Energy storage system power distribution method and apparatus

Abstract

The application provides a power distribution method and device for an energy storage system, and relates to power storage technology; the method comprises the following steps: determining target operation parameters of a power conversion system based on target power to be distributed and an efficiency model of the power conversion system; the target operation parameters comprise a required quantity and / or a power distribution ratio of the power conversion system; generating a power distribution strategy based on the target operation parameters, and distributing the target power to each power conversion system according to the power distribution strategy. Through the application, the efficiency of the energy storage system can be effectively improved.

Description

Technical Field

[0001] This application relates to power storage technology, and more particularly to a power distribution method and device for an energy storage system. Background Technology

[0002] Energy storage systems typically consist of multiple battery clusters and a power conversion system (PCS). Through the coordinated control of an energy management system (EMS) and a battery management system (BMS), the charging and discharging power of the battery clusters can be dynamically allocated.

[0003] In related technologies, power allocation in energy storage systems is mainly achieved by obtaining the target power of the system, combining it with the basic operating status of the battery clusters, and then initially allocating the power of each battery cluster and PCS. At the same time, by referring to the basic operating parameters of the PCS, the number of operating PCS and the approximate power allocation ratio are determined, thereby completing the power scheduling of the entire energy storage system.

[0004] However, the above power distribution method makes it difficult for the PCS to operate in the optimal efficiency range, and the battery cluster charging and discharging coordination is poor, resulting in low overall system energy utilization efficiency. Summary of the Invention

[0005] This application provides a power distribution method and device for an energy storage system, which enables the PCS to operate in the optimal efficiency range and improves the charging and discharging coordination of battery clusters, thereby improving the efficiency of the energy storage system.

[0006] In a first aspect, this application provides a power distribution method for an energy storage system, the method comprising:

[0007] Based on the target power to be allocated and the efficiency model of the power conversion system, the target operating parameters of the power conversion system are determined; the target operating parameters include the demand quantity and / or power allocation ratio of the power conversion system.

[0008] Based on the target operating parameters, a power allocation strategy is generated, and the target power is allocated to each of the power conversion systems according to the power allocation strategy.

[0009] In one possible implementation, the target power is the requested power received by the energy storage system, the power conversion system corresponds one-to-one with the battery clusters in the energy storage system, and the generation of a power allocation strategy based on the target operating parameters includes:

[0010] The target battery cluster is determined based on the charging and discharging priority of each battery cluster in the energy storage system and the required quantity.

[0011] Based on the power allocation ratio, the power allocation strategy is determined; the power allocation strategy is used to allocate the target power to each of the target battery clusters.

[0012] In one possible implementation, the method further includes:

[0013] Based on the power allocation constraints, the target operating parameters and the charge / discharge priority are updated; the constraints include power adjustment rate constraints and / or multi-dimensional safety constraints.

[0014] In one possible implementation, the constraints for power allocation are obtained, including:

[0015] Based on the operating status parameters of each battery cluster, the maximum allowable charge and discharge power of each battery cluster is dynamically determined, and the power adjustment rate constraint is determined in combination with the hardware characteristics of the power conversion system; the power adjustment rate constraint is used to indicate the maximum change in the charge and discharge power of the battery cluster and / or the output power of the power conversion system per unit time.

[0016] And / or, based on the operating status parameters of each of the battery clusters and the rated operating parameters of the power conversion system, and in conjunction with the performance of the energy storage system, the multi-dimensional safety constraints are determined; the multi-dimensional safety constraints are used to indicate the operating status limits and power limits of the battery clusters and the power conversion system participating in power distribution.

[0017] In one possible implementation, updating the target operating parameters and the charge / discharge priority based on the power allocation constraints includes:

[0018] Based on the aforementioned multi-dimensional safety constraints, corrections are made to determine the corrected target operating parameters and charging / discharging priorities. The corrections include: limiting the charging / discharging power of each battery cluster to below the power limit, limiting the target operating parameters to within the rated operating parameter range, eliminating battery clusters whose operating states do not meet the multi-dimensional safety constraints, and canceling their corresponding charging / discharging priorities.

[0019] Based on the power adjustment rate constraint, the corrected target operating parameters and charge / discharge priorities are adjusted to determine the updated target operating parameters and charge / discharge priorities. The adjustment includes: making the target operating parameters match the limit requirement of the maximum power change per unit time, and adapting the charge / discharge priorities of the remaining normal battery clusters to the power change rate.

[0020] In one possible implementation, the power conversion system includes multiple conversion modules; the generation of a power allocation strategy based on the target operating parameters and the charging / discharging priority includes:

[0021] For each target battery cluster, the cluster-level power allocation for the target battery cluster is determined based on the power allocation ratio;

[0022] Based on the cluster-level power allocation and the efficiency model of the conversion module, the target operating parameters of the conversion module are determined;

[0023] Based on the target operating parameters of the conversion module, a power allocation strategy for the target battery cluster is determined; the power allocation strategy for the target battery cluster is used to allocate the cluster-level power to a corresponding number of target conversion modules.

[0024] In one possible implementation, the target power is the cluster-level allocated power received by the target battery cluster in the energy storage system, the cluster-level allocated power being determined based on the requested power received by the energy storage system, and the target battery cluster corresponding to multiple power conversion systems; the generation of a power allocation strategy based on the target operating parameters includes:

[0025] Based on the demand indicated by the target operating parameters, the target conversion system to participate in power allocation is determined from the plurality of power conversion systems;

[0026] Based on the power allocation ratio, the power allocation strategy is generated; the power allocation strategy is used to allocate the cluster-level allocated power to each of the target conversion systems.

[0027] In one possible implementation, determining the target operating parameters of the power conversion system based on the target power to be allocated and the efficiency model of the power conversion system includes:

[0028] Based on the efficiency model, the efficient operating range of the power conversion system is determined, and the optimal output power value of the power conversion system within the efficient operating range is obtained.

[0029] The required number of power conversion systems to be put into operation is determined based on the ratio of the target power to the optimal output power value of the power conversion system.

[0030] If the target power needs to be shared by multiple power conversion systems, the power allocation ratio of each power conversion system shall be determined according to the power balance principle.

[0031] In one possible implementation, the method further includes:

[0032] The target power of the energy storage system is determined based on grid dispatch instructions or load power demand.

[0033] And / or, based on the state of charge of each battery cluster, a charge / discharge priority is generated for each battery cluster; wherein, the discharge priority of a battery cluster with a high state of charge is higher than that of a battery cluster with a low state of charge, and the charging priority of a battery cluster with a low state of charge is higher than that of a battery cluster with a high state of charge.

[0034] Secondly, this application provides a power distribution device for an energy storage system, the device comprising:

[0035] The processing module is used to determine the target operating parameters of the power conversion system based on the target power to be allocated and the efficiency model of the power conversion system; the target operating parameters include the demand quantity and / or power allocation ratio of the power conversion system.

[0036] The allocation module is used to generate a power allocation strategy based on the target operating parameters, and allocate the target power to each of the power conversion systems according to the power allocation strategy.

[0037] Thirdly, this application provides an electronic device, including at least one processor and a memory communicatively connected to the processor;

[0038] The memory stores computer-executed instructions;

[0039] The processor executes computer execution instructions stored in the memory to implement the method as described in any of the first aspects.

[0040] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0041] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in any of the first aspects.

[0042] This application provides a power allocation method and device for an energy storage system. The method first determines the target operating parameters of the power conversion system based on the target power to be allocated and the efficiency model of the power conversion system. Then, it generates a power allocation strategy based on the target operating parameters and allocates the target power to each power conversion system according to this strategy. In this application, the target operating parameters of the power conversion system are accurately determined through the efficiency model, and the number of operating systems and the power allocation ratio are rationally planned to ensure that the power conversion system operates within its optimal efficiency range, reduce energy conversion losses, and thus improve the overall energy utilization efficiency of the energy storage system. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0044] Figure 1 This is a schematic diagram illustrating an application scenario of a power allocation method for an energy storage system provided in an embodiment of this application.

[0045] Figure 2 A flowchart illustrating a power allocation method for an energy storage system provided in this application embodiment. Figure 1 ;

[0046] Figure 3 A flowchart illustrating a power allocation method for an energy storage system provided in this application embodiment. Figure 2 ;

[0047] Figure 4 A schematic diagram of the structure of a power distribution device for an energy storage system provided in an embodiment of this application;

[0048] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0049] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0051] With the rapid development of renewable energy sources (such as photovoltaics and wind power), energy storage systems play a crucial role in the energy transition. The core functions of energy storage systems include smoothing out renewable energy fluctuations, grid peak shaving and frequency regulation, and energy time shifting.

[0052] In practical applications, energy storage systems typically consist of multiple battery clusters and a power conversion system (PCS). Through the coordinated control of the energy management system (EMS) and the battery management system (BMS), the charging and discharging power of the battery clusters can be dynamically allocated.

[0053] In related technologies, power allocation in energy storage systems typically begins with the upper-level energy management system determining the overall target power of the system. Then, based on basic operating information such as the voltage, current, and health status of each battery cluster, a rough power allocation is performed on each battery cluster and its corresponding PCS. Simultaneously, based solely on conventional operating parameters such as the rated capacity and maximum number of operating PCS, the number of PCS to be put into operation and the approximate power allocation ratio among each PCS are simply determined, thereby completing the charging and discharging power scheduling and overall operation control of the entire energy storage system.

[0054] It should be understood that the above power allocation method is only based on basic operating information and conventional operating parameters for a rough allocation. It lacks specific consideration for the operating efficiency of the PCS and fine coordination of the charging and discharging behavior of the battery clusters. As a result, it is difficult to make the PCS work in the optimal efficiency range, and the charging and discharging coordination of the battery clusters is poor, which leads to the overall energy utilization efficiency of the system being low.

[0055] Therefore, embodiments of this application provide a power allocation method and device for an energy storage system to solve the above-mentioned problems. Specifically, this application proposes a power allocation method based on the efficiency model of a power conversion system. The optimal target operating parameters of the power conversion system are determined through the efficiency model to ensure that the power conversion system operates in the high-efficiency range, thereby improving the system's energy conversion efficiency and operational stability.

[0056] It is understood that the power allocation method of the energy storage system in this application is applicable to any energy storage system application scenario. For example, the method of this application can be used in an energy storage system for charging electric vehicles. In this scenario, the energy storage system needs to provide charging services for multiple electric vehicles simultaneously or at different times, and needs to dynamically allocate power according to the charging needs of the vehicles, while taking into account the operating efficiency of the energy storage system itself and the lifespan of the battery cluster. Figure 1 This is a schematic diagram illustrating an application scenario of a power allocation method for an energy storage system provided in an embodiment of this application, such as... Figure 1 As shown, the energy storage system includes n battery clusters, m PCSs and an energy management system. The method of this application is executed by the energy management system of the energy storage system.

[0057] In this electric vehicle charging scenario, the existing power allocation method only roughly allocates power, which can easily lead to problems such as low efficiency of the PCS due to unreasonable operating parameters and uneven charging and discharging of each battery cluster during the charging process, thereby affecting the vehicle charging efficiency and the stability of the energy storage system.

[0058] When applying the method of this application, the energy management system first obtains the target power corresponding to the charging demand of electric vehicles and the efficiency model of PCS. Then, based on the efficiency model and the target power, it determines the target operating parameters of PCS (demand quantity and / or power allocation ratio) that are suitable for the current charging demand of the vehicle. Finally, based on the target operating parameters, it generates a power allocation strategy and allocates the target power to each power conversion system according to the power allocation strategy, thereby completing the dynamic power allocation during the vehicle charging process.

[0059] In the above process, by relying on the efficiency model of the power conversion system to match the real-time target power, the number of PCS put into operation and the power allocation ratio can be accurately determined. This ensures that the PCS operates in the high-efficiency range under different power requirements of vehicle charging, effectively reducing energy conversion losses under different power conditions, avoiding the problem of inefficient equipment operation caused by traditional extensive power allocation, thereby improving the overall energy utilization efficiency of the energy storage system, and thus stably ensuring the vehicle charging efficiency and the economic operation of the energy storage system.

[0060] It should be understood that, in the above process, the implementing entity of the method of this application can also be a battery management system, PCS, or other equipment in the energy storage system, or a cloud server; this embodiment does not limit this. Furthermore, the application scenarios of the method of this application can also be other scenarios, such as grid-side energy storage for peak shaving, distributed photovoltaic-supported energy storage, and microgrid energy storage scenarios; this embodiment does not limit this.

[0061] The following detailed description, with reference to the accompanying drawings and using any electronic device as the execution subject, outlines some embodiments of the state of charge calibration method of this application. Where the embodiments do not conflict, the following embodiments and features thereof can be combined with each other.

[0062] This application provides a power distribution method for an energy storage system. Figure 2 A flowchart illustrating a power allocation method for an energy storage system provided in this application embodiment. Figure 1 ,like Figure 2 As shown, the method in this application embodiment includes:

[0063] S201. Based on the target power to be allocated and the efficiency model of the power conversion system, determine the target operating parameters of the power conversion system.

[0064] The target operating parameters include the required quantity and / or power allocation ratio of the power conversion system.

[0065] In this embodiment, the energy storage system includes battery clusters and a power conversion system. The power conversion system is used to convert the direct current (DC) power from the battery clusters into alternating current (AC) power or vice versa. The target power to be allocated in this application is divided into two application scenarios, corresponding to different system topologies and device correspondences: In the first scenario, the target power is the externally requested power received by the entire energy storage system. In this scenario, the energy storage system includes multiple battery clusters and multiple power conversion systems, and the battery clusters and power conversion systems can adopt a one-to-one correspondence architecture. In the second scenario, the target power is the cluster-level allocated power corresponding to a single battery cluster. In this scenario, a single battery cluster can correspond to multiple power conversion systems, with multiple power conversion systems jointly carrying the output / input power of the battery cluster. In this case, the power conversion system can specifically be an independent device or a conversion module within an independent device.

[0066] In practical applications, for the first scenario mentioned above, a single power conversion system paired with a battery cluster can also integrate multiple conversion modules, and this application does not limit this.

[0067] In this embodiment, the efficiency model is used to indicate the energy conversion efficiency of the power conversion system under different output power conditions. Specifically, in this embodiment, the electronic device obtains the efficiency model through the following process: First, it collects the operating data of the PCS under different output power conditions, including key parameters such as input power, output power, and power loss. In this embodiment, the operating data is obtained through offline acquisition plus online supplementation. Offline acquisition involves simulating different output power conditions (covering 0%-100% of the PCS's rated power, divided into 5% power intervals) in a laboratory environment before the PCS leaves the factory, and collecting the input power P under each condition. in Output power P out Calculate the energy conversion efficiency η=P under various operating conditions. out / P in This forms the initial running dataset. During the actual operation of the PCS, the electronic equipment collects its input power, output power, and power loss in real time, removes abnormal data caused by equipment failures and voltage fluctuations (such as sudden changes in power loss or abnormal input-output power ratios), and supplements it to the initial running dataset to ensure the completeness and accuracy of the dataset.

[0068] Secondly, the collected operational data was filtered and noise-reduced. Outlier data (data deviating more than three standard deviations from the dataset mean) was removed using the 3σ criterion. Missing data was then supplemented using linear interpolation to obtain the effective operational dataset. Finally, based on the effective operational dataset, a third-order polynomial fitting algorithm was used to establish an efficiency model, with the objective function being η = aP. 3 +bP 2+cP+d, where η is the PCS energy conversion efficiency, P is the PCS output power, and a, b, c, and d are fitting coefficients. The fitting process aims to minimize the sum of squared errors (i.e., min∑(η_measured - η_fitted)). 2 With the objective of obtaining the fitting coefficients through the least squares method, the resulting efficiency model accurately reflects the energy conversion efficiency of the PCS under different output powers, providing data support for determining the target operating parameters of the PCS.

[0069] It should be understood that in practical applications, the above-mentioned model generation process can be flexibly adjusted according to the actual scenario. For example, after generating the initial efficiency model, a model correction step can be added. The electronic device collects the actual operating data of the PCS in real time, compares the actual energy conversion efficiency with the model's predicted efficiency, and calculates the deviation value Δη = |η_measured - η_fitted|. If the deviation value Δη > the preset deviation threshold (e.g., Δη > 2%), then based on the latest collected valid data, the fitting coefficients a, b, c, and d are iteratively corrected using the gradient descent method to ensure that the efficiency model always fits the actual operating state of the PCS. In addition, the standard efficiency model provided by the PCS manufacturer can be directly adopted, and then fine-tuned and optimized by combining it with the actual operating data on site. Alternatively, a neural network algorithm (such as a BP neural network) can be used to replace the third-order polynomial fitting algorithm, and an efficiency model can be generated based on a large amount of operating data. This application does not limit this.

[0070] It should be understood that, whether a power conversion system is used as an independent power conversion device or as a conversion module within an independent device, the same data acquisition, data processing, and fitting modeling process described above can be used to obtain the corresponding efficiency model, so as to adapt to the power parameter calculation and efficiency optimization needs at different levels.

[0071] In this embodiment, the target operating parameters include the required number of PCS and the power allocation ratio. The electronic device determines the required number of PCS and the corresponding power allocation ratio of the number of PCS based on the efficiency model and the target power.

[0072] More specifically, based on an efficiency model, the electronic equipment determines the efficient operating range of the power conversion system and obtains the optimal output power value of the power conversion system within the efficient operating range; based on the ratio of the target power to the optimal output power value of the power conversion system, the required number of power conversion systems to be put into operation is determined; if the target power needs to be shared by multiple power conversion systems, the power allocation ratio of each power conversion system is determined according to the power balance principle.

[0073] It should be understood that the high-efficiency operating range of a PCS refers to the output power range determined based on the efficiency model, where the PCS energy conversion efficiency is not lower than a preset efficiency threshold (such as 90%). This range ensures that the PCS operates with minimal energy loss and maximum conversion efficiency, and is the core range that balances the stability of PCS operation and energy utilization efficiency. The optimal output power value of the PCS is the output power value with the highest energy conversion efficiency within the high-efficiency operating range. If multiple power values ​​correspond to the same maximum efficiency, the power value in the middle of the high-efficiency operating range is selected as the optimal output power value to ensure the stability of PCS operation and avoid efficiency fluctuations caused by the power being at the edge of the range.

[0074] In this embodiment, the electronic device calls the generated efficiency model, extracts the output power data of all energy conversion efficiencies ≥ a preset efficiency threshold from the efficiency model, and defines the power range corresponding to this part of the data as the high-efficiency operating range. In this embodiment, the preset efficiency threshold is a configurable parameter, which is determined by combining the energy utilization efficiency target of the energy storage system (such as the overall system efficiency ≥ 90%) and the rated efficiency parameter of the PCS, and calculating it by ηthreshold = ηsystem target × 0.95 (i.e., 95% of the system target efficiency).

[0075] In this embodiment, the electronic device first obtains the optimal output power value of the PCS (e.g., the optimal output power of a single PCS is 50kW), and then calculates the ratio of the target power to the optimal output power value. If the ratio is an integer (e.g., the target power is 150kW, 150÷50=3), then the required quantity is that integer (3). If the ratio is not an integer (e.g., the target power is 120kW, 120÷50=2.4), then the required quantity is determined by rounding up (3), ensuring that the total optimal output power of the PCS put into operation can cover the target power, while avoiding no-load losses caused by too many PCS being put into operation. If the target power is less than the optimal output power value of a single PCS (e.g., the target power is 30kW, and the optimal output power is 50kW), then the required quantity is determined to be 1 unit, ensuring that a single PCS operates within the high-efficiency operating range.

[0076] In this embodiment, the determination of the power allocation ratio follows the principles of power balance and optimal efficiency. Specifically, if the target power needs to be shared by multiple PCSs, the target power is evenly distributed to each operating PCS, so that the allocated power of each PCS is close to or equal to the optimal output power value, ensuring that all PCSs operate in the high-efficiency operating range and maximizing the overall energy conversion efficiency. If the target power only requires one PCS, and the target power is equal to the optimal output power value of that PCS, then all the target power is allocated to that PCS. If the target power is less than the optimal output power value of that PCS, then all the target power is allocated to that PCS. In this case, the PCS still operates within the high-efficiency operating range (since the high-efficiency operating range is a range), and no additional adjustment is required, thus satisfying the target power requirement while ensuring the efficient operation of the PCS.

[0077] In practical applications, the preset efficiency thresholds used to determine the high-efficiency operating range can be flexibly adjusted according to the actual scenario (e.g., the threshold can be appropriately lowered during grid off-peak hours and raised during peak hours); the selection of the optimal output power value can also be replaced by the average power value within the high-efficiency operating range, without being limited to the power value corresponding to the highest efficiency; the calculation of the required quantity can also be fine-tuned in combination with the rated power and operating losses of the PCS (e.g., when the ratio is close to an integer, it can be combined with the no-load loss to choose whether to round up); the power allocation ratio can also adopt on-demand allocation instead of balanced allocation (e.g., according to the actual efficiency differences of each PCS, allocate slightly more power to the PCS with slightly higher efficiency), and this application does not limit this.

[0078] Furthermore, in practical applications, the target operating parameters may only include the required number of PCS or only the power allocation ratio of the PCS, without needing to include both simultaneously. The specific choice can be flexibly made according to the actual operating scenario of the energy storage system. For example, when only the required number of PCS is determined (such as when the energy storage system has a preset fixed power allocation rule, there is no need to further refine the allocation ratio; it is only necessary to match a sufficient number of PCS according to the target power), the electronic equipment can directly determine the optimal output power value of the PCS based on the efficiency model, and then calculate and determine the required number of PCS to be put into operation by using the ratio of the target power to the optimal output power value. Subsequently, the power allocation can be completed according to the preset fixed ratio (such as equal distribution), without the need to calculate the allocation ratio separately. As another example, when only the power allocation ratio of the PCS is determined (such as when the energy storage system has a fixed number of PCS to be put into operation, there is no need to adjust the number of operating PCS; it is only necessary to dynamically adapt to the target power), the electronic equipment can determine the high-efficiency operating range of each PCS based on the efficiency model, and, in combination with the target power, directly determine the power allocation ratio of each PCS already put into operation according to the principle of optimal efficiency, without the need to calculate the required number of new or reduced PCS. This application does not impose any limitations on this.

[0079] In this embodiment, when determining the target operating parameters, the electronic device closely integrates the PCS efficiency model to ensure that the PCS always operates within its high-efficiency operating range, minimizing energy conversion losses and laying the foundation for improving the overall energy utilization efficiency of the energy storage system. Simultaneously, by dynamically calculating the required number of PCS, the device avoids the problems of idle losses due to excessive PCS deployment and insufficient deployment to cover the target power, achieving rational utilization of PCS resources. Furthermore, determining the power allocation ratio according to the principle of balance ensures efficient collaborative operation of each PCS while adapting to dynamic changes in the target power, balancing the rationality of power allocation with the stability of system operation. This also simplifies the parameter determination process, facilitating practical applications and adapting to dynamic power demand scenarios such as electric vehicle charging.

[0080] S202. Based on the target operating parameters, generate a power allocation strategy and allocate the target power to each power conversion system according to the power allocation strategy.

[0081] In this embodiment, the power allocation strategy relies on target operating parameters (number of PCS required, power allocation ratio) and combines them with the actual operating conditions of the energy storage system to form a complete control scheme, realizing power matching and coordinated operation between the power conversion system and the battery cluster: on the one hand, it relies on the target operating parameters to clarify the number of power conversion systems in operation and the power quota; on the other hand, it combines the system operating conditions to allocate the power tasks to be allocated to the battery clusters, so that the power conversion system and the battery clusters can adapt to each other, ensuring the efficient operation of the power conversion system while taking into account the overall charging and discharging balance of the battery clusters.

[0082] As mentioned above, as one possible implementation, the target power is the requested power received by the energy storage system, which is the total power requirement that the energy storage system needs to achieve in the current operating scenario. For the aforementioned electric vehicle charging energy storage scenario, the target power is the total charging power that the energy storage system needs to provide to meet the charging needs of multiple electric vehicles simultaneously or at different times. It will change dynamically with the number of electric vehicles connected, the remaining power of each vehicle, and the charging rate requirements.

[0083] At this point, the electronic device determines the target battery cluster based on the charging / discharging priority and required quantity of each battery cluster in the energy storage system; subsequently, it determines the power allocation strategy based on the power allocation ratio. The power allocation strategy is used to allocate the target power to each target battery cluster.

[0084] In this embodiment, the charging and discharging priority is a rule used to regulate the charging and discharging sequence of each battery cluster. The core is to reasonably allocate charging and discharging resources according to the differences in the operating status of the battery clusters, so as to avoid excessive wear and tear on some battery clusters and idle battery clusters, and ensure the overall operational balance of the energy storage system.

[0085] Specifically, in this embodiment, the electronic device generates a charging and discharging priority for each battery cluster based on the state of charge of each battery cluster; wherein, the discharging priority of battery clusters with high state of charge is higher than that of battery clusters with low state of charge, and the charging priority of battery clusters with low state of charge is higher than that of battery clusters with high state of charge.

[0086] More specifically, electronic devices collect the SOC data of each battery cluster in real time (i.e., the percentage of remaining capacity of each battery cluster relative to the total capacity), and sort the SOC values ​​of all battery clusters in ascending or descending order. In discharge scenarios (such as when electric vehicles have high charging demand and the energy storage system needs to discharge to supply power), battery clusters with high SOC values ​​are prioritized and given priority in discharging power. Battery clusters with lower SOC values ​​have lower discharge priority to avoid damage caused by over-discharging of low-SOC battery clusters. In charging scenarios (such as charging during off-peak hours or when vehicle charging demand is low), battery clusters with low SOC values ​​are prioritized and given priority in charging power. Battery clusters with higher SOC values ​​have lower charging priority to prevent overcharging of high-SOC battery clusters, thereby achieving a more balanced SOC among battery clusters and extending the overall lifespan of the battery clusters.

[0087] In this embodiment, the electronic device receives power grid dispatch instructions or load power demand and determines the target power based on the power grid dispatch instructions or load power demand.

[0088] More specifically, when the scenario involves charging electric vehicles, the load's power demand is the charging demand of each electric vehicle. Electronic devices (such as energy management systems) receive charging requests from the BMS of each electric vehicle in real time. The charging requests include information such as the vehicle's remaining power, target charging amount, and estimated charging time. The electronic devices aggregate and calculate the charging demands of all connected vehicles, and combine this with grid dispatch instructions (such as the maximum power limit allowed by the grid for the energy storage system to output, and off-peak / peak power limits), eliminating the demand that exceeds grid constraints and the energy storage system's own capacity. Finally, a target power for the energy storage system that is adapted to the current scenario is generated, ensuring that the target power meets both the vehicle charging demand and the grid operation requirements.

[0089] It should be understood that when the electronic device is an EMS (Electric Power Management System), the EMS directly interacts with the grid dispatch terminal, the electric vehicle BMS (Battery Management System), and the local monitoring terminal of the energy storage system to obtain grid dispatch instructions or load power demand. Based on these instructions, it generates a target power for use in the power allocation process. When the electronic device is a BMS, the BMS can first receive grid dispatch instructions forwarded by the EMS, and simultaneously collect charging demand information from each electric vehicle. Combining this with its own collected battery cluster operating data, the BMS assists in generating the target power, which is then fed back to the energy management system for final confirmation. When the electronic device is a cloud server, the cloud server can remotely receive relevant instructions and demand information sent by the grid dispatch platform and the electric vehicle charging management platform. After remotely calculating and generating the target power, it sends it to the local control equipment (such as the EMS or BMS) of the energy storage system, where the local control equipment executes subsequent power allocation operations to ensure accurate implementation of the target power.

[0090] In practical applications, in addition to generating the target power by receiving grid dispatch instructions or load power demand, users can also directly input the target power value through the local operation terminal of the energy storage system (such as maintenance personnel manually setting the total output power of the energy storage system according to on-site operation requirements); or electronic devices can generate the target power based on historical operating data, such as predicting the target power for the current period based on the electric vehicle charging demand and grid dispatch patterns during the same period in the past week. This application does not limit this.

[0091] In practical applications, in addition to obtaining the charging and discharging priority based on the SOC of the battery clusters, users can also directly input preset priority rules (such as maintenance personnel manually setting the charging and discharging priority of each battery cluster based on the aging degree and health status of the battery clusters); or the electronic device can combine the State of Health (SOH) of the battery clusters with the SOC to determine the priority. For example, under the same SOC, the battery cluster with a better health status has a higher charging and discharging priority. This application does not limit this.

[0092] In this embodiment, the method of generating the target power takes into account both the constraints of grid dispatching and the actual power demand of the load (such as electric vehicles). This avoids the situation where the target power is too high, leading to grid overload and increased energy storage system losses, or the target power is too low, failing to meet the load demand, thus ensuring the rationality and practicality of the target power. Furthermore, the method of generating charging and discharging priorities based on the SOC ranking of battery clusters does not require complex calculation logic and can quickly achieve balanced dispatching of each battery cluster. This can prevent some battery clusters from being overcharged and discharged and others from being idle, reducing battery cluster losses, extending battery life, and ensuring the stability of the energy storage system's power output, providing a reliable basis for the generation of subsequent power allocation strategies.

[0093] Based on the charging / discharging priority ranking and target power obtained from the above process, the electronic device first calls the charging / discharging priority ranking results of each battery cluster, determines the target battery cluster in combination with the charging or discharging scenario, and divides the total target power according to the principle of equal distribution. At the same time, the allocation results are corrected in combination with the maximum allowable charging / discharging power of each battery cluster to determine the cluster-level allocation power of each target battery cluster: If it is a discharging scenario (such as electric vehicle charging, energy storage system discharging power supply), battery clusters with high SOC and high discharging priority are selected as target battery clusters first, and their cluster-level allocation power is determined according to the maximum allowable charging / discharging power of each target battery cluster; if it is a charging scenario (such as charging during off-peak hours), battery clusters with low SOC and high charging priority are selected as target battery clusters first, and their cluster-level allocation power is determined according to the maximum allowable charging / discharging power of each target battery cluster.

[0094] Based on this, the electronic equipment calls the target operating parameters determined by S201 to clarify the number of power conversion systems to be put into operation (e.g., 3 units) and the power allocation ratio. Then, based on the power allocation ratio, it determines the basic power allocation value of each power conversion system (e.g., 50kW per unit, corresponding to a target power of 150kW). Then, based on the cluster-level allocation power of each target battery cluster, it matches each power conversion system to the corresponding target battery cluster, thus completing the targeted allocation of power.

[0095] For example, assuming a target power of 150kW, step S201 determines that three power conversion systems will be deployed, with each system allocated 50kW. Based on charging / discharging priorities, three target battery clusters are selected: cluster A, cluster B, and cluster C. The maximum allowable power of each battery cluster is no less than 50kW. Following a one-to-one correspondence rule, the three power conversion systems are assigned to clusters A, B, and C respectively, ensuring that the 50kW output power of each system does not exceed the power limit of its corresponding battery cluster. If the maximum allowable power of cluster A is only 40kW, which is less than the allocated power of a single power conversion system, the system will trigger a power anomaly warning and re-select target battery clusters or adjust the power allocation value of the power conversion systems.

[0096] After all PCS and battery clusters are matched, the operating status and actual working power of each PCS, as well as the charging and discharging power and matching relationship of each battery cluster, are determined to form a power allocation strategy that can be directly executed.

[0097] Furthermore, the electronic devices will distribute the generated power allocation strategy to the PCS of the energy storage system and the control module corresponding to each battery cluster, control the PCS to operate according to the predetermined state and power parameters, and drive each battery cluster to perform charging and discharging actions according to the allocated power, so as to ensure that the power allocation strategy is implemented and realize precise power scheduling of the energy storage system.

[0098] In practical applications, in addition to distributing the total target power equally, a differentiated distribution method can also be adopted based on factors such as the health status of the battery clusters and historical operating loads. At the same time, the selection rules and power correction conditions of the target battery clusters can be dynamically adjusted according to the on-site working conditions. This application does not impose any restrictions on these methods.

[0099] Based on this, if the power conversion system includes multiple conversion modules, after completing the cluster-level allocation described above, intra-cluster allocation can be further performed. That is, for each target battery cluster, the electronic equipment determines the cluster-level allocated power of the target battery cluster based on the power allocation ratio; determines the target operating parameters of the conversion module based on the cluster-level allocated power and the efficiency model of the conversion module; determines the power allocation strategy of the target battery cluster based on the target operating parameters of the conversion module; and uses the power allocation strategy of the target battery cluster to allocate the cluster-level allocated power to the corresponding number of target conversion modules.

[0100] Specifically, in this embodiment, cluster-level allocation uses a power equalization method to complete the total power division. The efficiency model used by the conversion module has the same construction, data processing, and fitting process as the power conversion system efficiency model described above, and will not be repeated here. After completing the cluster-level allocation, the cluster-level allocated power of a single target battery cluster is used as the input parameter. Combined with the conversion module efficiency model, the number of modules to be put into operation and the power allocation ratio are determined, and the cluster-level allocated power is distributed to each target conversion module according to the equalization rule.

[0101] In practical applications, in addition to the power distribution method of equal distribution, differentiated power distribution can also be carried out by combining the real-time operating efficiency of each conversion module and the aging status of the equipment, giving priority to allocating power to the module with better operating efficiency. This application does not limit this.

[0102] In the above process, cluster-level allocation divides the total power of the energy storage system among different battery clusters, ensuring balanced operation of all battery clusters based on charging / discharging priorities and power limits. Intra-cluster allocation performs fine-grained scheduling of conversion modules within a single power conversion system, relying on module efficiency models to ensure that all types of converter equipment operate in their high-efficiency range. The two-level allocation works together to optimize the system's operating state from both the battery and converter equipment sides, extending battery life while minimizing overall system energy loss.

[0103] As another possible implementation, the target power is the cluster-level allocated power received by the target battery cluster in the energy storage system. The cluster-level allocated power is determined based on the requested power received by the energy storage system, and the target battery cluster corresponds to multiple power conversion systems. In this scenario, the power conversion system can be a standalone device or a conversion module integrated within a device.

[0104] At this point, the electronic equipment determines the target conversion system to participate in power allocation from multiple power conversion systems based on the demand quantity indicated by the target operating parameters; a power allocation strategy is generated based on the power allocation ratio; the power allocation strategy is used to allocate cluster-level power to each target conversion system.

[0105] Specifically, the electronic equipment first combines the cluster-level power allocation with the optimal output power of the power conversion system to calculate the number of devices that need to be put into operation. Then, it determines the power allocation of a single device according to the power equalization principle, and finally generates the corresponding power allocation strategy to distribute the total power of a single cluster to each target conversion system.

[0106] In practical applications, in addition to the equal distribution mode, the power ratio can also be flexibly adjusted according to the load capacity and operating efficiency of each power conversion system to achieve differentiated distribution. This application does not limit this.

[0107] In this embodiment, the cluster-level power of a single battery cluster is used as the allocation object. Based on the efficiency model, the equipment to be put into operation is selected and the power is reasonably allocated. This allows multiple power conversion systems connected to the same battery cluster to operate stably in the high-efficiency range, fully releasing the load-carrying capacity of a single battery cluster and effectively improving the power output efficiency and operational stability of a single cluster device.

[0108] The method provided in this embodiment calculates the corresponding target operating parameters based on the efficiency model of the power conversion system, clarifies the number of power conversion systems in operation and the power allocation ratio, ensuring that the power conversion system always operates within its high-efficiency operating range, effectively reducing power loss during energy conversion. Power allocation based on these parameters optimizes operating efficiency at the converter equipment level, thereby effectively improving the overall energy utilization efficiency of the energy storage system.

[0109] This application also provides an embodiment of a power allocation method for an energy storage system, which provides a more detailed description of the process of generating a power allocation strategy during cluster-level allocation. Figure 3 A flowchart illustrating a power allocation method for an energy storage system provided in this application embodiment. Figure 2 ,like Figure 3 As shown, the method in this embodiment includes:

[0110] S301. Obtain the constraints for power allocation.

[0111] The constraints include power adjustment rate constraints and / or multi-dimensional security constraints.

[0112] In this embodiment, the power adjustment rate constraint is a constraint rule used to limit the rate of power change. Its core purpose is to avoid rapid power fluctuations from impacting the battery cluster and PCS, and to ensure stable equipment operation. The multi-dimensional safety constraint is a comprehensive safety rule covering the battery cluster, PCS and the entire energy storage system. Its core purpose is to limit the operating boundaries of each device, prevent the device from being damaged due to exceeding the operating limits, and ensure the overall operational safety of the energy storage system.

[0113] Specifically, in this embodiment, the electronic device dynamically determines the maximum allowable charge / discharge power of each battery cluster based on the operating status parameters of each battery cluster, and determines the power adjustment rate constraint in conjunction with the hardware characteristics of the PCS. The power adjustment rate constraint indicates the maximum change in the charge / discharge power of the battery cluster and / or the output power of the PCS per unit time.

[0114] More specifically, the electronic equipment collects the operating status parameters of each battery cluster in real time, including SOC, SOH, temperature, and voltage. Combined with the factory parameters of the battery cluster (such as the maximum charge / discharge rate), it dynamically calculates the maximum allowable charge / discharge power of each battery cluster under the current state. The calculation method is: Pmax = C × K × U, where Pmax is the maximum allowable charge / discharge power of the battery cluster, C is the rated capacity of the battery cluster, K is the allowable charge / discharge rate under the current state, and U is the real-time total voltage of the battery cluster. At the same time, it obtains the hardware characteristic parameters of the PCS, including the PCS power regulation response time τ (unit: s) and the maximum allowable power change ΔPmax (unit: kW). The specific value of the power adjustment rate constraint is determined as v = ΔPmax / τ (unit: kW / s), that is, the maximum change in the charge / discharge power of the battery cluster and the output power of the PCS per unit time must not exceed v. For example, if the PCS ΔPmax = 20kW and τ = 1s, then the power adjustment rate constraint v = 20kW / s, which means that the change in the charge / discharge power of the battery cluster and the output power of the PCS per second must not exceed 20kW to avoid the impact caused by sudden power changes.

[0115] In the above process, the electronic device determines the allowable charge and discharge rate based on the current SOC range of the battery cluster, and then calculates the theoretical maximum charge and discharge power by combining the rated capacity of the battery cluster and the voltage of each individual cell. Based on the battery cluster temperature, SOH and real-time total voltage, a correction coefficient is introduced to obtain the actual maximum allowable charge and discharge power under the current state. Subsequently, the smaller value between the actual maximum allowable charge and discharge power of each battery cluster and the allowable power change rate of the PCS is taken as the upper limit of the power adjustment rate of the battery cluster and the corresponding PCS, thus forming the overall power adjustment rate constraint.

[0116] In practical applications, the process of determining the power adjustment rate constraint described above can be flexibly adjusted. For example, the standard power adjustment rate limit provided by the battery cluster and PCS manufacturers can be directly adopted without real-time calculation; the dynamically calculated adjustment rate can also be fine-tuned by combining the operating scenarios of the energy storage system (such as appropriately increasing the adjustment rate in electric vehicle charging scenarios and decreasing the adjustment rate in grid peak shaving scenarios); or the process of determining the adjustment rate constraint can be optimized by fitting algorithms based on historical operating data. This embodiment does not limit this.

[0117] Specifically, in this embodiment, the electronic device determines multi-dimensional safety constraints based on the operating status parameters of each battery cluster and the rated operating parameters of the PCS, combined with the performance of the energy storage system. These multi-dimensional safety constraints indicate the operating status limits and power limits for the battery clusters and PCS participating in power distribution.

[0118] More specifically, the multi-dimensional safety constraints are divided into two parts: battery cluster safety constraints and PCS safety constraints. For the battery cluster, the electronic device determines the SOC limit (e.g., SOC must not be lower than 5% and higher than 95%), temperature limit (e.g., operating temperature must not exceed -20℃ to 60℃), voltage limit (e.g., single cell voltage must not exceed 3.0V to 3.6V) and maximum charge / discharge power limit as safety constraints for the battery cluster based on the collected operating status parameters such as SOC, temperature, and voltage, combined with the battery cluster safety operation standards. The maximum charge / discharge power limit is calculated according to the following formula: Current maximum charge / discharge power = rated charge / discharge power × temperature correction coefficient × SOH correction coefficient, where the temperature correction coefficient and SOH correction coefficient are selected according to preset rules.

[0119] For the PCS, the electronic equipment obtains the rated operating parameters of the PCS, including rated power, rated voltage, rated current, etc., and combines them with the overall operating performance of the energy storage system (such as the maximum output power of the system and grid access constraints) to determine the operating state limits (such as not being able to operate under overload for a long time and not being able to operate under abnormal voltage) and power limits (such as the output power not exceeding ±10% of the rated power) of the PCS as safety constraints for the PCS. The safety constraints of the battery cluster and the PCS are integrated to form a multi-dimensional safety constraint that covers the safety requirements of the equipment operation in all scenarios.

[0120] In the above process, the electronic equipment dynamically tightens or relaxes the voltage and power limits according to the degree to which the current temperature of the battery cluster deviates from the optimal operating range, and reduces the maximum allowable charge and discharge power according to the degree of SOH decay; for the PCS, the allowable overload ratio and duration are dynamically adjusted according to the current grid voltage deviation, module temperature and cumulative running time, forming a multi-dimensional safety constraint that adapts to the operating state, rather than a fixed threshold.

[0121] In practical applications, alternative solutions can be adopted for the process of determining multi-dimensional safety constraints. For example, industry standards or preset safety limits of the energy storage system can be directly used without real-time calculation based on operating status parameters. Alternatively, the safety limits can be dynamically adjusted according to the aging degree of the energy storage system and the operating environment (such as appropriately reducing the temperature limit in high-temperature environments). Grid-side safety constraints (such as grid voltage fluctuation constraints) can also be added to enrich the coverage of multi-dimensional safety constraints. This embodiment does not limit this aspect.

[0122] Furthermore, in practical applications, the constraints may include only the power adjustment rate constraint or only the multi-dimensional safety constraint, without needing to acquire both constraints simultaneously. If only the power adjustment rate constraint is acquired, the target operating parameters and charging / discharging priorities will only be updated based on this constraint, with a focus on ensuring the stability of power adjustment; if only the multi-dimensional safety constraint is acquired, the subsequent updates will only be based on this constraint, with a focus on ensuring the safety of equipment operation. This application does not impose any limitations on this.

[0123] In this embodiment, simultaneously acquiring power adjustment rate constraints and multi-dimensional safety constraints enables a dual guarantee of safe and stable power allocation. Specifically, the multi-dimensional safety constraints start from the hardware operating boundaries to prevent damage to battery clusters and PCS due to exceeding limits, ensuring the safety of core equipment in the energy storage system and reducing the risk of equipment failure. The power adjustment rate constraints start from the operation process to prevent power surges from impacting the equipment, ensuring a stable power allocation process, reducing equipment wear, and extending equipment lifespan. The combination of these two constraints provides a comprehensive and reliable basis for updating subsequent target operating parameters and charging / discharging priorities, ensuring that the subsequently generated power allocation strategy is both safe and feasible, further improving the safety and stability of the energy storage system operation.

[0124] S302. Based on the power allocation constraints, update the target operating parameters and charge / discharge priorities.

[0125] In this embodiment, the electronic device updates the target operating parameters and charge / discharge priorities based on multi-dimensional safety constraints and power adjustment rate constraints. Specifically, the electronic device makes corrections based on multi-dimensional safety constraints to determine the corrected target operating parameters and charge / discharge priorities. The corrections include: limiting the charge / discharge power of each battery cluster to below the power limit, limiting the target operating parameters to within the rated operating parameter range, and removing battery clusters whose operating states do not meet the multi-dimensional safety constraints and canceling their corresponding charge / discharge priorities, thus obtaining the corrected target operating parameters and charge / discharge priorities. Based on the power adjustment rate constraints, the corrected target operating parameters and charge / discharge priorities are adjusted to determine the updated target operating parameters and charge / discharge priorities. The adjustments include: matching the target operating parameters to the limit requirement of the maximum power change per unit time, and adapting the charge / discharge priorities of the remaining normal battery clusters to the power change rate, thus obtaining the updated target operating parameters and charge / discharge priorities.

[0126] More specifically, the electronic device first calls the multi-dimensional safety constraints obtained by S301 to correct the determined initial target operating parameters and charging / discharging priorities: it checks the operating status of each battery cluster one by one. If the SOC of a battery cluster exceeds the safety limit, the temperature is too high, or the voltage is abnormal, the battery cluster is removed, its charging / discharging priority is canceled, and it will no longer participate in subsequent power allocation. At the same time, it checks the required number of PCS and the power allocation ratio in the initial target operating parameters. If the power allocation value of a PCS exceeds its rated power limit, the power allocation ratio of the PCS is adjusted to ensure that it does not exceed the rated operating parameter range. If the adjustment still does not meet the requirements, the required number of PCS is reduced until the operating parameters of all PCS meet the safety constraints, and the corrected target operating parameters and charging / discharging priorities are obtained.

[0127] Subsequently, the electronic equipment adjusts based on the power adjustment rate constraint: the power change corresponding to the corrected target operating parameters is calculated; if the power change per unit time exceeds the constraint limit, the power adjustment amplitude is reduced, and the power allocation ratio of the PCS and the charging and discharging power allocation speed of the battery clusters are adjusted; at the same time, the priority ranking is fine-tuned in combination with the charging and discharging priority of the remaining normal battery clusters, so that the power adjustment rate of the high-priority battery clusters is adapted to the constraint requirements, avoiding damage to the high-priority battery clusters due to excessively rapid power adjustment. Finally, the updated target operating parameters and charging and discharging priorities are obtained, ensuring that both meet the safety requirements and the requirements for stable operation.

[0128] In the above process, the electronic device first calculates the difference between the current target power and the actual output power of the previous cycle to obtain the total power to be adjusted. Then, the power is evenly divided according to a preset time window to obtain the theoretical adjustment rate. If the rate exceeds the upper limit of the constraint, the power allocation step size of each PCS is reduced proportionally, and the charging and discharging power change amplitude of the high-priority battery clusters is reduced simultaneously to ensure that the overall adjustment rate does not exceed the limit. At the same time, the priority ranking is slightly rearranged so that the battery clusters with stronger power adjustment capabilities can bear more changes, ensuring that the priority rules are consistent with the rate constraints.

[0129] In practical applications, the above update process can be replaced by alternative solutions. For example, adjustments can be made first based on the power adjustment rate constraint, and then corrected based on the multi-dimensional safety constraints, without being limited to the order of correction before adjustment. Alternatively, an iterative update method can be used to verify the constraint satisfaction multiple times and gradually optimize the target operating parameters and charging / discharging priorities. Furthermore, depending on the scenario requirements, the adjustment of a certain constraint can be emphasized (e.g., in a safety-priority scenario, the correction process of multi-dimensional safety constraints can be optimized). This embodiment does not impose any restrictions on this.

[0130] Furthermore, in practical applications, if only the power adjustment rate constraint is included, the electronic device will adjust the initial target operating parameters and charging / discharging priorities based solely on this constraint, focusing on ensuring that the power change per unit time does not exceed the limit, without the need for correction of the safety constraints; if only multi-dimensional safety constraints are included, the electronic device will correct the initial parameters and priorities based solely on this constraint, eliminating devices and priorities that do not meet the safety requirements, ensuring operational safety, without the need for power adjustment rate adaptation, and this application does not impose any limitations on this.

[0131] In this embodiment, safety constraints and rate constraints work together to ensure that the updated target operating parameters and charge / discharge priorities meet the equipment's safe operating boundaries, preventing equipment damage, and to ensure a smooth power adjustment process, reducing equipment losses. At the same time, by eliminating abnormal battery clusters and adjusting priorities and operating parameters, the subsequently generated power allocation strategy becomes more feasible and reasonable, adapting to the dynamic operating scenarios of the energy storage system, and further improving the safety, stability, and reliability of the system operation.

[0132] S303. Generate a power allocation strategy based on the updated target operating parameters and charging / discharging priorities.

[0133] In this embodiment, the electronic device calls the updated target operating parameters and charge / discharge priorities obtained in S302, and uses the matching logic between the PCS and the battery cluster mentioned above, combined with the requirements of the constraints, to generate a power allocation strategy.

[0134] Specifically, the electronic equipment first clarifies the number of PCS to be put into operation after the update and the power allocation value of each PCS, ensuring that the power parameters of each PCS meet multi-dimensional safety constraints and adapt to the power adjustment rate requirements. Second, according to the updated charging and discharging priority, the corresponding number of battery clusters are selected in sequence, and the power conversion system is paired with the selected battery clusters one by one to ensure that the charging and discharging power of the battery clusters does not exceed the safety limit, the power adjustment speed meets the rate constraints, and the actual output power of each PCS is still in the high-efficiency operating range. Finally, the operating status of each PCS, the charging and discharging power of each battery cluster and the matching relationship are clarified, and the power adjustment rate requirements are marked to form the final power allocation strategy, ensuring that the strategy meets both the high-efficiency allocation requirements and the constraints of safe and stable operation.

[0135] In this embodiment, the method incorporates power adjustment rate constraints and multi-dimensional safety constraints during the power allocation strategy generation process, achieving a synergistic balance between high efficiency, safety, and stability in power allocation. Specifically, the multi-dimensional safety constraints define safety boundaries for power allocation, effectively preventing damage to battery clusters and PCS due to exceeding limits, reducing the risk of energy storage system failure, and ensuring long-term stable system operation. The power adjustment rate constraints prevent the impact of sudden power fluctuations on equipment, reduce battery cluster polarization and PCS module losses, and extend the service life of core equipment.

[0136] Meanwhile, by updating the target operating parameters and charging / discharging priorities through constraints, the generated power allocation strategy is made to better fit the actual operating state of the equipment, ensuring that the PCS always works in the high-efficiency range and the battery clusters are charged and discharged in a balanced manner, while also taking into account safety and stability. In addition, the flexible adaptation of constraints can be adapted to the needs of different application scenarios, improving the versatility and applicability of the method.

[0137] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0138] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0139] The above embodiments introduce a power distribution method for an energy storage system from the perspective of process flow. The following embodiments introduce a power distribution device for an energy storage system from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0140] This application also provides a power distribution device for an energy storage system, used to implement the method in the above-described method embodiments. Figure 4 This is a schematic diagram of the structure of a power distribution device for an energy storage system provided in an embodiment of this application, as shown below. Figure 4 As shown, in this embodiment, the energy storage system power distribution device may include:

[0141] Processing module 41 is used to determine the target operating parameters of the power conversion system based on the target power to be allocated and the efficiency model of the power conversion system; the target operating parameters include the demand quantity and / or power allocation ratio of the power conversion system;

[0142] The allocation module 42 is used to generate a power allocation strategy based on the target operating parameters and allocate the target power to each power conversion system according to the power allocation strategy.

[0143] In one possible implementation of this application embodiment, the target power is the requested power received by the energy storage system, the power conversion system corresponds one-to-one with the battery clusters in the energy storage system, and the allocation module 42 is specifically used for:

[0144] The target battery cluster is determined based on the charging and discharging priority and required quantity of each battery cluster in the energy storage system.

[0145] Based on the power allocation ratio, a power allocation strategy is determined; the power allocation strategy is used to allocate the target power to each target battery cluster.

[0146] In one possible implementation of this application embodiment, the allocation module 42 is further configured to:

[0147] Based on power allocation constraints, update target operating parameters and charge / discharge priorities; constraints include power adjustment rate constraints and / or multi-dimensional safety constraints.

[0148] In one possible implementation of this application embodiment, the allocation module 42 is specifically used for:

[0149] Based on the operating status parameters of each battery cluster, the maximum allowable charge and discharge power of each battery cluster is dynamically determined, and the power adjustment rate constraint is determined in combination with the hardware characteristics of the power conversion system. The power adjustment rate constraint is used to indicate the maximum change in the charge and discharge power of the battery cluster and / or the output power of the power conversion system per unit time.

[0150] And / or, based on the operating status parameters of each battery cluster and the rated operating parameters of the power conversion system, and in conjunction with the performance of the energy storage system, multi-dimensional safety constraints are determined; multi-dimensional safety constraints are used to indicate the operating status limits and power limits of the battery clusters and power conversion system participating in power distribution.

[0151] In one possible implementation of this application embodiment, the allocation module 42 is specifically used for:

[0152] Based on multi-dimensional safety constraints, corrections are made to determine the corrected target operating parameters and charging / discharging priorities. The corrections include: limiting the charging / discharging power of each battery cluster to below the power limit, limiting the target operating parameters to within the range of rated operating parameters, and removing battery clusters whose operating states do not meet the multi-dimensional safety constraints and canceling their corresponding charging / discharging priorities.

[0153] Based on the power adjustment rate constraint, the corrected target operating parameters and charge / discharge priorities are adjusted to determine the updated target operating parameters and charge / discharge priorities. The adjustments include: making the target operating parameters match the limit requirement of the maximum power change per unit time, and adapting the charge / discharge priorities of the remaining normal battery clusters to the power change rate.

[0154] In one possible implementation of this application embodiment, the power conversion system includes multiple conversion modules; the allocation module 42 is specifically used for:

[0155] For each target battery cluster, the cluster-level power allocation is determined based on the power allocation ratio.

[0156] Based on the cluster-level power allocation and conversion module efficiency model, the target operating parameters of the conversion module are determined;

[0157] Based on the target operating parameters of the conversion module, the power allocation strategy of the target battery cluster is determined; the power allocation strategy of the target battery cluster is used to allocate the cluster-level power to the corresponding number of target conversion modules.

[0158] In one possible implementation of this application embodiment, the target power is the cluster-level allocated power received by the target battery cluster in the energy storage system. The cluster-level allocated power is determined based on the requested power received by the energy storage system, and the target battery cluster corresponds to multiple power conversion systems. The allocation module 42 is specifically used for:

[0159] Based on the demand indicated by the target operating parameters, the target conversion system to participate in power allocation is determined from multiple power conversion systems;

[0160] Based on the power allocation ratio, a power allocation strategy is generated; the power allocation strategy is used to allocate cluster-level power to each target conversion system.

[0161] In one possible implementation of this application embodiment, the processing module 41 is specifically used for:

[0162] Based on the efficiency model, the efficient operating range of the power conversion system is determined, and the optimal output power value of the power conversion system within the efficient operating range is obtained.

[0163] The required number of power conversion systems to be put into operation is determined based on the ratio of the target power to the optimal output power of the power conversion system.

[0164] If the target power needs to be shared by multiple power conversion systems, the power allocation ratio of each power conversion system shall be determined according to the power balance principle.

[0165] In one possible implementation of this application embodiment, the processing module 41 is further configured to:

[0166] Based on grid dispatch instructions or load power demand, the target power of the energy storage system is generated.

[0167] And / or, based on the state of charge of each battery cluster, a charging and discharging priority is generated for each battery cluster; wherein, the discharging priority of battery clusters with high state of charge is higher than that of battery clusters with low state of charge, and the charging priority of battery clusters with low state of charge is higher than that of battery clusters with high state of charge.

[0168] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0169] This application provides an electronic device. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 5 As shown, Figure 5The illustrated electronic device includes at least one processor 51 and a memory 52. ​​The processor 51 and the memory 52 are connected, for example, via a bus 53. Optionally, the electronic device may also include a transceiver 54. It should be noted that in practical applications, the transceiver 54 is not limited to one, and the structure of this electronic device does not constitute a limitation on the embodiments of this application.

[0170] Processor 51 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 51 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0171] Bus 53 may include a pathway for transmitting information between the aforementioned components. Bus 53 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Bus 53 may be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0172] The memory 52 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0173] The memory 52 is used to store computer execution instructions for implementing the scheme of this application, and the execution is controlled by the processor 51. The processor 51 is used to execute the computer execution instructions stored in the memory 52 to implement the content shown in the foregoing method embodiments.

[0174] This application also provides a computer-readable storage medium, which may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Specifically, the computer-readable storage medium stores computer-executable instructions, which are used to implement the methods in the above embodiments.

[0175] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the technical solution of the above method embodiments. Its implementation principle and technical effects are similar, and will not be repeated here.

[0176] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0177] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0178] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A power distribution method for an energy storage system, characterized in that, The method includes: Based on the target power to be allocated and the efficiency model of the power conversion system, the target operating parameters of the power conversion system are determined; the target operating parameters include the demand quantity and / or power allocation ratio of the power conversion system. Based on the target operating parameters, a power allocation strategy is generated, and the target power is allocated to each of the power conversion systems according to the power allocation strategy.

2. The method according to claim 1, characterized in that, The target power is the requested power received by the energy storage system. The power conversion system corresponds one-to-one with the battery clusters in the energy storage system. The generation of a power allocation strategy based on the target operating parameters includes: The target battery cluster is determined based on the charging and discharging priority of each battery cluster in the energy storage system and the required quantity. Based on the power allocation ratio, the power allocation strategy is determined; the power allocation strategy is used to allocate the target power to each of the target battery clusters.

3. The method according to claim 2, characterized in that, The method further includes: Based on the power allocation constraints, the target operating parameters and the charge / discharge priority are updated; the constraints include power adjustment rate constraints and / or multi-dimensional safety constraints.

4. The method according to claim 3, characterized in that, Obtain the constraints for power allocation, including: Based on the operating status parameters of each battery cluster, the maximum allowable charge and discharge power of each battery cluster is dynamically determined, and the power adjustment rate constraint is determined in combination with the hardware characteristics of the power conversion system; the power adjustment rate constraint is used to indicate the maximum change in the charge and discharge power of the battery cluster and / or the output power of the power conversion system per unit time. And / or, based on the operating status parameters of each of the battery clusters and the rated operating parameters of the power conversion system, and in conjunction with the performance of the energy storage system, the multi-dimensional safety constraints are determined; the multi-dimensional safety constraints are used to indicate the operating status limits and power limits of the battery clusters and the power conversion system participating in power distribution.

5. The method according to claim 3 or 4, characterized in that, The power allocation-based constraints update the target operating parameters and the charge / discharge priority, including: Based on the aforementioned multi-dimensional safety constraints, corrections are made to determine the corrected target operating parameters and charging / discharging priorities. The corrections include: limiting the charging / discharging power of each battery cluster to below the power limit, limiting the target operating parameters to within the rated operating parameter range, eliminating battery clusters whose operating states do not meet the multi-dimensional safety constraints, and canceling their corresponding charging / discharging priorities. Based on the power adjustment rate constraint, the corrected target operating parameters and charge / discharge priorities are adjusted to determine the updated target operating parameters and charge / discharge priorities. The adjustment includes: making the target operating parameters match the limit requirement of the maximum power change per unit time, and adapting the charge / discharge priorities of the remaining normal battery clusters to the power change rate.

6. The method according to any one of claims 2-4, characterized in that, The power conversion system includes multiple conversion modules; the generation of a power allocation strategy based on the target operating parameters and the charging / discharging priority includes: For each target battery cluster, the cluster-level power allocation for the target battery cluster is determined based on the power allocation ratio; Based on the cluster-level power allocation and the efficiency model of the conversion module, the target operating parameters of the conversion module are determined; Based on the target operating parameters of the conversion module, a power allocation strategy for the target battery cluster is determined; the power allocation strategy for the target battery cluster is used to allocate the cluster-level power to a corresponding number of target conversion modules.

7. The method according to claim 1, characterized in that, The target power is the cluster-level allocated power received by the target battery cluster in the energy storage system. The cluster-level allocated power is determined based on the requested power received by the energy storage system. The target battery cluster corresponds to multiple power conversion systems. The generation of a power allocation strategy based on the target operating parameters includes: Based on the demand indicated by the target operating parameters, the target conversion system to participate in power allocation is determined from the plurality of power conversion systems; Based on the power allocation ratio, the power allocation strategy is generated; the power allocation strategy is used to allocate the cluster-level allocated power to each of the target conversion systems.

8. The method according to claim 1, characterized in that, The determination of the target operating parameters of the power conversion system based on the target power to be allocated and the efficiency model of the power conversion system includes: Based on the efficiency model, the efficient operating range of the power conversion system is determined, and the optimal output power value of the power conversion system within the efficient operating range is obtained. The required number of power conversion systems to be put into operation is determined based on the ratio of the target power to the optimal output power value of the power conversion system. If the target power needs to be shared by multiple power conversion systems, the power allocation ratio of each power conversion system shall be determined according to the power balance principle.

9. The method according to any one of claims 2-4, characterized in that, The method further includes: The target power is determined based on power grid dispatch instructions or load power demand; And / or, based on the state of charge of each battery cluster, a charge / discharge priority is generated for each battery cluster; wherein, the discharge priority of a battery cluster with a high state of charge is higher than that of a battery cluster with a low state of charge, and the charging priority of a battery cluster with a low state of charge is higher than that of a battery cluster with a high state of charge.

10. An electronic device, characterized in that, It includes at least one processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-9.

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