A method for partitioning a cluster of energy storage devices and related apparatus

Abstract

The application provides a method and related device for partitioning a cluster of energy storage devices. The method includes: obtaining hardware parameters and operating parameters corresponding to each energy storage device in the cluster of energy storage devices; determining a performance score corresponding to each energy storage device based on the hardware parameters and operating parameters corresponding to each energy storage device; obtaining a service weight corresponding to each energy storage service; and dividing the cluster of energy storage devices into multiple device partitions according to the performance score corresponding to each energy storage device and the service weight corresponding to each energy storage service. In the application, the cluster of energy storage devices is divided into multiple device partitions according to the performance score corresponding to each energy storage device and the service weight corresponding to each energy storage service, so that the hardware attributes of each energy storage device and the service value corresponding to the energy storage service are considered in the process of partitioning the cluster of energy storage devices, and dynamic adaptation between the partitioning of the cluster of energy storage devices and the energy storage service is achieved.

Description

Technical Field

[0001] This invention relates to the field of equipment management technology, and in particular to a method for partitioning energy storage equipment clusters and related devices. Background Technology

[0002] With the deepening of power market reforms, the profit model and operational scenarios of energy storage equipment clusters are undergoing profound changes. Energy storage equipment clusters can participate in various energy storage businesses, including but not limited to peak-valley price arbitrage, electricity spot market trading, automatic generation control ancillary services, user-side demand management, local consumption of renewable energy, and grid voltage stabilization and backup. Among these, different energy storage businesses exhibit significant differences in response speed, power control accuracy, battery loss tolerance, revenue premium levels, and grid connection security constraints.

[0003] In existing technologies, energy storage equipment clusters rely on physical hardware such as battery cabinets and battery clusters to divide control units, thereby dividing the energy storage equipment cluster into multiple partitions. Each partition executes corresponding energy storage services based on its own hardware characteristics.

[0004] However, existing partitioning technologies for energy storage device clusters are entirely based on predefined hardware topologies and lack dynamic reconfiguration capabilities. Once the battery cabinets, electrical wiring, and hardware topology are fixed, the partitioning granularity is locked, making it impossible to repartition based on dynamic changes in energy storage devices and adjustments to energy storage operations. In other words, existing energy storage device cluster partitioning methods cannot achieve dynamic adaptation to energy storage operations. Summary of the Invention

[0005] The main objective of this invention is to provide a method and related apparatus for partitioning energy storage device clusters. This aims to solve the technical problem that existing energy storage device cluster partitioning methods cannot achieve dynamic adaptation to energy storage services.

[0006] To achieve the above objectives, the present invention provides a method for partitioning an energy storage device cluster, the method comprising: Obtain the hardware and operating parameters of each energy storage device in the energy storage device cluster; Based on the hardware parameters and operating parameters corresponding to each energy storage device, the performance score corresponding to each energy storage device is determined. Obtain the business weight corresponding to each energy storage business; Based on the performance score of each energy storage device and the business weight of each energy storage service, the energy storage device cluster is divided into multiple device partitions. Each device partition includes at least one energy storage device, and each device partition is used to execute different energy storage services.

[0007] Optionally, determining the performance score for each energy storage device based on its corresponding hardware and operating parameters includes: For each energy storage device, the health level and degradation level of the energy storage device are determined based on the corresponding hardware parameters. Based on the operating parameters of the energy storage device, determine the charging and discharging response speed and voltage-temperature difference operating stability of the energy storage device. The health status, attenuation level, charge / discharge response speed, and voltage / temperature difference operation stability are quantitatively scored to obtain the corresponding performance score of the energy storage device.

[0008] Optionally, the step of dividing the energy storage device cluster into multiple device partitions based on the performance score corresponding to each energy storage device and the business weight corresponding to each energy storage business includes: Using a preset clustering algorithm, the performance score and business weight of each energy storage device are used as clustering features to perform clustering analysis on the multiple energy storage devices included in the energy storage device cluster, resulting in multiple device partitions.

[0009] Optionally, after dividing the energy storage device cluster into multiple device partitions based on the performance score corresponding to each energy storage device and the business weight corresponding to each energy storage business, the method further includes: Continuously monitor the hardware and operating parameters of each energy storage device in the multiple device partitions; Based on the monitored hardware and operating parameters, the performance score corresponding to each energy storage device is determined again. Based on the performance score of each energy storage device and the business weight of each energy storage business, the energy storage devices in some device partitions are adjusted.

[0010] Optionally, the plurality of equipment partitions include a first partition and a second partition, and the energy storage service includes a first service and a second service; After dividing the energy storage device cluster into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage service, the method further includes: Invoke the first partition to execute the first service, and; The second partition is invoked to execute the second service, and the upper limit of the charging and discharging rate, the daily cycle frequency, and the power switching action of the energy storage devices in the second partition are restricted. Among them, the health status, charge and discharge response speed and voltage temperature difference operation stability of the energy storage devices in the first partition are higher than those of the energy storage devices in the second partition, and the attenuation degree of the energy storage devices in the first partition is lower than that of the energy storage devices in the second partition. The business value corresponding to the first business is higher than the business value corresponding to the second business.

[0011] Optionally, after dividing the energy storage device cluster into multiple device partitions based on the performance score corresponding to each energy storage device and the business weight corresponding to each energy storage business, the method further includes: Upon receiving an anomaly signal, acquire electricity price data, grid connection constraints, operating losses for each equipment zone, and equipment aging rate. Based on the electricity price data, the grid connection constraints, the operating losses corresponding to each equipment partition, and the equipment aging rate, the energy storage equipment cluster is controlled to perform the target actions.

[0012] Optionally, the abnormal signal includes any one of the following: Electricity price fluctuation signals; Anomaly signals related to grid connection constraints; Signals of abnormal operating losses; Abnormal signal of equipment aging rate; The target action includes at least one of the following: Re-partition the equipment; Adjust the operating mode of each device partition; Adjust the charging and discharging power of each device zone.

[0013] Furthermore, to achieve the above objectives, the present invention also provides an energy storage device cluster partitioning device, the device comprising: The first acquisition module is used to acquire the hardware parameters and operating parameters corresponding to each energy storage device in the energy storage device cluster. The first determining module is used to determine the performance score of each energy storage device based on the hardware parameters and operating parameters corresponding to each energy storage device. The second acquisition module is used to acquire the business weight corresponding to each energy storage business; The partitioning module is used to divide the energy storage device cluster into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage business. Each device partition includes at least one energy storage device, and each device partition is used to execute different energy storage businesses.

[0014] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution: The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the energy storage device cluster partitioning method proposed in any of the embodiments of this application.

[0015] To address the aforementioned technical problems, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the energy storage device cluster partitioning method proposed in any of the embodiments of this application.

[0016] This application provides a method and related apparatus for partitioning an energy storage device cluster. The method includes: obtaining the hardware parameters and operating parameters corresponding to each energy storage device in the cluster; determining the performance score for each energy storage device based on its hardware and operating parameters; obtaining the business weight for each energy storage service; and dividing the energy storage device cluster into multiple device partitions according to the performance score and business weight of each energy storage service. Each device partition includes at least one energy storage device, and each partition is used to execute different energy storage services. In this application, the energy storage device cluster is divided into multiple device partitions based on the performance score and business weight of each energy storage service. This partitioning process considers the hardware attributes of each energy storage device and the business value of the energy storage services, thereby achieving dynamic adaptation between the energy storage device cluster partitions and the energy storage services. Attached Figure Description

[0017] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the energy storage device cluster partitioning method provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the application process of the energy storage device cluster partitioning method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the energy storage equipment cluster partitioning device provided in the embodiments of this application; Figure 4 This is a basic structural block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or accompanying drawings of this application are used to distinguish different objects, not to describe a particular order.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] Please see Figure 1 The flowchart illustrates an embodiment of the energy storage device cluster partitioning method according to this application. The energy storage device cluster partitioning method provided in this application includes the following steps: Step 101: Obtain the hardware parameters and operating parameters corresponding to each energy storage device in the energy storage device cluster.

[0022] It should be understood that the aforementioned energy storage equipment includes, but is not limited to, battery clusters, battery modules, individual battery cells, and related supporting equipment. These devices are distributed at different levels within the site and are interconnected through electrical connections and communication networks, forming an energy storage equipment cluster.

[0023] In this step, communication connections can be established with each device in the energy storage device cluster through various power communication protocols. By combining address traversal with a heartbeat handshake verification mechanism, all devices in the station are detected one by one, and the hardware parameters and operating parameters of each energy storage device are automatically obtained.

[0024] The hardware parameters include battery health status, capacity degradation rate, and internal resistance degradation rate. The operating parameters include power regulation response delay, charge / discharge switching response speed, individual cell voltage difference, and operating temperature distribution.

[0025] Step 102: Determine the performance score for each energy storage device based on the hardware parameters and operating parameters corresponding to each energy storage device.

[0026] In this step, the performance level of each energy storage device is comprehensively and quantitatively evaluated based on its hardware and operating parameters, generating a corresponding performance score. This performance score is used to characterize the overall performance of the energy storage device, including but not limited to the device's health aging level, charge and discharge response capability, and operational stability.

[0027] Step 103: Obtain the business weight corresponding to each energy storage business.

[0028] Optionally, energy storage services include, but are not limited to: frequency regulation ancillary services, electricity spot trading, peak-valley price arbitrage, local consumption of renewable energy, grid spinning reserve, user-side demand backup, grid connection point voltage stabilization, fault emergency power support, and frequency stability control.

[0029] It should be understood that different energy storage services have different performance requirements for energy storage equipment. For example, frequency regulation ancillary services require energy storage equipment to have rapid power response and power regulation capabilities, which places high demands on equipment performance. On the other hand, backup services such as grid spinning reserve and voltage stabilization have relatively lower requirements for equipment response speed.

[0030] In this step, a corresponding business weight is generated for each energy storage business based on factors such as market revenue potential, grid demand urgency, and technical barriers to entry. These business weights characterize the importance, revenue level, and performance requirements of the corresponding energy storage business in the current time period.

[0031] Optionally, the weight of business operations changes dynamically with market conditions and grid demand. For example, during peak electricity price periods, the weight of peak-valley price arbitrage increases; when grid frequency fluctuates drastically, the weight of frequency regulation ancillary services increases.

[0032] Step 104: Based on the performance score of each energy storage device and the business weight of each energy storage service, the energy storage device cluster is divided into multiple device partitions. Each device partition includes at least one energy storage device, and each device partition is used to execute different energy storage services.

[0033] In this step, the energy storage device cluster is divided into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage business. In other words, energy storage devices with similar performance characteristics are aggregated into the same device partition, while devices with significantly different performance characteristics are assigned to different device partitions.

[0034] Optionally, energy storage devices with high performance scores are assigned to equipment zones for performing high-yield, high-requirement energy storage services; energy storage devices with low performance scores are assigned to equipment zones for performing low-yield, low-requirement energy storage services.

[0035] In this embodiment, the energy storage device cluster is divided into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage business. In this way, the hardware attributes of each energy storage device and the business value of the energy storage business are taken into account during the partitioning of the energy storage device cluster, thereby realizing dynamic adaptation between the energy storage device cluster partitions and the energy storage business.

[0036] Optionally, determining the performance score for each energy storage device based on its corresponding hardware and operating parameters includes: For each energy storage device, the health level and degradation level of the energy storage device are determined based on the corresponding hardware parameters. Based on the operating parameters of the energy storage device, determine the charging and discharging response speed and voltage-temperature difference operating stability of the energy storage device. The health status, attenuation level, charge / discharge response speed, and voltage / temperature difference operation stability are quantitatively scored to obtain the corresponding performance score of the energy storage device.

[0037] As mentioned above, hardware parameters include battery health status, capacity degradation rate, and internal resistance degradation rate. Operating parameters include power regulation response delay, charge / discharge switching response speed, individual cell voltage difference, and operating temperature distribution.

[0038] One alternative implementation is to use the SOH value in the hardware parameters as the basic indicator of health status, with a higher SOH indicating a better health status of the device.

[0039] One alternative implementation involves determining the degree of degradation of the energy storage device based on the capacity degradation rate and the internal resistance degradation rate. The higher the degree of degradation, the more severe the performance degradation of the device.

[0040] One optional implementation involves determining the charge / discharge response speed of the energy storage device based on the power regulation response delay and the charge / discharge switching response speed. A shorter response delay and a faster switching speed indicate a faster charge / discharge response speed.

[0041] One possible implementation is to determine the voltage-temperature difference operational stability of the energy storage device based on the individual unit voltage difference and operating temperature distribution; the smaller the individual unit voltage difference and the more uniform and closer the operating temperature is to the optimal operating temperature range, the better the operational stability of the device.

[0042] Furthermore, after obtaining the health status, attenuation level, charge / discharge response speed, and voltage / temperature difference operational stability of the energy storage device, the above-mentioned health status, attenuation level, charge / discharge response speed, and voltage / temperature difference operational stability are quantitatively scored, and then weighted and summed to obtain the performance score of the energy storage device.

[0043] Optionally, the health rating is based on the State of Health (SOH) value, with a score of 100 points when the SOH value is 100%, and 1 point deducted for every 1% decrease in the SOH value. The degradation rating starts at 100 points, with points deducted for capacity degradation and increased internal resistance, respectively. The charge / discharge response speed rating is based on the response delay. The voltage-temperature difference operational stability rating is based on the voltage difference and temperature deviation.

[0044] In this embodiment, a performance score that can accurately characterize the performance of an energy storage device is generated based on the device's health status, attenuation level, charge / discharge response speed, and voltage / temperature difference operational stability.

[0045] Optionally, the step of dividing the energy storage device cluster into multiple device partitions based on the performance score corresponding to each energy storage device and the business weight corresponding to each energy storage business includes: Using a preset clustering algorithm, the performance score and business weight of each energy storage device are used as clustering features to perform clustering analysis on the multiple energy storage devices included in the energy storage device cluster, resulting in multiple device partitions.

[0046] Optionally, the above clustering algorithm is the K-means clustering algorithm.

[0047] An optional implementation involves constructing a feature vector V for the i-th energy storage device in the energy storage device cluster. i =[ P i , W1, W2,..., W M ], where P i The performance scores for this device are W1, W2, ..., W. M These are the business weights corresponding to the M energy storage businesses.

[0048] Optionally, the initial number of clusters K can be determined based on the quantity and type of energy storage services. For example, if the energy storage service has two service types, K can be set to 2.

[0049] The K-means algorithm iteratively calculates the distance from the feature vector of each energy storage device to the cluster center, assigning each device to the nearest cluster. The center of each cluster is then updated to the mean of the feature vectors of all devices within that cluster. This process is repeated until the maximum number of iterations is reached, resulting in multiple device partitions. In this embodiment, the clustering algorithm automates the partitioning of devices, which is more scientific, objective, and efficient than manual partitioning. Furthermore, it can adaptively adjust the partitioning results based on changes in device performance and business weights.

[0050] Optionally, after dividing the energy storage device cluster into multiple device partitions based on the performance score corresponding to each energy storage device and the business weight corresponding to each energy storage business, the method further includes: Continuously monitor the hardware and operating parameters of each energy storage device in the multiple device partitions; Based on the monitored hardware and operating parameters, the performance score corresponding to each energy storage device is determined again. Based on the performance score of each energy storage device and the business weight of each energy storage business, the energy storage devices in some device partitions are adjusted.

[0051] In this embodiment, after dividing the energy storage device cluster into multiple device partitions, the hardware parameters and operating parameters of each energy storage device in the multiple device partitions are continuously monitored. If abnormal fluctuations are detected in the hardware parameters and operating parameters of the energy storage device, the performance score of each energy storage device is recalculated based on the latest obtained hardware parameters and operating parameters.

[0052] Furthermore, when the recalculated performance score changes, or the business weight of the energy storage business changes, the partition adjustment process is triggered. Specifically, based on the updated performance score and business weight, multiple energy storage devices are re-clustered.

[0053] Optionally, re-clustering analysis may be performed only on energy storage devices with significant changes in performance scores (e.g., performance score changes exceeding a preset threshold) and energy storage devices associated with energy storage businesses with significant changes in business weights (e.g., business weight changes exceeding a preset threshold).

[0054] In this embodiment, a continuous monitoring and dynamic adjustment mechanism is used to ensure that the device partitioning results match the real-time status of the device and business requirements, thus overcoming the shortcomings of traditional static partitioning that cannot adapt to changes in the status of the device throughout its entire lifecycle.

[0055] Optionally, the plurality of equipment partitions include a first partition and a second partition, and the energy storage service includes a first service and a second service; After dividing the energy storage device cluster into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage service, the method further includes: Invoke the first partition to execute the first service, and; The second partition is invoked to execute the second service, and the upper limit of the charging and discharging rate, the daily cycle frequency, and the power switching action of the energy storage devices in the second partition are restricted. Among them, the health status, charge and discharge response speed and voltage temperature difference operation stability of the energy storage devices in the first partition are higher than those of the energy storage devices in the second partition, and the attenuation degree of the energy storage devices in the first partition is lower than that of the energy storage devices in the second partition. The business value corresponding to the first business is higher than the business value corresponding to the second business.

[0056] In this embodiment, the first partition is configured to operate in a revenue-first mode, and the first partition is invoked to execute the high-return first service. When executing the first service, the power regulation bandwidth of the first partition is opened up to support millisecond-level fast power response, allow high-frequency high-power condition switching, and do not strictly limit the number of daily cycles. It prioritizes responding to market-based trading instructions, with the core objective of maximizing electricity price arbitrage and ancillary service revenue.

[0057] The first business includes, but is not limited to: automatic generation control frequency regulation auxiliary services, intraday spot electricity trading, peak-valley price arbitrage, and local consumption of new energy, among other high-yield market-oriented businesses.

[0058] In this embodiment, the second partition is configured in a safety-assurance operation mode, and the second service, which is a backup measure, is invoked in this partition. When the second service is executed, the following restrictions are imposed on the second partition: the upper limit of the charge and discharge rate is locked to prevent excessive current surges; the daily cycle frequency is limited to avoid accelerated aging due to high-frequency charge and discharge; frequent power switching is prohibited, and only smooth steady-state charge and discharge are performed; cycle loss is strictly controlled to prioritize the safe operation of the equipment and slow down the aging rate of the battery.

[0059] The second service includes, but is not limited to: grid spinning reserve, user-side demand backup, grid connection point voltage stabilization, emergency power support for faults, frequency stability control and other support services.

[0060] It should be noted that the business links of the first partition and the business links of the second partition run in parallel, and their scheduling logic is isolated from each other and does not affect each other.

[0061] In this embodiment, a zoned management mechanism is used to achieve fine-grained resource allocation. The high-performance first zone undertakes the high-yield first business, while the low-performance second zone undertakes the guaranteed second business, thereby maximizing the utilization value of battery resources.

[0062] Optionally, after dividing the energy storage device cluster into multiple device partitions based on the performance score corresponding to each energy storage device and the business weight corresponding to each energy storage business, the method further includes: Upon receiving an anomaly signal, acquire electricity price data, grid connection constraints, operating losses for each equipment zone, and equipment aging rate. Based on the electricity price data, the grid connection constraints, the operating losses corresponding to each equipment partition, and the equipment aging rate, the energy storage equipment cluster is controlled to perform the target actions.

[0063] In this embodiment, upon receiving an anomaly signal, the system acquires electricity price data, grid connection constraints, operating losses for each equipment zone, and equipment aging rates.

[0064] Optionally, the abnormal signal includes any one of the following: Electricity price fluctuation signals; Anomaly signals related to grid connection constraints; Signals of abnormal operating losses; Abnormal signal of equipment aging rate.

[0065] Specifically, a price fluctuation signal is triggered when there are significant fluctuations in the time-of-use electricity price or ancillary service price in the electricity market; a grid connection constraint fluctuation signal is triggered when there are changes in grid dispatch instructions, grid connection power limits, or power fluctuation rate constraints; an operating loss fluctuation signal is triggered when the charging and discharging loss or input-output return ratio of a certain equipment zone deviates from the optimal range; and an equipment aging rate fluctuation signal is triggered when the SOH decay rate or cell voltage difference of energy storage equipment in a certain equipment zone exceeds a preset threshold.

[0066] Furthermore, based on electricity price data, grid connection constraints, operating losses corresponding to each equipment zone, and equipment aging rates, the energy storage equipment cluster is controlled to perform target actions. That is, with the goal of maximizing the net revenue of the energy storage power station throughout its entire life cycle, a multi-objective optimization model is built, outputting control commands to control the energy storage equipment cluster to perform target actions.

[0067] Optionally, the target action includes at least one of the following: Re-partition the equipment; Adjust the operating mode of each device partition; Adjust the charging and discharging power of each device zone.

[0068] The aforementioned re-division of equipment zones specifically involves reassigning energy storage devices with improved health status to high-performance zones when the aging rate of equipment fluctuates, and relocating energy storage devices with worsening degradation from high-load zones to low-performance zones, thereby achieving dynamic reconstruction of the zone structure.

[0069] The aforementioned adjustment to the operating mode of each equipment zone specifically involves intelligently rotating the zone's operating mode based on the daily electricity price sequence and grid demand.

[0070] The above-mentioned adjustment of the charging and discharging power of each equipment zone is specifically as follows: when the operating loss changes, the charging and discharging power of each zone is adaptively allocated, and high-power commands such as frequency modulation arbitrage are reasonably distributed to avoid long-term full-load operation of equipment zones and delay equipment aging.

[0071] This embodiment achieves the optimal return on the entire lifecycle of the energy storage device cluster by dynamically balancing power generation revenue and battery loss costs through the above optimization process.

[0072] For a better understanding of the overall technical solution, please refer to [link / reference]. Figure 2 ,like Figure 2 As shown, the system obtains the hardware and operating parameters of each energy storage device and the business weight of each energy storage service; based on the above hardware parameters, operating parameters and business weight of each energy storage service, the energy storage device cluster is divided into multiple device partitions; each device partition is controlled to execute the corresponding energy storage service; the hardware and operating parameters of each energy storage device are continuously monitored, and the device partitions are iteratively optimized.

[0073] Further reading Figure 3 As a response to the above Figure 1 The implementation of the method shown in this application provides an embodiment of an energy storage device cluster partitioning device 300, which is similar to... Figure 1 The method embodiments shown correspond to those described.

[0074] An embodiment of the present invention provides an energy storage device cluster partitioning device 300 comprising: The first acquisition module 301 is used to acquire the hardware parameters and operating parameters corresponding to each energy storage device in the energy storage device cluster. The first determining module 302 is used to determine the performance score of each energy storage device based on the hardware parameters and operating parameters corresponding to each energy storage device. The second acquisition module 303 is used to acquire the business weight corresponding to each energy storage business; The partitioning module 304 is used to divide the energy storage device cluster into multiple device partitions according to the performance score corresponding to each energy storage device and the business weight corresponding to each energy storage business. Each device partition includes at least one energy storage device, and each device partition is used to execute different energy storage businesses.

[0075] Optionally, the first determining module 302 is specifically used for: For each energy storage device, the health level and degradation level of the energy storage device are determined based on the corresponding hardware parameters. Based on the operating parameters of the energy storage device, determine the charging and discharging response speed and voltage-temperature difference operating stability of the energy storage device. The health status, attenuation level, charge / discharge response speed, and voltage / temperature difference operation stability are quantitatively scored to obtain the corresponding performance score of the energy storage device.

[0076] Optionally, the partitioning module 304 is specifically used for: Using a preset clustering algorithm, the performance score and business weight of each energy storage device are used as clustering features to perform clustering analysis on the multiple energy storage devices included in the energy storage device cluster, resulting in multiple device partitions.

[0077] Optionally, the energy storage device cluster partitioning device 300 further includes: The monitoring module is used to continuously monitor the hardware parameters and operating parameters of each energy storage device in the multiple device partitions. The second determining module is used to determine the performance score corresponding to each energy storage device again based on the monitored hardware parameters and operating parameters. The adjustment module is used to adjust the energy storage devices in a certain device partition according to the performance score corresponding to each energy storage device and the business weight corresponding to each energy storage business.

[0078] Optionally, the plurality of equipment partitions include a first partition and a second partition, and the energy storage service includes a first service and a second service; Optionally, the energy storage device cluster partitioning device 300 further includes: The first calling module is used to call the first partition to execute the first service, and; The second calling module is used to call the second partition to execute the second service, and to limit the upper limit of the charging and discharging rate, the daily cycle frequency, and the power switching action of the energy storage devices in the second partition; Among them, the health status, charge and discharge response speed and voltage temperature difference operation stability of the energy storage devices in the first partition are higher than those of the energy storage devices in the second partition, and the attenuation degree of the energy storage devices in the first partition is lower than that of the energy storage devices in the second partition. The business value corresponding to the first business is higher than the business value corresponding to the second business.

[0079] Optionally, the energy storage device cluster partitioning device 300 further includes: The third acquisition module is used to acquire electricity price data, grid connection constraints, operating losses of each equipment zone, and equipment aging rate when receiving abnormal signals. The control module is used to control the energy storage equipment cluster to perform target actions based on the electricity price data, the grid connection constraints, the operating losses corresponding to each equipment partition, and the equipment aging rate.

[0080] Optionally, the abnormal signal includes any one of the following: Electricity price fluctuation signals; Anomaly signals related to grid connection constraints; Signals of abnormal operating losses; Abnormal signal of equipment aging rate; The target action includes at least one of the following: Re-partition the equipment; Adjust the operating mode of each device partition; Adjust the charging and discharging power of each device zone.

[0081] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed] for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0082] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0083] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0084] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating platform and various application software installed on the computer device 4, such as the program code of the energy storage device cluster partitioning method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0085] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run program code stored in the memory 41 or process data, for example, to run program code for the energy storage device cluster partitioning method.

[0086] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0087] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described energy storage device cluster partitioning method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0088] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware online platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0089] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0090] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for partitioning an energy storage device cluster, characterized in that, The method includes: Obtain the hardware and operating parameters of each energy storage device in the energy storage device cluster; Based on the hardware parameters and operating parameters corresponding to each energy storage device, the performance score corresponding to each energy storage device is determined. Obtain the business weight corresponding to each energy storage business; Based on the performance score of each energy storage device and the business weight of each energy storage service, the energy storage device cluster is divided into multiple device partitions. Each device partition includes at least one energy storage device, and each device partition is used to execute different energy storage services.

2. The method according to claim 1, characterized in that, The process of determining the performance score for each energy storage device based on its hardware and operating parameters includes: For each energy storage device, the health level and degradation level of the energy storage device are determined based on the corresponding hardware parameters. Based on the operating parameters of the energy storage device, determine the charging and discharging response speed and voltage-temperature difference operating stability of the energy storage device. The health status, attenuation level, charge / discharge response speed, and voltage / temperature difference operation stability are quantitatively scored to obtain the corresponding performance score of the energy storage device.

3. The method according to claim 1, characterized in that, The energy storage device cluster is divided into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage service, including: Using a preset clustering algorithm, the performance score and business weight of each energy storage device are used as clustering features to perform clustering analysis on the multiple energy storage devices included in the energy storage device cluster, resulting in multiple device partitions.

4. The method according to claim 1, characterized in that, After dividing the energy storage device cluster into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage service, the method further includes: Continuously monitor the hardware and operating parameters of each energy storage device in the multiple device partitions; Based on the monitored hardware and operating parameters, the performance score corresponding to each energy storage device is determined again. Based on the performance score of each energy storage device and the business weight of each energy storage business, the energy storage devices in some device partitions are adjusted.

5. The method according to claim 1, characterized in that, The multiple equipment partitions include a first partition and a second partition, and the energy storage service includes a first service and a second service; After dividing the energy storage device cluster into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage service, the method further includes: Invoke the first partition to execute the first service, and; The second partition is invoked to execute the second service, and the upper limit of the charging and discharging rate, the daily cycle frequency, and the power switching action of the energy storage devices in the second partition are restricted. Among them, the health status, charge and discharge response speed and voltage temperature difference operation stability of the energy storage devices in the first partition are higher than those of the energy storage devices in the second partition, and the attenuation degree of the energy storage devices in the first partition is lower than that of the energy storage devices in the second partition. The business value corresponding to the first business is higher than the business value corresponding to the second business.

6. The method according to claim 1, characterized in that, After dividing the energy storage device cluster into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage service, the method further includes: Upon receiving an anomaly signal, acquire electricity price data, grid connection constraints, operating losses for each equipment zone, and equipment aging rates. Based on the electricity price data, the grid connection constraints, the operating losses corresponding to each equipment partition, and the equipment aging rate, the energy storage equipment cluster is controlled to perform the target actions.

7. The method according to claim 6, characterized in that, The abnormal signal includes any one of the following: Electricity price fluctuation signals; Abnormal signals of grid connection constraints; Signals of abnormal operating losses; Abnormal signal of equipment aging rate; The target action includes at least one of the following: Re-partition the equipment; Adjust the operating mode of each device partition; Adjust the charging and discharging power of each device zone.

8. A cluster partitioning device for energy storage equipment, characterized in that, The device includes: The first acquisition module is used to acquire the hardware parameters and operating parameters corresponding to each energy storage device in the energy storage device cluster. The first determining module is used to determine the performance score of each energy storage device based on the hardware parameters and operating parameters corresponding to each energy storage device. The second acquisition module is used to acquire the business weight corresponding to each energy storage business; The partitioning module is used to divide the energy storage device cluster into multiple device partitions based on the performance score of each energy storage device and the business weight of each energy storage business. Each device partition includes at least one energy storage device, and each device partition is used to execute different energy storage businesses.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the energy storage device cluster partitioning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the energy storage device cluster partitioning method as described in any one of claims 1 to 7.

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