Electrochemical energy storage power plant power distribution method and system

Abstract

The application is suitable for the field of power distribution, and particularly relates to an electrochemical energy storage power station power distribution method and system.The method comprises the following steps: determining the component units of the electrochemical energy storage power station, collecting the configuration data of each component unit, clustering into a plurality of battery clusters, configuring the task types of the electrochemical energy storage power station, setting the demand factors of each task type, wherein the demand factors at least include response speed, start-up time and battery cycle life, creating parent nodes corresponding to the task types, determining the number of each battery cluster by using a preset numbering rule, creating child nodes, and establishing the corresponding relationship between the child nodes and the battery clusters.The application can perform power scheduling based on priority by determining active nodes, effectively guarantees the normal execution of critical tasks, dynamically releases energy storage resources, thereby realizes the automatic distribution of power, and guarantees the persistent and stable operation of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power distribution technology, and in particular to a power distribution method and system for an electrochemical energy storage power station. Background Technology

[0002] An electrochemical energy storage power station is an energy system that uses the principle of electrochemical reaction to store electrical energy in batteries and release it when needed. It typically consists of multiple battery modules that store and release electrical energy through charging and discharging processes, effectively balancing load fluctuations in the power system. Electrochemical energy storage power stations are widely used in energy regulation when renewable energy (such as solar and wind power) is connected to the grid, utilizing peak-valley electricity price differences, emergency power supply, and load balancing. Because of the wide application of electrochemical energy storage power stations, power allocation is extremely important.

[0003] In existing technologies, power scheduling is generally carried out through preset power allocation schemes, but it lacks dynamic adjustment capabilities, which may lead to energy waste or instability of energy storage systems.

[0004] Therefore, "how to dynamically adjust the power of an electrochemical energy storage power station" is the technical problem that this invention aims to solve. Summary of the Invention

[0005] The purpose of this invention is to provide a power allocation method and system for electrochemical energy storage power stations, so as to solve the problem of "how to dynamically adjust the power of electrochemical energy storage power stations" mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A power allocation method for an electrochemical energy storage power station, the method comprising:

[0008] The constituent units of the electrochemical energy storage power station are identified, configuration data of each constituent unit is collected and clustered into several battery clusters, the task types of the electrochemical energy storage power station are configured, and the requirement factors for each task type are set, wherein the requirement factors include at least: response speed, start-up time and battery cycle life.

[0009] Create parent nodes that correspond one-to-one with the task types, determine the number of each battery cluster using a preset numbering rule, create child nodes, and establish the correspondence between child nodes and battery clusters. Based on the demand factors, attach the child nodes to the corresponding parent nodes, integrate the child nodes and parent nodes, and generate a scheduling tree.

[0010] The system acquires real-time tasks from the energy storage power station and integrates them into the scheduling tree. Based on the task type, it triggers the activation of the corresponding parent node. It then determines whether there is a power gap in the parent node. If so, it configures the priority of each real-time task, where the priority includes at least high, medium, and low. From the parent nodes corresponding to the low priority, an active node is selected and migrated sequentially to the parent nodes corresponding to the high priority until power balance is achieved. Then, an active node is reselected to dynamically balance the scheduling tree.

[0011] Furthermore, the step of identifying the constituent units of the electrochemical energy storage power station, collecting configuration data for each constituent unit, and clustering them into several battery clusters includes:

[0012] Using a pre-set sensing device, sensing data of the constituent units are collected, and the sensing data and configuration data are integrated to cluster the constituent units. The sensing data includes at least temperature, voltage and current, and the configuration data includes at least capacity, chemical type and voltage level.

[0013] Extract the commonalities of each battery cluster and insert tags generated from these commonalities into the battery clusters.

[0014] Furthermore, the step of configuring the task type of the electrochemical energy storage power station and setting the demand factors for each task type, wherein the demand factors include at least: response speed, start-up time, and battery cycle life, includes:

[0015] Build a scoring mechanism to determine the score for each battery cluster and different task types;

[0016] Configure a score for each child node, and sort the child nodes in descending order of score.

[0017] Furthermore, the steps of creating a parent node that corresponds one-to-one with the task type, determining the number of each battery cluster using a preset numbering rule, and creating child nodes include:

[0018] Select suitable tasks from the task types to generate a task set;

[0019] Establish a mapping between the task set and the parent node.

[0020] Furthermore, the step of attaching child nodes to their corresponding parent nodes based on the aforementioned demand factors, integrating child nodes and parent nodes, and generating a scheduling tree includes:

[0021] Create a time window, and dynamically adjust the scheduling tree when the time window arrives;

[0022] The system retrieves user-uploaded viewing requests, extracts a status snapshot from the scheduling tree, and sends it to the user terminal.

[0023] Furthermore, the step of acquiring real-time tasks from energy storage power stations and connecting them to the scheduling tree, and triggering the start of the corresponding parent node based on the task type, includes:

[0024] A priority access mechanism is embedded in the scheduling tree, wherein the priority access mechanism is: when a real-time task is detected, all active nodes are used to process the real-time task first;

[0025] Insert timestamps into the scheduling tree and integrate a rollback mechanism.

[0026] Furthermore, the method also includes:

[0027] Locate the deployment location of the edge device and grant the edge device permission to adjust the scheduling tree;

[0028] A prediction model is written into the edge device to identify the changing trend of the real-time task and offset the scheduling tree.

[0029] Furthermore, the system includes:

[0030] The configuration module is used to determine the constituent units of the electrochemical energy storage power station, collect the configuration data of each constituent unit, cluster them into several battery clusters, configure the task type of the electrochemical energy storage power station, and set the requirement factors for each task type, wherein the requirement factors include at least: response speed, start-up time and battery cycle life.

[0031] The generation module is used to create parent nodes that correspond one-to-one with the task type, determine the number of each battery cluster using a preset numbering rule, create child nodes, establish the correspondence between child nodes and battery clusters, attach child nodes to the corresponding parent nodes based on the demand factors, integrate child nodes and parent nodes, and generate a scheduling tree.

[0032] The rebalancing module is used to acquire real-time tasks from the energy storage power station and connect them to the scheduling tree. Based on the task type, it triggers the start of the corresponding parent node, determines whether there is a power gap in the parent node, and if so, configures the priority of each real-time task. The priority includes at least high, medium and low. Among the parent nodes corresponding to low priority, active nodes are selected and migrated to the parent nodes corresponding to high priority in turn until power balance is achieved. Then, active nodes are selected again to dynamically balance the scheduling tree.

[0033] Furthermore, the setting module includes:

[0034] A clustering unit is used to collect sensing data of the constituent units using a preset sensing device, integrate the sensing data and configuration data, and cluster the constituent units. The sensing data includes at least temperature, voltage and current, and the configuration data includes at least capacity, chemical type and voltage level.

[0035] An extraction unit is used to extract the commonalities of each battery cluster and insert tags generated from the commonalities into the battery clusters;

[0036] The building block is used to construct the scoring mechanism and determine the score for each battery cluster and different task types;

[0037] The sorting unit is used to configure the score of each child node and sort the child nodes in descending order of score.

[0038] Furthermore, the generation module includes:

[0039] The selection unit is used to select suitable tasks from the task types and generate a task set;

[0040] A mapping unit is used to establish a mapping between the task set and the parent node;

[0041] An adjustment unit is used to create a time window and dynamically adjust the scheduling tree when the time window arrives.

[0042] The distribution unit is used to obtain the user's uploaded query request, extract the status snapshot from the scheduling tree, and distribute it to the user terminal.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] By clustering the constituent units into several battery clusters, hierarchical control of the constituent units can be achieved, optimizing power allocation accuracy and greatly improving power allocation efficiency. By constructing a scheduling tree, multiple real-time tasks can be processed in parallel, while power can be dynamically adjusted, further improving scheduling efficiency. By determining active nodes, power scheduling can be performed based on priority, effectively ensuring the normal execution of critical tasks and dynamically releasing energy storage resources, thereby realizing automated power allocation and ensuring the long-term stable operation of the power grid. Attached Figure Description

[0045] Figure 1 A flowchart illustrating the power allocation method for an electrochemical energy storage power station provided in an embodiment of the present invention;

[0046] Figure 2 This is a first sub-flowchart of the electrochemical energy storage power station power allocation method provided in an embodiment of the present invention;

[0047] Figure 3 This is a second sub-flowchart of the electrochemical energy storage power station power allocation method provided in an embodiment of the present invention;

[0048] Figure 4 This is a third sub-flow diagram of the power allocation method for an electrochemical energy storage power station provided in an embodiment of the present invention;

[0049] Figure 5 This is a block diagram of the composition of an electrochemical energy storage power station power distribution system provided in an embodiment of the present invention;

[0050] Figure 6 A block diagram showing the composition of modules in an electrochemical energy storage power station power distribution system provided in an embodiment of the present invention;

[0051] Figure 7 A block diagram of the generation module in the electrochemical energy storage power station power distribution system provided in an embodiment of the present invention;

[0052] Figure 8 This is a block diagram of the rebalancing module in the power distribution system of an electrochemical energy storage power station provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] In Example 1, Figure 1 The implementation flow of the power allocation method for an electrochemical energy storage power station provided in an embodiment of the present invention is shown below in detail:

[0055] S100: Determine the constituent units of the electrochemical energy storage power station, collect the configuration data of each constituent unit, cluster them into several battery clusters, configure the task type of the electrochemical energy storage power station, and set the requirement factors for each task type, wherein the requirement factors include at least: response speed, start-up time and battery cycle life.

[0056] Electrochemical energy storage power stations generally consist of equipment such as battery systems, battery management systems, converters, and transformers. The battery cells within the battery system are defined as constituent units. By measuring or querying manufacturer production data, the configuration data of each constituent unit is determined. This configuration data includes: capacity, rated voltage, maximum charge / discharge power, current state of charge, and cycle life. Constituent units with identical or similar configuration data are identified and integrated into several battery clusters with consistent performance and similar operating states. Based on the usage scenarios of the electrochemical energy storage power station, various task types are determined, such as frequency regulation, peak shaving and valley filling, electricity price arbitrage, backup support, grid accident emergency response, and islanded operation assurance. The demand factors for each task type are configured, including power response speed, continuous output duration, energy accuracy, depth of discharge, task execution time window, and priority. Based on these demand factors, battery clusters are intelligently matched and dynamically allocated to ensure that each task meets technical constraints while achieving optimal utilization of energy storage resources.

[0057] S200: Create a parent node that corresponds one-to-one with the task type, determine the number of each battery cluster using a preset numbering rule, create child nodes, establish the correspondence between child nodes and battery clusters, and attach the child nodes to the corresponding parent nodes based on the demand factors. Integrate the child nodes and parent nodes to generate a scheduling tree.

[0058] Create a parent node corresponding to each task type, where the parent node is mainly used to represent the task type; number all battery clusters in the energy storage power station using preset numbering rules (location numbering rules and numerical sequence numbering rules, etc.); create child nodes, where the child nodes are mainly used to represent battery clusters, determine the correspondence between child nodes and battery clusters, and attach suitable child nodes to the corresponding parent nodes according to the requirements of each task type; integrate the parent nodes and child nodes to generate a scheduling tree, where the scheduling tree is similar to a binary tree and is mainly used to find battery clusters suitable for handling different task types; in other words, the scheduling tree not only clearly shows the relationship between tasks and constituent units, but also provides a data foundation for power allocation, priority adjustment, etc.

[0059] S300: Obtain the real-time tasks of the energy storage power station and connect them to the scheduling tree. Based on the task type, trigger the start of the corresponding parent node, determine whether there is a power gap in the parent node, and if so, configure the priority of each real-time task. The priority includes at least high, medium and low. Select the active node from the parent nodes corresponding to the low priority and migrate them to the parent nodes corresponding to the high priority in sequence until the power is balanced. Then, reselect the active node and dynamically balance the scheduling tree.

[0060] The system acquires real-time tasks initiated by the power grid dispatch center, energy management system, or users and integrates these tasks into the dispatch tree. It determines the task type and corresponding parent node for each real-time task, utilizing the child nodes under that parent node to process the task. It checks for power deficits; if present, the total power required by the parent node exceeds the sum of the currently allocated power of its child nodes. It identifies parent nodes currently processing other tasks and determines their priorities (high, medium, and low). Child nodes under low-priority parent nodes are defined as active nodes, and these active nodes are migrated to high-priority parent nodes to balance power and adjust the dispatch tree.

[0061] In Example 2, Figure 2 The implementation flow of the power allocation method for an electrochemical energy storage power station provided in this embodiment of the invention is illustrated. The following details the steps of determining the constituent units of the electrochemical energy storage power station, collecting the configuration data of each constituent unit, and clustering them into several battery clusters:

[0062] S101: Using a preset sensing device, collect sensing data of the constituent units, integrate the sensing data and configuration data, and cluster the constituent units, wherein the sensing data includes at least: temperature, voltage and current, and the configuration data includes at least: capacity, chemical type and voltage level.

[0063] Temperature, voltage, and current sensors are used to monitor the real-time status of each component unit, and the collected data is defined as sensor data. Based on the sensor data and configuration data, the component units are clustered.

[0064] S102: Extract the commonalities of each battery cluster and insert tags generated from the commonalities into the battery clusters.

[0065] Based on the clustering results, common features of each battery cluster are extracted, such as temperature distribution range, voltage stability, current fluctuation characteristics, capacity range, and voltage level. The constituent units within each battery cluster exhibit the same or similar characteristics in the above dimensions. The common dimensions are identified, and labels are generated and inserted into the corresponding battery clusters. The advantage of doing this is that it intuitively displays the commonalities of each battery cluster and improves power distribution efficiency.

[0066] In Example 3, Figure 2 The implementation flow of the power allocation method for an electrochemical energy storage power station provided in this embodiment of the invention is illustrated below. The steps for configuring the task types of the electrochemical energy storage power station and setting the requirement factors for each task type, wherein the requirement factors include at least: response speed, start-up time, and battery cycle life, are described in detail below:

[0067] S103: Construct a scoring mechanism to determine the score for each battery cluster and different task types.

[0068] Based on the configuration data of the battery cluster, such as capacity, rated voltage, maximum charge and discharge power, current state of charge, and cycle life, a weighted scoring model is constructed for each configuration data item according to different task types (such as high power output, long-term energy storage, frequent charge and discharge, and backup power). For example, a multi-index regression model based on machine learning is used to calculate the score of each battery cluster under each task type. The scoring mechanism is as follows: the battery cluster and task type are input into the multi-index regression model, and the score is output.

[0069] S104: Configure the score for each child node and sort the child nodes in descending order of score.

[0070] Within each parent node, sort the child nodes according to their scores from highest to lowest.

[0071] In Example 4, Figure 3 The implementation flow of the power allocation method for an electrochemical energy storage power station provided in this embodiment of the invention is illustrated. The following details the steps of creating a parent node that corresponds one-to-one with the task type, determining the number of each battery cluster using a preset numbering rule, and creating child nodes:

[0072] S201: Select suitable tasks from the task types and generate a task set.

[0073] Once the battery cluster is determined, tasks that highly match the current battery cluster are selected from all candidate task types; these are called adaptation tasks. For example, if a battery cluster scores higher than a set threshold in the "high-power short-time output" task type and scores relatively lower in other task types, then the task type with a score higher than the threshold can be defined as an adaptation task. Each parent node can have one or more adaptation tasks. When there are multiple adaptation tasks, all adaptation tasks are integrated to generate a task set.

[0074] S202: Establish the mapping between the task set and the parent node.

[0075] Each parent node corresponds to an adaptation task or a set of tasks.

[0076] In Example 5, Figure 3 The implementation flow of the power allocation method for an electrochemical energy storage power station provided in this embodiment of the invention is illustrated. The following details the steps of attaching child nodes to corresponding parent nodes based on the demand factors, integrating child nodes and parent nodes, and generating a scheduling tree:

[0077] S203: Create a time window, and dynamically adjust the scheduling tree when the time window arrives.

[0078] Two time points are selected to generate time windows, which are determined by the managers of the electrochemical energy storage power station. When a time window arrives, the correspondence between parent and child nodes is adjusted and the scheduling tree is updated.

[0079] S204: Obtain the user-uploaded query request, extract the status snapshot from the scheduling tree, and send it to the user terminal.

[0080] When a user needs to view the scheduling tree, a viewing request is generated, and status information related to the user's request is extracted from the scheduling tree, including task execution progress, battery cluster running status, scoring results, and the current matching task set, etc. A status snapshot is generated and sent to the user terminal.

[0081] In Example 6, Figure 4 The implementation flow of the power allocation method for an electrochemical energy storage power station provided in this embodiment of the invention is illustrated. The following details the steps of obtaining the real-time tasks of the energy storage power station, accessing them in the scheduling tree, and triggering the start of the corresponding parent node based on the task type:

[0082] S301: Embed a priority access mechanism into the scheduling tree, wherein the priority access mechanism is: when a real-time task is detected, all active nodes are used to process the real-time task first.

[0083] When a new real-time task is received, the active node is used to process the real-time task.

[0084] S302: Insert a timestamp into the scheduling tree and integrate a rollback mechanism.

[0085] A timestamp is inserted into the scheduling tree. After each child node migration is completed, a scheduling log is generated. The scheduling log contains the node status before the change, associated resources, and timestamps. The scheduling log is stored in the historical version chain. When a scheduling anomaly is detected, a user initiates a rollback request, or scheduling reconstruction is required, a specific version node is located based on the timestamp, and the scheduling tree is restored to the state at the specified time through the rollback mechanism.

[0086] In Example 7, unlike Example 1, the method further includes:

[0087] Locate the deployment location of the edge device and grant the edge device permission to adjust the scheduling tree;

[0088] A prediction model is written into the edge device to identify the changing trend of the real-time task and offset the scheduling tree.

[0089] The deployment locations of edge devices are determined, and adjustments such as merging, splitting, and connecting battery clusters are made using edge devices and adjustment permissions. Lightweight prediction models, such as time series models or graph neural network-based prediction models, are written into the edge devices to identify the changing trends of real-time tasks, including dynamic features such as increasing task load, priority shifting, and battery cluster state degradation. Based on the prediction results, the scheduling tree and battery clusters are dynamically adjusted.

[0090] Figure 5 This diagram illustrates the structural block diagram of an electrochemical energy storage power station power distribution system provided in an embodiment of the present invention. The electrochemical energy storage power station power distribution system 1 includes:

[0091] The setting module 11 is used to determine the constituent units of the electrochemical energy storage power station, collect the configuration data of each constituent unit, cluster them into several battery clusters, configure the task type of the electrochemical energy storage power station, and set the demand factors for each task type, wherein the demand factors include at least: response speed, start-up time and battery cycle life.

[0092] The generation module 12 is used to create parent nodes that correspond one-to-one with the task type, determine the number of each battery cluster using a preset numbering rule, create child nodes, establish the correspondence between child nodes and battery clusters, attach child nodes to the corresponding parent nodes based on the demand factors, integrate child nodes and parent nodes, and generate a scheduling tree.

[0093] The rebalancing module 13 is used to acquire real-time tasks of the energy storage power station and connect them to the scheduling tree. Based on the task type, it triggers the start of the corresponding parent node, determines whether there is a power gap in the parent node, and if so, configures the priority of each real-time task. The priority includes at least high, medium and low. Among the parent nodes corresponding to the low priority, the active node is selected and migrated to the parent node corresponding to the high priority in turn until the power is balanced. Then, the active node is reselected to dynamically balance the scheduling tree.

[0094] Figure 6 This diagram illustrates the structural composition of an electrochemical energy storage power station power distribution system provided in an embodiment of the present invention. The setting module 11 includes:

[0095] Clustering unit 111 is used to collect sensing data of constituent units using a preset sensing device, integrate the sensing data and configuration data, and cluster the constituent units, wherein the sensing data includes at least: temperature, voltage and current, and the configuration data includes at least: capacity, chemical type and voltage level.

[0096] Extraction unit 112 is used to extract the commonalities of each battery cluster and insert tags generated from the commonalities into the battery cluster;

[0097] Construction unit 113 is used to construct a scoring mechanism to determine the score of each battery cluster for different task types;

[0098] The sorting unit 114 is used to configure the score of each child node and sort the child nodes in descending order of score.

[0099] Figure 7 This diagram illustrates the structural composition of an electrochemical energy storage power station power distribution system provided in an embodiment of the present invention. The generation module 12 includes:

[0100] The selection unit 121 is used to select suitable tasks from the task types and generate a task set;

[0101] Mapping unit 122 is used to establish the mapping between the task set and the parent node;

[0102] The adjustment unit 123 is used to create a time window and dynamically adjust the scheduling tree when the time window arrives.

[0103] The sending unit 124 is used to obtain the user's uploaded query request, extract the status snapshot from the scheduling tree, and send it to the user terminal.

[0104] Figure 8 This diagram illustrates the structural composition of an electrochemical energy storage power station power distribution system provided in an embodiment of the present invention. The rebalancing module 13 includes:

[0105] Embedding unit 131 is used to embed a priority access mechanism into the scheduling tree, wherein the priority access mechanism is: when a real-time task is detected, all active nodes are used to process the real-time task first.

[0106] Integration unit 132 is used to insert timestamps into the scheduling tree and integrate a rollback mechanism.

[0107] The setting module 11 is mainly used to complete step S100, the generation module 12 is mainly used to complete step S200, and the rebalancing module 13 is mainly used to complete step S300.

[0108] Clustering unit 111 is mainly used to complete step S101, extraction unit 112 is mainly used to complete step S102, construction unit 113 is mainly used to complete step S103, and sorting unit 114 is mainly used to complete step S104.

[0109] The selection unit 121 is mainly used to complete step S201, the mapping unit 122 is mainly used to complete step S202, the adjustment unit 123 is mainly used to complete step S203, and the distribution unit 124 is mainly used to complete step S204.

[0110] The embedded unit 131 is mainly used to complete step S301, and the integrated unit 132 is mainly used to complete step S302.

[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power distribution method for an electrochemical energy storage power station, characterized in that, The method includes: The constituent units of the electrochemical energy storage power station are identified, configuration data of each constituent unit is collected and clustered into several battery clusters, the task types of the electrochemical energy storage power station are configured, and the requirement factors for each task type are set, wherein the requirement factors include at least: response speed, start-up time and battery cycle life. Create parent nodes that correspond one-to-one with the task types, determine the number of each battery cluster using a preset numbering rule, create child nodes, and establish the correspondence between child nodes and battery clusters. Based on the demand factors, attach the child nodes to the corresponding parent nodes, integrate the child nodes and parent nodes, and generate a scheduling tree. The system acquires real-time tasks from the energy storage power station and integrates them into the scheduling tree. Based on the task type, it triggers the activation of the corresponding parent node. It then determines whether there is a power gap in the parent node. If so, it configures the priority of each real-time task, where the priority includes at least high, medium, and low. In the parent nodes corresponding to low priority, an active node is selected and migrated sequentially to the parent nodes corresponding to high priority until power balance is achieved. Then, an active node is reselected to dynamically balance the scheduling tree. The steps of acquiring real-time tasks from energy storage power stations, connecting them to the scheduling tree, and triggering the start of the corresponding parent node based on the task type include: A priority access mechanism is embedded in the scheduling tree, wherein the priority access mechanism is: when a real-time task is detected, all active nodes are used to process the real-time task first; Insert timestamps into the scheduling tree and integrate a rollback mechanism; The method further includes: Locate the deployment location of the edge device and grant the edge device permission to adjust the scheduling tree; A prediction model is written into the edge device to identify the changing trend of the real-time task and offset the scheduling tree.

2. The power distribution method for an electrochemical energy storage power station according to claim 1, characterized in that, The steps of determining the constituent units of the electrochemical energy storage power station, collecting configuration data for each constituent unit, and clustering them into several battery clusters include: Using a pre-set sensing device, sensing data of the constituent units are collected, and the sensing data and configuration data are integrated to cluster the constituent units. The sensing data includes at least temperature, voltage and current, and the configuration data includes at least capacity, chemical type and voltage level. Extract the commonalities of each battery cluster and insert tags generated from these commonalities into the battery clusters.

3. The power distribution method for an electrochemical energy storage power station according to claim 1, characterized in that, The step of configuring the task type of the electrochemical energy storage power station and setting the requirement factors for each task type, wherein the requirement factors include at least: response speed, start-up time, and battery cycle life, includes: Build a scoring mechanism to determine the score for each battery cluster and different task types; Configure a score for each child node, and sort the child nodes in descending order of score.

4. The power distribution method for an electrochemical energy storage power station according to claim 3, characterized in that, The steps of creating a parent node that corresponds one-to-one with the task type, determining the number of each battery cluster using a preset numbering rule, and creating child nodes include: Select suitable tasks from the task types to generate a task set; Establish a mapping between the task set and the parent node.

5. The power distribution method for an electrochemical energy storage power station according to claim 1, characterized in that, The steps of attaching child nodes to their corresponding parent nodes based on the aforementioned demand factors, integrating child nodes and parent nodes, and generating a scheduling tree include: Create a time window, and dynamically adjust the scheduling tree when the time window arrives; The system retrieves user-uploaded viewing requests, extracts a status snapshot from the scheduling tree, and sends it to the user terminal.

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