A multi-node collaborative control system and method for industrial and commercial energy storage power stations

Abstract

This application provides a multi-node collaborative control system and method for commercial and industrial energy storage power stations, relating to the field of collaborative control technology. The method involves initializing each energy storage node unit in the target commercial and industrial energy storage power station and collecting capacity parameters. An operational stability coefficient is determined using historical operating data, and a capacity readiness level is determined based on the capacity parameters. Node election responses are performed on the energy storage node units based on the operational stability coefficient and capacity readiness level to obtain an election response degree. The responses of each energy storage node unit are adjusted based on the election response degree and dynamic adjustment parameters to obtain a calibration election index. A dominant node unit and various cooperating node units are determined based on these calibration election indices. The target commercial and industrial energy storage power station is then collaboratively controlled through the dominant node unit and the various cooperating node units. This application can avoid system risks caused by single node failures or performance bottlenecks, and improves the power response accuracy and operational stability of commercial and industrial energy storage power stations.

Description

Technical Field

[0001] This application relates to the field of collaborative control technology, and more specifically, to a multi-node collaborative control system and method for industrial and commercial energy storage power stations. Background Technology

[0002] Commercial and industrial energy storage power stations typically consist of multiple energy storage node units, each including a battery module, a bidirectional converter, and a monitoring and management unit. Existing energy storage power stations mostly employ centralized or distributed coordinated control methods to achieve unified scheduling and power allocation of the energy storage system. In centralized control, the power station collects operational data from each energy storage node through a host computer or energy management system (EMS) and issues charging and discharging power commands to each node according to a predetermined scheduling strategy to respond to the external power grid. In distributed coordinated control, each energy storage node possesses a certain degree of autonomy, enabling it to monitor its operational status and perform preliminary adjustments locally, while also interacting with other nodes through a communication network to achieve collaborative operation between nodes.

[0003] Existing industrial and commercial energy storage power stations primarily rely on centralized or distributed control strategies. While these strategies can achieve basic power distribution and grid response, the overall response remains limited during multi-node collaborative operation due to differences in node performance or single-node failures. Furthermore, node selection and power distribution lack adaptability under dynamic operating conditions, making it difficult to balance system stability and economy. Therefore, avoiding system risks caused by single-node failures or performance bottlenecks, and improving the power response accuracy and operational stability of industrial and commercial energy storage power stations, has become a significant challenge for the industry. Summary of the Invention

[0004] This application provides a multi-node collaborative control system and method for industrial and commercial energy storage power stations, which can avoid system risks caused by single node failures or performance bottlenecks, and improve the power response accuracy and operational stability of industrial and commercial energy storage power stations.

[0005] In a first aspect, this application provides a multi-node collaborative control method for industrial and commercial energy storage power stations, the collaborative control method comprising the following steps:

[0006] Initialize each energy storage node unit in the target industrial and commercial energy storage power station and collect the capacity parameters of each energy storage node unit;

[0007] Historical operating data of each energy storage node unit is acquired, and the operating stability coefficient of each energy storage node unit is determined through the historical operating data. The capability readiness of each energy storage node unit is determined based on the capability parameters of each energy storage node unit. Then, node election response is performed on each energy storage node unit based on all operating stability coefficients and all capability readiness, so as to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station.

[0008] Initialize dynamic adjustment parameters, adjust the response of each energy storage node unit according to each election response degree and the dynamic adjustment parameters, obtain the calibration election index of each energy storage node unit, and then determine the dominant node unit and each cooperative node unit in the target industrial and commercial energy storage power station based on each calibration election index.

[0009] The target industrial and commercial energy storage power station is controlled collaboratively by the dominant node unit and each cooperating node unit.

[0010] In this embodiment, the capability parameters of each energy storage node unit are collected by extracting the capability parameters of each energy storage node unit through the battery management system embedded in the target industrial and commercial energy storage power station.

[0011] In this embodiment, determining the operational stability coefficient of each energy storage node unit using the historical operational data specifically includes:

[0012] For each energy storage node unit, the node availability time, node failure frequency, and cumulative failure time of the energy storage node unit are extracted from the historical operation data.

[0013] The fault frequency penalty factor is determined based on the node fault frequency of the corresponding energy storage node unit.

[0014] The node availability rate of the energy storage node unit is determined by the node availability duration and the cumulative fault duration.

[0015] The operational stability coefficient of the corresponding energy storage node unit is determined by the fault frequency penalty factor and the node availability, thereby obtaining the operational stability coefficient of each energy storage node unit.

[0016] In this embodiment, determining the capability readiness of each energy storage node unit based on its capability parameters specifically includes:

[0017] Obtain the current pre-execution strategy of the target industrial and commercial energy storage power station;

[0018] For each energy storage node unit, the capability weight of each capability parameter of the energy storage node unit is determined according to the pre-execution strategy;

[0019] The capability readiness of each energy storage node unit is determined based on its various capability parameters and corresponding capability weights, thereby obtaining the capability readiness of each energy storage node unit.

[0020] In this embodiment, the node election response of each energy storage node unit is performed based on all operational stability coefficients and all capability readiness, and the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station specifically includes:

[0021] Each energy storage node unit is screened by a preset health gating mask to obtain each candidate node in the target industrial and commercial energy storage power station;

[0022] For each candidate node in the target industrial and commercial energy storage power station, the election response degree of the corresponding candidate node is determined by the operational stability coefficient and capability readiness degree of the candidate node, thereby obtaining the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station.

[0023] In this embodiment, the response adjustment of each energy storage node unit based on each election response degree and the dynamic adjustment parameters to obtain the calibration election index of each energy storage node unit specifically includes:

[0024] The election adjustment coefficient of each energy storage node unit is determined by the dynamic adjustment parameters and the operational stability coefficient of each energy storage node unit.

[0025] Each election response is adjusted according to the election adjustment coefficient to obtain the calibration election index of each energy storage node unit.

[0026] In this embodiment, the coordinated control of the target industrial and commercial energy storage power station through the dominant node unit and various cooperating node units specifically includes:

[0027] The dominant node unit receives power commands from the target industrial and commercial energy storage power station and generates a power allocation strategy.

[0028] Power is allocated to each cooperating node unit according to the power allocation strategy.

[0029] Secondly, this application provides a multi-node collaborative control system for industrial and commercial energy storage power stations to execute a multi-node collaborative control method for industrial and commercial energy storage power stations, the multi-node collaborative control system comprising:

[0030] The energy storage node module is used to initialize each energy storage node unit in the target industrial and commercial energy storage power station and collect the capacity parameters of each energy storage node unit.

[0031] The election response module is used to acquire historical operating data of each energy storage node unit, determine the operating stability coefficient of each energy storage node unit through the historical operating data, determine the capability readiness of each energy storage node unit according to the capability parameters of each energy storage node unit, and then perform node election response on each energy storage node unit based on all operating stability coefficients and all capability readiness to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station.

[0032] The node election module is used to initialize dynamic adjustment parameters, adjust the response of each energy storage node unit according to each election response degree and the dynamic adjustment parameters, obtain the calibration election index of each energy storage node unit, and then determine the dominant node unit and each cooperative node unit in the target industrial and commercial energy storage power station based on each calibration election index.

[0033] The node coordination module is used to coordinate the control of the target industrial and commercial energy storage power station through the dominant node unit and various coordinating node units.

[0034] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-described multi-node collaborative control method for industrial and commercial energy storage power stations.

[0035] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned multi-node collaborative control method for industrial and commercial energy storage power stations.

[0036] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0037] Initialize each energy storage node unit in the target industrial and commercial energy storage power station and collect the capability parameters of each energy storage node unit; acquire historical operating data of each energy storage node unit, determine the operating stability coefficient of each energy storage node unit based on the historical operating data, determine the capability readiness of each energy storage node unit based on the capability parameters of each energy storage node unit, and then perform node election response for each energy storage node unit based on all operating stability coefficients and all capability readiness, to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station; initialize dynamic adjustment parameters, adjust the response of each energy storage node unit based on each election response degree and the dynamic adjustment parameters, to obtain the calibration election index of each energy storage node unit, and then determine the dominant node unit and each cooperating node unit in the target industrial and commercial energy storage power station based on each calibration election index; and perform coordinated control of the target industrial and commercial energy storage power station through the dominant node unit and each cooperating node unit.

[0038] Therefore, this application demonstrates several key advantages. First, by initializing each energy storage node unit in the target industrial and commercial energy storage power station and collecting its capability parameters, a comprehensive understanding of the key operating characteristics of each node can be achieved. This provides accurate data support for subsequent node election and power allocation, ensuring the system's real-time and targeted control decisions. Second, by acquiring historical operating data of each energy storage node unit and calculating its operational stability coefficient, while simultaneously determining its capability readiness based on capability parameters, a comprehensive evaluation of nodes can be conducted across both historical reliability and current availability dimensions. This results in a more objective and comprehensive election response rate, more accurately reflecting the differences in long-term operational stability and immediate response capabilities among different nodes. This provides a strong basis for the rational selection of leading and cooperating nodes, effectively improving the control accuracy and overall operational reliability of industrial and commercial energy storage power stations. Third, by initializing and dynamically adjusting parameters and dynamically correcting them based on the election response rate of each energy storage node unit, a calibrated election index that better meets the grid operating environment and real-time dispatch requirements can be obtained. This introduces external operating condition factors and strategy guidance into the node election process. The dominant node unit and the cooperative node unit determined in this way can not only reflect the capabilities and stability of the nodes themselves, but also adapt and adjust to ensure that the industrial and commercial energy storage power station has a better power distribution effect under different operating scenarios. Finally, the target industrial and commercial energy storage power station is controlled in a coordinated manner through the dominant node unit and each cooperative node unit. The dominant node is responsible for receiving external power commands and generating a global power distribution strategy, while the cooperative nodes execute charging and discharging operations according to the distribution results, thereby realizing accurate tracking and rapid response of the power of the entire station.

[0039] In summary, the technical solution adopted in this application can avoid system risks caused by single node failures or performance bottlenecks, and improve the power response accuracy and operational stability of industrial and commercial energy storage power stations. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is an exemplary flowchart of the multi-node collaborative control method for industrial and commercial energy storage power stations provided in this application;

[0042] Figure 2 This is an exemplary flowchart for obtaining the election response of each energy storage node unit in the target industrial and commercial energy storage power station according to the present application;

[0043] Figure 3 This is an exemplary flowchart of obtaining the calibration election index of each energy storage node unit according to the present application;

[0044] Figure 4 This is a modular structure diagram of the multi-node collaborative control system for industrial and commercial energy storage power stations provided in this application;

[0045] Figure 5 This is a schematic diagram of the structure of a computer device for implementing a multi-node collaborative control method for industrial and commercial energy storage power stations, as provided in this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] This application provides a multi-node collaborative control system and method for commercial and industrial energy storage power stations. The core of this system involves initializing each energy storage node unit in the target commercial and industrial energy storage power station, collecting the capability parameters of each node unit, acquiring historical operating data of each node unit, determining the operational stability coefficient of each node unit based on the historical operating data, determining the capability readiness of each node unit based on its capability parameters, and then performing node election responses based on all operational stability coefficients and capability readiness to obtain the election response degree of each node unit in the target commercial and industrial energy storage power station. Dynamic adjustment parameters are initialized, and the response of each node unit is adjusted based on the election response degree and the dynamic adjustment parameters to obtain the calibration election index of each node unit. The dominant node unit and each cooperating node unit in the target commercial and industrial energy storage power station are then determined based on the calibration election index. The target commercial and industrial energy storage power station is then collaboratively controlled through the dominant node unit and each cooperating node unit. This approach avoids system risks caused by single-node failures or performance bottlenecks, and improves the power response accuracy and operational stability of commercial and industrial energy storage power stations.

[0048] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a multi-node collaborative control method for an industrial and commercial energy storage power station according to this embodiment of the present application. The collaborative control method includes the following steps:

[0049] In step S1, each energy storage node unit in the target industrial and commercial energy storage power station is initialized, and the capacity parameters of each energy storage node unit are collected.

[0050] In this embodiment, each energy storage node unit in the target industrial and commercial energy storage power station is initialized. In specific implementation, the back-end system of the target industrial and commercial energy storage power station can be used to count all energy storage containers, register the identities of all energy storage containers, and obtain all energy storage node units.

[0051] In this embodiment, the capability parameters of each energy storage node unit are collected by extracting the capability parameters of each energy storage node unit through the battery management system embedded in the target industrial and commercial energy storage power station. In specific implementation, the rated capacity, rated power, current power and current capacity of each energy storage node unit can be extracted through the battery management system embedded in the target industrial and commercial energy storage power station, so as to use the rated capacity, rated power, current power and current capacity of each energy storage node unit as the capability parameters of each energy storage node unit.

[0052] It should be noted that by initializing each energy storage node unit in the target industrial and commercial energy storage power station and collecting its capability parameters, the key operating characteristics of each node can be fully grasped, providing accurate data support for subsequent node election and power allocation, thereby ensuring that the system has real-time and targeted control decisions.

[0053] In step S2, historical operating data of each energy storage node unit is acquired, the operating stability coefficient of each energy storage node unit is determined through the historical operating data, the capability readiness of each energy storage node unit is determined according to the capability parameters of each energy storage node unit, and then node election response is performed on each energy storage node unit based on all operating stability coefficients and all capability readiness, so as to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station.

[0054] In practice, historical operating data of each energy storage node unit can be obtained. Specifically, for each energy storage node unit, the available time, cumulative fault time, and node fault frequency of the energy storage node unit can be extracted from the historical operating log stored locally on the controller of the energy storage node unit. The available time, cumulative fault time, and node fault frequency of the energy storage node unit are used as the historical operating data of the energy storage node unit, thereby obtaining the historical operating data of each energy storage node unit.

[0055] In this embodiment, determining the operational stability coefficient of each energy storage node unit using the historical operational data can be achieved through the following steps:

[0056] For each energy storage node unit, the node availability time, node failure frequency, and cumulative failure time of the energy storage node unit are extracted from the historical operation data.

[0057] The fault frequency penalty factor is determined based on the node fault frequency of the corresponding energy storage node unit.

[0058] The node availability rate of the energy storage node unit is determined by the node availability duration and the cumulative fault duration.

[0059] The operational stability coefficient of the corresponding energy storage node unit is determined by the fault frequency penalty factor and the node availability, thereby obtaining the operational stability coefficient of each energy storage node unit.

[0060] In specific implementation, firstly, for each energy storage node unit, the node availability time, node failure frequency, and cumulative failure time of the energy storage node unit are extracted from the historical operation data. That is, for each energy storage node unit, the node availability time, node failure frequency, and cumulative failure time of the energy storage node unit are extracted from the historical operation data. Then, the failure frequency penalty factor can be determined based on the node failure frequency of the corresponding energy storage node unit. That is, the failure frequency penalty factor of the energy storage node unit can be obtained by the following formula:

[0061] ;

[0062] Where P represents the fault frequency penalty factor; N represents the node fault frequency. It should be noted that the fault frequency penalty factor is a factor that quantifies the stability of the target node. Secondly, the node availability rate of the energy storage node unit can be determined by the node availability duration and the cumulative fault duration. That is, the node availability rate of the energy storage node unit can be obtained by the following formula:

[0063] ;

[0064] Where A represents the node availability rate; Indicates the duration of node availability; The cumulative fault duration is represented. Finally, the operational stability coefficient of the corresponding energy storage node unit can be determined by the fault frequency penalty factor and the node availability rate, thereby obtaining the operational stability coefficient of each energy storage node unit. That is, the fault frequency penalty factor of the energy storage node unit can be multiplied by the node availability rate, and the result can be used as the operational stability coefficient of the energy storage node unit. Thus, the operational stability coefficient of each energy storage node unit can be obtained according to the above steps. It should be noted that the operational stability coefficient is a coefficient used to comprehensively evaluate the stability of an energy storage node unit during its operational life cycle.

[0065] In this embodiment, determining the capability readiness of each energy storage node unit based on its capability parameters can be achieved through the following steps:

[0066] Obtain the current pre-execution strategy of the target industrial and commercial energy storage power station;

[0067] For each energy storage node unit, the capability weight of each capability parameter of the energy storage node unit is determined according to the pre-execution strategy;

[0068] The capability readiness of each energy storage node unit is determined based on its various capability parameters and corresponding capability weights, thereby obtaining the capability readiness of each energy storage node unit.

[0069] In practical implementation, firstly, the current pre-execution strategy of the target commercial and industrial energy storage power station can be obtained. Specifically, the current pre-execution strategy can be extracted through the battery management system embedded in the target commercial and industrial energy storage power station. The pre-execution strategy includes: strategy type and power value, such as: energy type (charging during off-peak hours and discharging during peak hours), power value; power type (smoothing load curve), power value; extreme power type (rapid response to grid frequency fluctuations to maintain grid stability), power value. Then, for each energy storage node unit, the capability weights of each capability parameter of the energy storage node unit are determined according to the pre-execution strategy. That is, the capability weights of each capability parameter of the energy storage node unit can be preset according to the pre-execution strategy. For example: energy type, the capability weight of rated capacity is 0.3, the capability weight of rated power is 0.2, the capability weight of current power is 0.2, and the capability weight of current capacity is 0.3; power type, the capability weight of rated capacity is 0.2, the capability weight of rated power is 0.3, and the capability weight of current power is 0.35. The current capacity capability weight is 0.15; for the maximum power type, the rated capacity capability weight is 0.1, the rated power capability weight is 0.35, the current capacity capability weight is 0.1, and the current power capability weight is 0.45. These weights can be preset based on historical experience to obtain the capability weights of each capability parameter of the energy storage node unit. Finally, the capability readiness of the energy storage node unit can be determined based on its various capability parameters and their corresponding capability weights. This is achieved by normalizing each capability parameter, multiplying the normalized result by its corresponding capability weight, and summing the results. The sum is then used as the capability readiness of the energy storage node unit. It should be noted that the capability readiness is a coefficient used to comprehensively evaluate the overall capability of the energy storage node unit in performing power charging and discharging tasks.

[0070] Preferably, in this embodiment, reference Figure 2 As shown, this figure is an exemplary flowchart of obtaining the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station in an embodiment of this application. In this embodiment, the election response of each energy storage node unit is performed based on all operational stability coefficients and all capability readiness. The specific steps to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station are as follows:

[0071] In step S21, each energy storage node unit is screened by a preset health gating mask to obtain each candidate node in the target industrial and commercial energy storage power station;

[0072] In step S22, for each candidate node in the target industrial and commercial energy storage power station, the election response degree of the corresponding candidate node is determined by the operational stability coefficient and capability readiness degree of the candidate node, thereby obtaining the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station.

[0073] In practice, firstly, each energy storage node unit can be screened using a preset health gating mask to obtain each candidate node in the target industrial and commercial energy storage power station. That is, the communication delay of each energy storage node unit can be obtained from the back-end system of the target industrial and commercial energy storage power station, the average communication delay of each energy storage node unit is calculated, and the average communication delay is used as the health gating mask. The communication delay of each energy storage node unit is compared with the health gating mask, and all energy storage node units with communication delays greater than the health gating mask are eliminated. The remaining energy storage node units are then used as each candidate node in the target industrial and commercial energy storage power station. Then, for each candidate node in the target industrial and commercial energy storage power station, the election response degree of the corresponding candidate node is determined by the operational stability coefficient and capability readiness of the candidate node, thereby obtaining the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station. That is, for each candidate node in the target industrial and commercial energy storage power station, the operational stability coefficient and capability readiness of the candidate node are multiplied, and the result is used as the election response degree of the candidate node, thereby obtaining the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station. It should be noted that the election response degree represents the degree of adaptation of the energy storage node unit to the dominant node at the current moment.

[0074] It should be noted that by acquiring historical operating data of each energy storage node and calculating its operational stability coefficient, while simultaneously determining its capability readiness based on capability parameters, a comprehensive evaluation of the nodes can be conducted across both historical reliability and current availability dimensions. This results in a more objective and comprehensive election response rate. This approach more accurately reflects the differences in long-term operational stability and immediate response capabilities among different nodes, thus providing a strong basis for the rational selection of leading and cooperating nodes, and effectively improving the control precision and overall operational reliability of commercial and industrial energy storage power stations.

[0075] In step S3, the dynamic adjustment parameters are initialized, and the response of each energy storage node unit is adjusted according to each election response degree and the dynamic adjustment parameters to obtain the calibration election index of each energy storage node unit. Then, the dominant node unit and each cooperative node unit in the target industrial and commercial energy storage power station are determined based on each calibration election index.

[0076] In specific implementation, the dynamic adjustment parameters are initialized by obtaining the current pre-execution strategy of the target industrial and commercial energy storage power station. The dynamic adjustment parameters are initialized according to the pre-execution strategy. Specifically, the power value in the pre-execution strategy is subtracted from the current power of the target industrial and commercial energy storage power station, and the absolute value of the result is taken to obtain the power difference. This power difference is then compared with the maximum power of the target industrial and commercial energy storage power station. When the power difference is less than 5% of the maximum power, the urgency parameter is set to 0.2; when the power difference is greater than 5% but less than 15% of the maximum power, the urgency parameter is set to 0.5; when the power difference is greater than 15% of the maximum power, the urgency parameter is set to 0.9. Then, a type adjustment parameter is preset according to the strategy type in the current pre-execution strategy: 0.3 for energy type, 0.6 for power type, and 0.9 for maximum power type. The urgency parameter and the type adjustment parameter are multiplied, and the result is used as the dynamic adjustment parameter.

[0077] Preferably, in this embodiment, reference Figure 3 As shown, this figure is an exemplary flowchart of obtaining the calibration election index of each energy storage node unit in an embodiment of this application. In this embodiment, the energy storage node unit is adjusted according to each election response degree and the dynamic adjustment parameter to obtain the calibration election index of each energy storage node unit. This can be achieved by the following steps:

[0078] In step S31, the election adjustment coefficient of each energy storage node unit is determined by the dynamic adjustment parameters and the operational stability coefficient of each energy storage node unit.

[0079] In step S32, the election response is adjusted according to each election adjustment coefficient to obtain the calibration election index of each energy storage node unit.

[0080] In practice, firstly, the election adjustment coefficient of each energy storage node unit can be determined through the dynamic adjustment parameters and the operational stability coefficient of each energy storage node unit. That is, the election adjustment coefficient can be obtained by the following formula:

[0081] ;

[0082] in, represents the election adjustment coefficient for the i-th energy storage node unit; C represents the dynamic adjustment parameter; This represents the operational stability coefficient of the energy storage node unit. Then, each election response can be adjusted according to the election adjustment coefficient to obtain the calibration election index of each energy storage node unit. That is, each election response can be multiplied by the election adjustment coefficient, and the result can be used as the calibration election index of each energy storage node unit. It should be noted that the calibration election index is the final election index after being corrected by dynamic adjustment parameters.

[0083] In this embodiment, the dominant node unit and each cooperating node unit in the target industrial and commercial energy storage power station are determined based on each calibration election index. In specific implementation, the calibration election index of each energy storage node unit is sorted from largest to smallest, and the energy storage node unit with the largest calibration election index is taken as the dominant node unit in the target industrial and commercial energy storage power station, and the remaining energy storage node units are taken as the cooperating node units in the target industrial and commercial energy storage power station.

[0084] It should be noted that by initially adjusting parameters dynamically and then dynamically correcting them based on the election response of each energy storage node, a calibrated election index that better meets the grid operating environment and real-time dispatch requirements can be obtained. This introduces external operating conditions and strategy guidance into the node election process. The dominant and cooperating node units determined in this way not only reflect the node's own capabilities and stability but also can adaptively adjust, ensuring that commercial and industrial energy storage power stations have better power allocation performance under different operating scenarios.

[0085] In step S4, the target industrial and commercial energy storage power station is controlled collaboratively by the dominant node unit and each cooperating node unit.

[0086] In this embodiment, the coordinated control of the target industrial and commercial energy storage power station through the dominant node unit and various cooperating node units can be achieved through the following steps:

[0087] The dominant node unit receives power commands from the target industrial and commercial energy storage power station and generates a power allocation strategy.

[0088] Power is allocated to each cooperating node unit according to the power allocation strategy.

[0089] In specific implementation, the power command from the target industrial and commercial energy storage power station can be received through the dominant node unit, and a power allocation strategy can be generated. That is, the power command from the target industrial and commercial energy storage power station can be received through the dominant node unit, and power can be allocated according to the calibration election index of each cooperative node unit. The higher the ranking, the more power is allocated, but it cannot exceed the rated power of the cooperative node unit, thus obtaining the power allocation strategy. Then, power can be allocated to each cooperative node unit according to the power allocation strategy. That is, the power allocation strategy can be converted into power value commands and transmitted to each cooperative node unit to complete the multi-node cooperative control of the target industrial and commercial energy storage power station.

[0090] It should be noted that the target industrial and commercial energy storage power station is controlled collaboratively by the leading node unit and various cooperating node units. The leading node is responsible for receiving external power commands and generating a global power allocation strategy, while the cooperating nodes execute charging and discharging operations according to the allocation results, thereby achieving accurate tracking and rapid response of the entire station's power.

[0091] Therefore, this application demonstrates several key advantages. First, by initializing each energy storage node unit in the target industrial and commercial energy storage power station and collecting its capability parameters, a comprehensive understanding of the key operating characteristics of each node can be achieved. This provides accurate data support for subsequent node election and power allocation, ensuring the system's real-time and targeted control decisions. Second, by acquiring historical operating data of each energy storage node unit and calculating its operational stability coefficient, while simultaneously determining its capability readiness based on capability parameters, a comprehensive evaluation of nodes can be conducted across both historical reliability and current availability dimensions. This results in a more objective and comprehensive election response rate, more accurately reflecting the differences in long-term operational stability and immediate response capabilities among different nodes. This provides a strong basis for the rational selection of leading and cooperating nodes, effectively improving the control accuracy and overall operational reliability of industrial and commercial energy storage power stations. Third, by initializing and dynamically adjusting parameters and dynamically correcting them based on the election response rate of each energy storage node unit, a calibrated election index that better meets the grid operating environment and real-time dispatch requirements can be obtained. This introduces external operating condition factors and strategy guidance into the node election process. The dominant node unit and the cooperative node unit determined in this way can not only reflect the capabilities and stability of the nodes themselves, but also adapt and adjust to ensure that the industrial and commercial energy storage power station has a better power distribution effect under different operating scenarios. Finally, the target industrial and commercial energy storage power station is controlled in a coordinated manner through the dominant node unit and each cooperative node unit. The dominant node is responsible for receiving external power commands and generating a global power distribution strategy, while the cooperative nodes execute charging and discharging operations according to the distribution results, thereby realizing accurate tracking and rapid response of the power of the entire station.

[0092] In summary, the technical solution adopted in this application can avoid system risks caused by single node failures or performance bottlenecks, and improve the power response accuracy and operational stability of industrial and commercial energy storage power stations.

[0093] Example 2: This application provides a reference for a multi-node collaborative control system for industrial and commercial energy storage power stations. Figure 4 As shown in the figure, this is a modular structure diagram of a multi-node collaborative control system for an industrial and commercial energy storage power station according to this embodiment of the application. The multi-node collaborative control system for the industrial and commercial energy storage power station includes:

[0094] The energy storage node module 100 is used to initialize each energy storage node unit in the target industrial and commercial energy storage power station and collect the capacity parameters of each energy storage node unit.

[0095] The election response module 200 is used to acquire historical operating data of each energy storage node unit, determine the operating stability coefficient of each energy storage node unit through the historical operating data, determine the capability readiness of each energy storage node unit according to the capability parameters of each energy storage node unit, and then perform node election response on each energy storage node unit based on all operating stability coefficients and all capability readiness to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station.

[0096] The node election module 300 is used to initialize dynamic adjustment parameters, adjust the response of each energy storage node unit according to each election response degree and the dynamic adjustment parameters, obtain the calibration election index of each energy storage node unit, and then determine the dominant node unit and each cooperative node unit in the target industrial and commercial energy storage power station based on each calibration election index.

[0097] The node coordination module 400 is used to coordinate the control of the target industrial and commercial energy storage power station through the dominant node unit and each coordinating node unit.

[0098] The foregoing detailed an example of a multi-node collaborative control system and method for industrial and commercial energy storage power stations provided in this application. It is understood that the corresponding device, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0099] In embodiment three, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for calling and running the computer programs from the memory, so that the computer device executes the above-described multi-node collaborative control method for industrial and commercial energy storage power stations.

[0100] In this embodiment, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of a computer device for a multi-node collaborative control system of an industrial and commercial energy storage power station according to an embodiment of this application. The above-described multi-node collaborative control method for an industrial and commercial energy storage power station in the above embodiment can be achieved through... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device may be a terminal device, a server or a chip.

[0101] Processor 501 can be a general-purpose processor or a special-purpose processor. For example, processor 501 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 505 to realize signal input (reception) and output (transmission).

[0102] For example, the computer device may be a chip, and the communication unit 505 may be the input and / or output circuit of the chip, or the communication unit 505 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0103] For example, the computer device may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0104] The computer device may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0105] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0106] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in processor 501. Processor 501 can be a central processing unit, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA), or other programmable logic device, such as discrete gate, transistor logic device, or discrete hardware component.

[0107] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] In embodiment four, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described multi-node collaborative control method for industrial and commercial energy storage power stations.

[0109] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0110] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A multi-node collaborative control method for industrial and commercial energy storage power stations, characterized in that, The collaborative control method includes: Initialize each energy storage node unit in the target industrial and commercial energy storage power station and collect the capacity parameters of each energy storage node unit; Obtain historical operating data for each energy storage node unit, and determine the operational stability coefficient of each energy storage node unit using the historical operating data. Specifically, determining the operational stability coefficient of each energy storage node unit using the historical operating data includes: For each energy storage node unit, the node availability time, node failure frequency, and cumulative failure time of the energy storage node unit are extracted from the historical operation data. The fault frequency penalty factor is determined based on the node fault frequency of the corresponding energy storage node unit. The node availability rate of the energy storage node unit is determined by the node availability duration and the cumulative fault duration. The operational stability coefficient of the corresponding energy storage node unit is determined by the fault frequency penalty factor and the node availability, thereby obtaining the operational stability coefficient of each energy storage node unit. The capability readiness of each energy storage node unit is then determined based on its capability parameters. Specifically, determining the capability readiness of each energy storage node unit based on its capability parameters includes: Obtain the current pre-execution strategy of the target industrial and commercial energy storage power station; For each energy storage node unit, the capability weight of each capability parameter of the energy storage node unit is determined according to the pre-execution strategy; The capability readiness of each energy storage node unit is determined based on its capability parameters and corresponding capability weights. This yields the capability readiness of each individual energy storage node unit. Then, based on all operational stability coefficients and capability readiness scores, a node election response is performed on each energy storage node unit to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station. Specifically, the node election response based on all operational stability coefficients and capability readiness scores to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station includes: Each energy storage node unit is screened by a preset health gating mask to obtain each candidate node in the target industrial and commercial energy storage power station; For each candidate node in the target industrial and commercial energy storage power station, the election response degree of the corresponding candidate node is determined by the operational stability coefficient and capability readiness degree of the candidate node, thereby obtaining the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station. Initialize dynamic adjustment parameters, adjust the response of each energy storage node unit according to each election response degree and the dynamic adjustment parameters, obtain the calibration election index of each energy storage node unit, and then determine the dominant node unit and each cooperative node unit in the target industrial and commercial energy storage power station based on each calibration election index. The target industrial and commercial energy storage power station is controlled collaboratively by the dominant node unit and each cooperating node unit.

2. The multi-node collaborative control method for industrial and commercial energy storage power stations as described in claim 1, characterized in that, The capability parameters of each energy storage node unit are collected by extracting the capability parameters of each energy storage node unit from the battery management system embedded in the target industrial and commercial energy storage power station.

3. The multi-node collaborative control method for industrial and commercial energy storage power stations as described in claim 1, characterized in that, Based on the election responsiveness and the aforementioned dynamic adjustment parameters, the response of each energy storage node unit is adjusted to obtain the calibration election index of each energy storage node unit, specifically including: The election adjustment coefficient of each energy storage node unit is determined by the dynamic adjustment parameters and the operational stability coefficient of each energy storage node unit. Each election response is adjusted according to the election adjustment coefficient to obtain the calibration election index of each energy storage node unit.

4. The multi-node collaborative control method for industrial and commercial energy storage power stations as described in claim 1, characterized in that, The coordinated control of the target industrial and commercial energy storage power station through the dominant node unit and various cooperating node units specifically includes: The dominant node unit receives power commands from the target industrial and commercial energy storage power station and generates a power allocation strategy. Power is allocated to each cooperating node unit according to the power allocation strategy.

5. A multi-node collaborative control system for an industrial and commercial energy storage power station, used to execute the multi-node collaborative control method for an industrial and commercial energy storage power station as described in any one of claims 1 to 4, characterized in that, The multi-node collaborative control system includes: The energy storage node module is used to initialize each energy storage node unit in the target industrial and commercial energy storage power station and collect the capacity parameters of each energy storage node unit. The election response module is used to acquire historical operating data of each energy storage node unit, determine the operating stability coefficient of each energy storage node unit through the historical operating data, determine the capability readiness of each energy storage node unit according to the capability parameters of each energy storage node unit, and then perform node election response on each energy storage node unit based on all operating stability coefficients and all capability readiness to obtain the election response degree of each energy storage node unit in the target industrial and commercial energy storage power station. The node election module is used to initialize dynamic adjustment parameters, adjust the response of each energy storage node unit according to each election response degree and the dynamic adjustment parameters, obtain the calibration election index of each energy storage node unit, and then determine the dominant node unit and each cooperative node unit in the target industrial and commercial energy storage power station based on each calibration election index. The node coordination module is used to coordinate the control of the target industrial and commercial energy storage power station through the dominant node unit and various coordinating node units.

6. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device executes the multi-node collaborative control method for industrial and commercial energy storage power stations according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement a multi-node collaborative control method for an industrial and commercial energy storage power station as described in any one of claims 1 to 4.

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