achieving dynamic coordination between the discharge power and the remaining capacity among nodes.

[0064] This embodiment dynamically analyzes the differences between the charging power and remaining capacity of adjacent nodes to precisely adjust the charging strategy. This prioritizes the charging needs of low-capacity nodes, preventing them from being unable to respond to grid dispatch due to insufficient power, thereby improving overall system reliability. It also limits overcharging of high-capacity nodes, reducing the risk of overcharging and extending battery life. Through power complementation adjustments, the total power of the grid connection point is maintained consistent with grid demand, avoiding system risks caused by power fluctuations. Furthermore, it optimizes charging distribution by combining off-peak electricity pricing mechanisms, increasing the utilization rate of low-priced electricity and reducing electricity costs for industrial and commercial users.

[0065] In one embodiment of the present application, after determining the current charge and discharge state of the node based on the node data of the node and determining the current charge and discharge state of the first adjacent node based on the node data of the first adjacent node, the method further includes:

[0066] If the current charge and discharge state of the node is the discharge state, and the current charge and discharge state of the first adjacent node is the 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;

[0067] A power allocation strategy is determined 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 allocation strategy is used to update the discharge power of the node and the discharge power of the first adjacent node.

[0068] In this embodiment, the power allocation strategy is determined 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 neighboring node, specifically including:

[0069] Calculating a power difference between a current discharge power of the node and a current discharge power of a first adjacent node, and calculating a capacity difference between a current remaining capacity of the node and a current remaining capacity of the first adjacent node;

[0070] determining a power adjustment value based on the power difference and the capacity difference;

[0071] A power allocation policy is generated based on the power adjustment value.

[0072] In this embodiment, the current discharge power refers to the real-time power value of the node in the discharge state, reflecting the rate of electric energy release. When two adjacent nodes are in the discharge state, this embodiment also needs to solve the two problems of capacity balance and power coordination. With regard to capacity balance, if the node with low remaining capacity continues to discharge at high power, it may cause over-discharge, and its discharge power needs to be reduced; the node with high remaining capacity can appropriately increase the discharge power and use its redundant power to share the load. With regard to power coordination, the adjusted total discharge power needs to match the grid demand, so the power reduction of one node needs to be compensated by the power increase of another node to ensure that the total power of the grid connection point remains unchanged.

[0073] Exemplarily, this embodiment can extract the current discharge power and remaining capacity from node C with low remaining capacity and the first adjacent node D with high remaining capacity, and calculate the power difference and capacity difference. Based on the principle that the lower the capacity, the lower the discharge power, combined with the power difference, the adjustment amount is determined by preset rules. 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 15kW higher than that of D, the discharge power of C can be reduced and the discharge power of D can be increased, and the final discharge power value that needs to be reduced can be obtained comprehensively. Before adjustment, it is also necessary to verify whether the equipment limit is exceeded to ensure that the adjusted node operates within a safe range.

[0074] This embodiment can achieve balanced control of adjacent discharge nodes through dynamic adjustment of capacity and power. This embodiment gives priority to reducing the discharge power of low-capacity nodes to avoid over-discharge damage and improve equipment reliability; 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, meets peak high-load demands, and improves the grid coordination capability; 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 operation and maintenance costs. This embodiment is particularly suitable for scenarios where multiple sites in industrial and commercial parks discharge simultaneously, such as the midday production peak or the concentrated air-conditioning load period, and is a key technology for refined scheduling of distributed energy storage systems.

[0075] In one embodiment of the present application, allocating charging and discharging power to each node based on grid power demand and node data of all nodes includes:

[0076] Determine the node priority based on the grid power demand and node data of all nodes in the future target time period; the node priority is the power allocation priority of each node;

[0077] The charging and discharging power is allocated to each node based on the power allocation priority. The power allocation priority of a node is positively correlated with the charging and discharging power allocated to the node.

[0078] In this embodiment, the node data includes remaining capacity, health status, and real-time power. The node priority is determined based on the grid power demand and the node data of all nodes, including:

[0079] For each of all nodes, the power allocation priority of the node is calculated based on the power demand of the power grid and the node data of the node and using a priority calculation formula;

[0080] The priority calculation formula is:

[0081] ;

[0082] in, Assign the power priority to the i-th node, 、 and is the weight coefficient, , is the adaptability of the remaining capacity of the i-th node to the power demand of the grid, is the health status of the i-th node, is the standardized coefficient of health status, is the power adjustment margin of the i-th node under the power demand of the grid;

[0083] is calculated based on the remaining capacity of the i-th node and the power demand of the grid; is calculated based on the real-time power of the i-th node and the power demand of the grid.

[0084] For example, = ;in, is the remaining capacity of the i-th node, is the power demand of the grid, and m is the total number of nodes.

[0085] For example, assume that the grid's discharge power demand in the next hour is 200 kW, and there are two energy storage nodes.

[0086] Node 1 has a remaining capacity of 50%, a health status of 0.8, and a charge / discharge power of 100 kW.

[0087] Node 2 has a remaining capacity of 30%, a health status of 0.9, and a charge / discharge power of 130 kW.

[0088] Set the weight coefficient =0.4, =0.3, =0.3, y=50.

[0089] Calculate the remaining capacity fitness of node 1 =50 / 200×m; let m be 200, then =50;

[0090] Calculate the power adjustment margin of node 1 = ;in, is the maximum discharge power allowed by the node. is 160, then =60;

[0091] Calculate the remaining capacity fitness of node 2 (2)=30 / 200×m=30;

[0092] Calculate the power adjustment margin of node 2 (2)= =30;

[0093] P(1)=0.4×50+0.3×0.8×50+0.3×60=50;

[0094] P(2)=0.4×30+0.3×0.9×50+0.3×30=37.5.

[0095] 50>37.5, the priority of node 1 is higher than that of node 2. By allocating power based on priority, node 1 can be allocated a discharge power of 120 kW, and node 2 can be allocated a discharge power of 80 kW.

[0096] In this embodiment, the method for determining the power demand of the power grid in the future target time period includes:

[0097] Obtain historical power grid data, real-time power grid operation data, industrial and commercial user electricity consumption characteristics, and meteorological data within the future target time period;

[0098] The power demand of the power grid in the future target time period is predicted based on the historical data of the power grid, 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.

[0099] In this embodiment, grid power demand refers to the charging and discharging power instructions the grid issues to the energy storage power station during a future target time period, including peak-period discharge demand and valley-period charging demand. Node priority quantifies the priority of a node in power allocation, with higher values ​​indicating higher priority. This value is calculated based on a weighted combination of remaining capacity adaptability, health status, and power adjustment margin.

[0100] Historical grid data refers to grid operation data from the past period, used to identify patterns in electricity usage and load patterns. This data can include historical load curves, such as hourly power consumption data from the past 1-3 years; peak and valley period distribution, such as peak and valley periods on weekdays and weekends; and historical electricity price data, such as historical fluctuations in time-of-use and tiered electricity prices. Analyzing historical grid data can identify cyclical patterns in electricity usage and provide trend references for future power demand forecasts.

[0101] Real-time grid operation data refers to the current real-time operating status of the grid, reflecting the immediate supply and demand balance. This data can include real-time power (the grid's current total generated power and load power), voltage and frequency, power quality indicators, renewable energy output (real-time generated power of photovoltaic and wind power), and grid connection point status (real-time power flow between the energy storage power station and the grid connection point). This real-time grid operation data can be used to refine prediction models in real time. For example, when the grid frequency deviates from 50Hz, the charging and discharging power of the energy storage power station can be rapidly adjusted to participate in frequency regulation.

[0102] Industrial and commercial user electricity usage characteristics refer to their electricity usage patterns and load characteristics, which are used to refine electricity demand forecasts. These characteristics can include industry type, such as electricity usage patterns in manufacturing, commerce, data centers, and other industries; production schedules, such as factory equipment start and stop times and load fluctuation patterns in production lines; special electricity usage events, such as load changes during holiday overtime and equipment overhauls; historical electricity usage data, such as daily and hourly electricity usage curves for the past 12 months; and demand response characteristics, such as the sensitivity of users to electricity price fluctuations. For example, the concentrated start and stop of production lines in manufacturing users can cause sudden load changes. Using these characteristics, we can predict these instantaneous power demands in advance.

[0103] Meteorological data for the target future time period refers to the forecasted weather data for the target forecast period, which directly impacts electricity load and renewable energy output. This data can include temperature and humidity, irradiance, wind speed, precipitation probability, air pressure, and air quality. For example, if high temperatures are predicted for the next day, air conditioning load will increase by 20%, necessitating the pre-emptive dispatch of energy storage power stations to discharge during peak periods to supplement grid power supply gaps.

[0104] This embodiment combines grid power demand with node status (remaining capacity, health status, and real-time power) to determine node charging and discharging priorities through weighted calculation, addressing the single point of failure and resource waste inherent in traditional centralized control. Nodes with high remaining capacity are more suitable for responding to discharge demands, nodes with high health status should be prioritized to carry loads to extend their overall lifespan, and nodes with large power adjustment margins are more flexible in adapting to grid demand. These three objectives are balanced through weighted allocation.

[0105] For example, this embodiment can collect real-time operating data such as the historical load of the power grid, current voltage and frequency by extracting 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 connect to the meteorological platform to obtain forecast data such as temperature and irradiance, and construct a multi-dimensional input data set.

[0106] This embodiment can use machine learning algorithms such as long short-term memory networks and random forests to train the prediction model, identify power consumption patterns based on historical data, and combine real-time data with weather forecasts to correct the power demand curve for the next 1-7 days.

[0107] This embodiment can obtain and analyze the remaining capacity, health status, and real-time power of each node in real time through the battery management system; this embodiment calculates the priority of each node according to a preset weight formula and the obtained data, and the weight can be dynamically adjusted according to the scenario.

[0108] This embodiment can automatically issue power instructions to each node according to priority through blockchain smart contracts or distributed consensus algorithms, and support self-organizing communication and collaborative adjustment between nodes, such as low-capacity nodes triggering power compensation of adjacent nodes.

[0109] In this embodiment, the power grid's historical load curves, real-time operating data, user electricity consumption characteristics, and meteorological data can be integrated to predict power demand during future target periods using a long-short-term memory network. For example, if the grid is expected to require a 600kW discharge from the energy storage system during the next day's peak period due to high temperatures, resulting in a surge in air conditioning load, the power grid may need to discharge 600kW.

[0110] If the grid needs to charge, nodes with lower remaining capacity have higher fitness (charging priority); if discharging is required, nodes with higher remaining capacity have higher fitness (discharging priority). Nodes with low health status are automatically prioritized to prevent overuse and accelerated aging. The power adjustment margin indicates that the further the current power is from the maximum allowable value, the greater the power adjustment margin and the higher the priority (easier to adjust power).

[0111] This embodiment allocates power based on node priority, from high to low. Nodes with higher priorities receive more charging and discharging power. For example, when the grid needs to discharge, nodes with 80% remaining capacity, 90% health status, and low current discharge power are prioritized for more discharge, ensuring efficient use of energy storage resources and equipment safety.

[0112] This embodiment predicts grid power demand through multi-source data fusion, combines node remaining capacity, health status, and power adjustment margin to calculate priority, and dynamically allocates charging and discharging power. This improves power allocation accuracy and system response speed, prevents device overcharging and over-discharging, extends battery life, enhances grid coordination, and optimizes energy storage resource utilization.

[0113] Corresponding to the control method of the industrial and commercial energy storage power station in the above embodiment, Figure 2 This is a structural block diagram of an industrial and commercial energy storage power station control device provided by an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 2 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.

[0114] The data sharing network construction module 21 is used to construct a distributed data sharing network based on blockchain for the energy storage power station, wherein each energy storage site in the energy storage power station serves as a node in the distributed data sharing network, and the operating status data of each energy storage site serves as the node data corresponding to the energy storage site;

[0115] A global power allocation 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;

[0116] The local power allocation module 23 is configured to determine a node that satisfies a first condition from all nodes, where the first condition is that the remaining capacity is less than a first capacity threshold;

[0117] The charging and discharging power adjustment operation includes: obtaining node data of the first adjacent node of the node, determining a power allocation 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 allocation strategy, where the first adjacent node is an adjacent node that directly communicates with the node through a self-organizing network protocol.

[0118] In one embodiment of the present application, the local power allocation module 23 is specifically configured to determine the current charge and discharge state of the node based on the node data of the node, and determine the current charge and discharge state of the first adjacent node based on the node data of the first adjacent node;

[0119] If the current charge and discharge state of the node is the charging state and the current charge and discharge state of the first adjacent node is the charging state, extracting the current charging power and the current remaining capacity of the node from the node data of the node, and extracting the current charging power and the current remaining capacity of the first adjacent node from the node data of the first adjacent node;

[0120] A power allocation strategy is determined 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 allocation strategy is used to update the charging power of the node and the charging power of the first adjacent node.

[0121] In one embodiment of the present application, the industrial and commercial energy storage power station control device 20 further includes: if the current charge and discharge state of the node is a discharge state, and the current charge and discharge state of the first adjacent node is a discharge state, extracting the current discharge power and the current remaining capacity of the node from the node data of the node, and extracting the current discharge power and the current remaining capacity of the first adjacent node from the node data of the first adjacent node;

[0122] A power allocation strategy is determined 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 allocation strategy is used to update the discharge power of the node and the discharge power of the first adjacent node.

[0123] In one embodiment of the present application, the local power allocation module 23 is further used to calculate the power difference between the current charging power of the node and the current charging power of the first adjacent node, calculate the capacity difference between the current remaining capacity of the node and the current remaining capacity of the first adjacent node; determine the power adjustment value based on the power difference and the capacity difference; and generate a power allocation strategy based on the power adjustment value.

[0124] In one embodiment of the present application, the global power allocation module 22 is specifically used to 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; based on the power allocation priority, the charging and discharging power is allocated to each node, and the power allocation priority of a node is positively correlated with the charging and discharging power allocated to the node.

[0125] In one embodiment of the present application, the node data includes remaining capacity, health status, and real-time power; the global power allocation module 22 is further configured to calculate the power allocation priority of each node among all nodes based on the power demand of the power grid and the node data of the node and using a priority calculation formula;

[0126] The priority calculation formula is:

[0127] ;

[0128] in, Assign the power priority to the i-th node, 、 and is the weight coefficient, , is the adaptability of the remaining capacity of the i-th node to the power demand of the 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 grid;

[0129] is calculated based on the remaining capacity of the i-th node and the power demand of the grid; is calculated based on the real-time power of the i-th node and the power demand of the grid.

[0130] In one embodiment of the present application, a method for determining the power demand of the power grid within a future target time period includes: obtaining historical power grid data, real-time power grid operation data, electricity consumption characteristics of industrial and commercial users, and meteorological data within the future target time period; and predicting the power demand of the power grid within the future target time period based on the historical power grid data, real-time power grid operation data, electricity consumption characteristics of industrial and commercial users, and meteorological data within the future target time period.

[0131] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 3 The electronic device 300 in the embodiment shown 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 processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 2 The functions of the data sharing network construction module 21, the global power allocation module 22 and the local power allocation module 23 are shown.

[0132] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0133] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0134] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store node data information.

[0135] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present application can execute the implementation method described in the embodiment of the industrial and commercial energy storage power station control method provided in the embodiments of the present application, and can also execute the implementation method of the electronic device 300 described in the embodiments of the present application, which will not be repeated here.

[0136] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0137] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as the electronic device's hard drive or memory. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0138] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0139] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0140] In the several embodiments provided in 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 may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or modules, or can be an electrical, mechanical or other form of connection.

[0141] Modules described as separate components may or may not be physically separate, and components displayed as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0142] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0143] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A control method for an industrial and commercial energy storage power station, characterized in that: include: Constructing a blockchain-based distributed data sharing network for energy storage power stations, wherein each energy storage site in the energy storage power station serves as a node in the distributed data sharing network, and the operating status data of each energy storage site serves as the node data corresponding to the energy storage site; Allocate charging and discharging power to each node based on the grid power demand and node data of all nodes; Determining a node that satisfies a first condition from all nodes, so that each node that satisfies the first condition performs a charge and discharge power adjustment operation; the node that satisfies the first condition is a node whose remaining capacity is less than a first capacity threshold; The charge and discharge power adjustment operation includes: obtaining node data of a first neighboring node of the node, determining a power allocation strategy based on the node data of the node and the node data of the first neighboring node; and adjusting the charge and discharge power of the node and the charge and discharge power of the first neighboring node based on the power allocation strategy, where the first neighboring node is a neighboring node that directly communicates with the node through a self-organizing network protocol. The allocating charging and discharging power to each node based on the power demand of the power grid and the node data of all nodes includes: determining a node priority based on the power demand of the power grid in a future target time period and the node data of all nodes; the node priority is the power allocation priority of each node; and allocating charging and discharging power to each node based on the power allocation priority, wherein the power allocation priority of a node is positively correlated with the charging and discharging power allocated to the node; The node data includes remaining capacity, health status, and real-time power; determining the node priority based on the grid power demand in the future target time period and the node data of all nodes includes: for each of all the nodes, calculating the power allocation priority of the node based on the grid power demand and the node data of the node and using a priority calculation formula; The priority calculation formula is: ； in, Assign the power priority to the i-th node, 、 and is the weight coefficient, , is the adaptability of the remaining capacity of the i-th node to the power demand of the 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 grid; described is calculated based on the remaining capacity of the i-th node and the power demand of the grid; is calculated based on the real-time power of the i-th node and the power demand of the grid.

2. The industrial and commercial energy storage power station control method according to claim 1, characterized in that: The method for determining the power demand of the power grid within the future target time period includes: Obtain historical power grid data, real-time power grid operation data, industrial and commercial user electricity consumption characteristics, and meteorological data within the future target time period; The power demand of the power grid in the future target time period is predicted based on the historical data of the power grid, 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.

3. The industrial and commercial energy storage power station control method according to claim 1, characterized in that: The determining of the power allocation strategy based on the node data of the node and the node data of the first adjacent node includes: Determining a current charge and discharge state of the node based on the node data of the node, and determining a current charge and discharge state of the first adjacent node based on the node data of the first adjacent node; If the current charge and discharge state of the node is a charging state and the current charge and discharge state of the first neighboring node is a charging state, extracting the current charging power and the current remaining capacity of the node from the node data of the node, and extracting the current charging power and the current remaining capacity of the first neighboring node from the node data of the first neighboring node; A power allocation strategy is determined 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 allocation strategy is used to update the charging power of the node and the charging power of the first adjacent node.

4. The industrial and commercial energy storage power station control method according to claim 3, characterized in that: After determining the current charge and discharge state of the node based on the node data of the node and determining the current charge and discharge state of the first adjacent node based on the node data of the first adjacent node, the method further includes: If the current charge and discharge state of the node is the discharge state, and the current charge and discharge state of the first neighboring node is the discharge state, extracting the current discharge power and the current remaining capacity of the node from the node data of the node, and extracting the current discharge power and the current remaining capacity of the first neighboring node from the node data of the first neighboring node; A power allocation strategy is determined 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 allocation strategy is used to update the discharge power of the node and the discharge power of the first adjacent node.

5. The industrial and commercial energy storage power station control method according to claim 3, characterized in that: The determining of the power allocation 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 a power difference between the current charging power of the node and the current charging power of the first neighboring node, and calculating a capacity difference between the current remaining capacity of the node and the current remaining capacity of the first neighboring node; determining a power adjustment value based on the power difference and the capacity difference; The power allocation policy is generated based on the power adjustment value.

6. An industrial and commercial energy storage power station control device, characterized in that: include: A data sharing network construction module is used to build a distributed data sharing network based on blockchain for the energy storage power station, wherein each energy storage site in the energy storage power station serves as a node of the distributed data sharing network, and the operating status data of each energy storage site serves as the node data corresponding to the energy storage site; The global power allocation module 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; A global power allocation module is specifically configured to determine node priorities based on grid power demand and node data of all nodes within a future target time period; the node priorities are power allocation priorities for each node; charging and discharging power is allocated to each node based on the power allocation priorities, with a node's power allocation priority being positively correlated with the charging and discharging power allocated to the node; the node data includes remaining capacity, health status, and real-time power; The global power allocation module is further configured to calculate, for each of the nodes, a power allocation priority of the node based on the power demand of the power grid and the node data of the node and using a priority calculation formula; The priority calculation formula is: ； in, Assign the power priority to the i-th node, 、 and is the weight coefficient, , is the adaptability of the remaining capacity of the i-th node to the power demand of the 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 grid; described is calculated based on the remaining capacity of the i-th node and the power demand of the grid; is calculated based on the real-time power of the i-th node and the power demand of the grid; a local power allocation module, configured to determine a node satisfying a first condition from all nodes, so that each node satisfying the first condition performs a charge and discharge power adjustment operation; the node satisfying the first condition is a node whose remaining capacity is less than a first capacity threshold; Among them, the charging and discharging power adjustment operation includes: obtaining the node data of the first adjacent node of the node, and determining the power allocation 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 allocation strategy, where the first adjacent node is an adjacent node that directly communicates with the node through a self-organizing network protocol.

7. 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, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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