Energy storage unit direct control method and device based on frequency modulation mileage, medium and product

Abstract

The invention provides an energy storage unit direct control method and device based on frequency modulation mileage, a medium and a product, and the method comprises the following steps: building and updating a heterogeneous battery unit state model of an energy storage power station, and recording the current charge state, health state and historical accumulated frequency modulation mileage of each battery unit by the model; extracting time-frequency domain characteristics of the received automatic power generation control frequency modulation instruction, and decomposing the instruction into a plurality of frequency modulation sub-tasks containing different frequency components in a time dimension according to the time-frequency domain characteristics; calculating a load balancing value corresponding to the historical accumulated frequency modulation mileage, and combining the health state and the current charge state to calculate a comprehensive evaluation score; determining a matched target battery unit according to the score and generating a power execution instruction; and driving the target battery unit to execute the instruction and accumulating the frequency modulation mileage generated by the execution. By implementing the technical scheme provided by the invention, self-adaptive distribution and balanced control of the frequency modulation task can be realized, and the frequency modulation response precision and the operation life of the energy storage system are improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, device, medium and product for direct control of energy storage units based on frequency regulation mileage. Background Technology

[0002] With the construction of new power systems and the increasing penetration of new energy sources, the volatility and randomness of power grid frequencies are increasing, placing higher demands on the regulation capabilities of Automatic Generation Control (AGC). Large-capacity energy storage power stations, with their advantages of fast response and high regulation accuracy, have become an important regulatory resource for participating in the power ancillary services market and ensuring power grid frequency stability. How to achieve precise scheduling of the massive number of battery cells within an energy storage power station to meet high-frequency AGC commands from the power grid while also considering battery life and safety has become a core requirement for improving the operational efficiency and full lifecycle management level of energy storage power stations.

[0003] In existing technologies, when virtual power plants or dispatching systems aggregate multiple electrochemical energy storage power stations for frequency regulation, they typically treat the energy storage power stations as a single entity and control them in a black-box manner. For AGC commands from the grid, they often employ simple average or sequential allocation strategies to distribute them to each battery cluster or cell. For example, the total power demand is directly allocated to all batteries according to their rated capacity. However, this crude control method ignores the objective differences among different battery cells in terms of real-time State of Charge (SOC), State of Health (SOH), adjustable range, and historical cumulative frequency regulation mileage. In actual scenarios involving high-frequency and large-fluctuation AGC frequency regulation commands, existing technologies do not fully consider the heterogeneous characteristics and real-time state differences of battery cells. This non-adaptive power allocation method makes it difficult to ensure a precise match between task requirements and battery capabilities. This often leads to some battery cells aging prematurely due to excessive regulation tasks or operating in conditions that deviate from the healthy state of charge range for a long time. This further exacerbates the consistency dispersion within the battery cluster, posing a risk of rapid degradation of the overall usable capacity of the energy storage power station and a shortened service life throughout its entire life cycle. Summary of the Invention

[0004] In view of this, this application provides a method, device, medium and product for direct control of energy storage units based on frequency regulation mileage, in order to solve the above problems.

[0005] Firstly, a direct control method for energy storage units based on frequency regulation mileage is provided, the method comprising:

[0006] Establish and update the state model of heterogeneous battery cells in the energy storage power station. The state model of heterogeneous battery cells records the current state of charge, health status and historical cumulative frequency regulation mileage of each battery cell.

[0007] In response to the received automatic generation control frequency modulation command, the time-frequency domain features of the automatic generation control frequency modulation command are extracted, and the automatic generation control frequency modulation command is decomposed into multiple frequency modulation sub-tasks containing different frequency components in the time dimension based on the time-frequency domain features.

[0008] Calculate the load balancing value corresponding to the historical cumulative frequency regulation mileage, and combine the load balancing value, health status and current state of charge to calculate the comprehensive evaluation score of each battery cell.

[0009] Based on the comprehensive evaluation score, the target battery cell that matches each frequency modulation subtask is determined from each battery cell, and power execution instructions for controlling the target battery cell are generated.

[0010] The target battery cell is driven to execute a power execution command, and the frequency modulation mileage generated in this execution is added to the target battery cell's historical cumulative frequency modulation mileage.

[0011] The above technical solution establishes and continuously updates the state model of heterogeneous battery cells in the energy storage power station, and decomposes the frequency regulation sub-tasks of different frequencies according to the time and frequency characteristics of the frequency regulation command under the frequency regulation task. This enables different battery cells to be rationally allocated frequency regulation load according to their health status, state of charge and frequency regulation mileage differences, thereby achieving fine allocation and dynamic balance of frequency regulation power among multiple battery cells, avoiding overuse or premature aging of some battery cells, extending the life of the entire station and improving frequency regulation response efficiency.

[0012] Optionally, establish and update the state model of heterogeneous battery cells in the energy storage power station, specifically including:

[0013] A state model of the heterogeneous battery cell is established using the basic physical parameters of each battery cell, including the maximum charge and discharge power.

[0014] The dynamic operating parameters of each battery cell are collected, and the state model of the heterogeneous battery cell is updated in real time using the dynamic operating parameters, including real-time temperature and current state of charge.

[0015] After each frequency modulation subtask is completed, the lifetime statistics record in the heterogeneous battery cell state model is updated based on the cumulative number of cycles, usage days, and historical cumulative frequency modulation mileage of the battery cell.

[0016] The above technical solution establishes a state model using the basic physical parameters of the battery cell and updates the model based on dynamic parameters such as real-time temperature and state of charge, enabling the system to continuously monitor the actual operating status of each battery cell. At the same time, updating the cycle count, usage days, and frequency regulation mileage after each frequency regulation task effectively reflects the battery degradation trend and changes in available capacity, providing accurate data support for subsequent frequency regulation task allocation.

[0017] Optionally, based on time-frequency domain characteristics, the automatic generation control frequency modulation command is decomposed into multiple frequency modulation sub-tasks containing different frequency components in the time dimension, specifically including:

[0018] The number of zero-crossings and the number of power direction changes of the automatic generation control frequency regulation command within a preset time window are counted, and the frequency type of the automatic generation control frequency regulation command is determined based on the number of zero-crossings and the number of power direction changes.

[0019] If the frequency type is determined to be a high-frequency signal that meets the preset high-frequency conditions, then a capture window is constructed with a preset time window length, and the capture window is shifted on the time axis of the automatic power generation control frequency modulation command according to the preset overlap step size. Based on the shifted capture window, multiple short-time frequency modulation sub-tasks are captured in sequence.

[0020] If the frequency type is determined to be a low-frequency signal that meets the preset low-frequency conditions, the automatic power generation control frequency modulation command is divided equally according to the preset fixed number of segments or the preset long time interval to obtain multiple long-time energy type sub-tasks.

[0021] If the frequency type is determined to be a mixed signal, identify the power zero-crossing time in the automatic generation control frequency modulation command, obtain the command segment between two adjacent power zero-crossing times, and construct each command segment into a mixed-type frequency modulation subtask.

[0022] The above technical solution identifies the frequency type by analyzing the number of zero-crossings and the number of power direction changes in the automatic power generation control frequency modulation command. It then uses different decomposition strategies (high-frequency short-time decomposition, low-frequency long-time decomposition, and mixed signal fragmentation) to achieve multi-scale decomposition of the frequency modulation command. This allows the system to allocate battery cells with different response characteristics to matching frequency subtasks, thereby achieving faster dynamic response in high-frequency scenarios and maintaining stable energy output in low-frequency scenarios, thus improving the accuracy and energy efficiency of frequency modulation control.

[0023] Optionally, calculate the load balancing values ​​corresponding to the historical cumulative frequency regulation mileage, specifically including:

[0024] Obtain the statistical distribution characteristics of the historical cumulative frequency regulation mileage of all battery cells participating in frequency regulation within the energy storage power station;

[0025] Calculate the degree of deviation between the historical cumulative frequency modulation mileage of each battery cell and the preset mean index in the statistical distribution characteristics;

[0026] If the historical cumulative frequency regulation mileage is lower than the average indicator, the load balancing value calculated based on the degree of deviation is greater than the preset benchmark value.

[0027] If the historical cumulative frequency regulation mileage is higher than or equal to the average value, the load balancing value is calculated based on the degree of deviation and is less than or equal to the preset benchmark value.

[0028] The above technical solution quantifies the differences in usage load of each unit by statistically analyzing the historical cumulative frequency regulation mileage distribution characteristics of all battery units in the energy storage power station and calculating the degree of deviation of each unit from the mean. Then, the load balancing value is adjusted according to the direction of deviation, so that the frequency regulation task is preferentially allocated to the battery units with lower frequency regulation mileage, thereby balancing the usage frequency of each battery unit, reducing the excessive wear of individual units, and improving the sustainable frequency regulation capability of the entire station.

[0029] Optionally, a comprehensive evaluation score for each battery cell is calculated by combining load balancing values, health status, and current state of charge, specifically including:

[0030] Calculate the absolute value of the difference between the current state of charge and the preset target state of charge, and generate a state of charge preference score based on the absolute value;

[0031] Based on the cycle count and calendar life parameters in the health status, a health score characterizing the aging degree of the battery cell is generated.

[0032] The real-time temperature of the battery cell is obtained, and a temperature deviation score is calculated based on the real-time temperature. The temperature deviation score represents the degree to which the real-time temperature is close to the preset optimal operating temperature range.

[0033] These are preset weighting coefficients assigned to the load balancing value, state of charge preference score, health score, and temperature deviation score, respectively.

[0034] The weighted load balancing value, state of charge preference score, health score, and temperature deviation score are summed to obtain a comprehensive evaluation score.

[0035] The above technical solution, by comprehensively considering multiple factors such as load balancing values, state of charge deviation, health status (cycle count and life parameters), and temperature deviation score, and generating a comprehensive evaluation score through weighted summation, can achieve a quantitative assessment of the current schedulability of each battery cell. This allows the scheduling system to take into account performance, health, and safety when allocating tasks, thereby maintaining the stable operating temperature range and reasonable charge range of each battery cell while ensuring frequency regulation accuracy, and reducing the risk of thermal imbalance and overcharging / discharging.

[0036] Optionally, a target battery cell matching each frequency modulation subtask is determined from the battery cells based on the comprehensive evaluation score, and a power execution command for controlling the target battery cell is generated, specifically including:

[0037] The battery cells are sorted in descending order of their comprehensive evaluation scores.

[0038] Based on the ranking results, select the top-ranked battery cells as target battery cells in sequence until the total adjustable power of all selected target battery cells meets the total power requirement of the frequency modulation subtask.

[0039] Based on the maximum charge / discharge power of each target battery cell and the proportion of the comprehensive evaluation score, the total power requirement of the frequency modulation subtask is allocated to each target battery cell, and corresponding power execution instructions are generated.

[0040] The above technical solution sorts battery cells according to comprehensive evaluation scores and selects the optimal cell to perform frequency regulation tasks. At the same time, it allocates task power according to the maximum charging and discharging power and the score ratio, which can achieve optimal matching and allocation of frequency regulation power requirements. This allows the frequency regulation response to not only meet the power requirements, but also make full use of the differences in battery performance, thereby achieving smooth adjustment of overall output power and maximizing energy utilization.

[0041] Optionally, the method also includes:

[0042] Real-time monitoring of the current operating status of the battery cells and the current task queue;

[0043] When it is detected that the current running state is idle and the current task queue is empty, it is determined whether the current charge state deviates from the preset healthy charge range.

[0044] If the current state of charge deviates from the healthy state of charge range, a charging or discharging command with a power value equal to the preset maintenance power value is generated according to the direction of deviation, and the charging or discharging command is used as an active balancing control command.

[0045] The drive battery cell executes active balancing control commands, and stops executing active balancing control commands when it detects that the current state of charge is in the healthy state of charge range.

[0046] The above technical solution detects whether the state of charge deviates from the healthy range during the idle period of the battery cells and actively triggers charge and discharge maintenance operations. It can automatically perform SOC (State of Charge) balancing under the condition of no external frequency adjustment task, keep each battery cell in a suitable charge range, prevent capacity decay or voltage unevenness caused by long-term quiescence, and thus improve the safety and stability of the system in long-term operation.

[0047] In a second aspect, an electronic device is provided, including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any of the above.

[0048] Thirdly, a computer-readable storage medium is provided that stores instructions which, when executed, perform the method as described in any of the preceding descriptions.

[0049] Fourthly, a computer program product containing instructions is provided, which, when run on a server, causes the server to perform the method described in the first aspect and any possible implementation thereof.

[0050] Understandably, the electronic device provided in the second aspect, the computer-readable storage medium provided in the third aspect, and the computer program product provided in the fourth aspect are all used to execute the method provided in this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0051] In summary, implementing one or more technical solutions provided in this application has at least the following technical effects or advantages:

[0052] By introducing a direct control mechanism for battery cells based on frequency regulation mileage, the frequency regulation control of energy storage power stations no longer relies on the average distribution strategy of a single power command, but achieves refined management based on battery lifespan, energy state, and operating load. By combining time-frequency decomposition and multi-dimensional state modeling, coordination can be achieved between millisecond-level frequency regulation response and hour-level energy regulation, taking into account both dynamic performance and energy balance. By coupling the frequency regulation execution process with the lifespan model, thermal management model, and SOC balance logic, adaptive optimization of the energy storage system under different operating scenarios is realized. In addition, the control logic of this application is scalable and can be adapted to heterogeneous battery arrays containing cells from different manufacturers and of different types, thereby significantly improving the controllability, maintainability, and economic operating efficiency of energy storage power stations, and providing a unified direct control strategy foundation for the safe, long-life, and high-quality frequency regulation of large-scale energy storage systems. Attached Figure Description

[0053] Figure 1 This is an exemplary system architecture diagram of an energy storage unit direct control method based on frequency regulation mileage disclosed in this application;

[0054] Figure 2 This is a flowchart illustrating a direct control method for energy storage units based on frequency regulation mileage disclosed in this application;

[0055] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in this application.

[0056] Explanation of reference numerals in the attached figures: 100, System architecture; 101, First terminal device; 102, Second terminal device; 103, Third terminal device; 104, Network; 105, Server; 301, Processor; 302, Communication bus; 303, User interface; 304, Network interface; 305, Memory. Detailed Implementation

[0057] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0058] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0059] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0060] Figure 1 A schematic diagram of an exemplary system architecture is shown, illustrating an embodiment of a frequency-modulated mileage-based direct control method for energy storage units to which this application can be applied.

[0061] like Figure 1 As shown, the system architecture 100 may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0062] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as model training applications, video recognition applications, web browser applications, social platform software, etc.

[0063] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptops, and desktop computers, etc. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. They can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services) or as a single software program or software module. No specific limitations are imposed here.

[0064] When terminals 101, 102, and 103 are hardware devices, video capture devices can also be installed on them. These video capture devices can be various devices capable of capturing video, such as cameras, sensors, etc. Users can use the video capture devices on terminals 101, 102, and 103 to capture video.

[0065] Server 105 can be a server that provides various services, such as a backend server for processing data displayed on terminal devices 101, 102, and 103. The backend server can analyze and process the received data and can feed back the processing results (such as recognition results) to the terminal devices.

[0066] It should be noted that a server can be either hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0067] It should be understood that Figure 1The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included. In particular, if the target data does not need to be obtained remotely, the above system architecture may exclude the network and include only terminal devices or servers.

[0068] Figure 2 This is a flowchart illustrating a direct control method for energy storage units based on frequency regulation mileage, as described in this application. This method can be implemented using a computer program or a microcontroller. The computer program can be integrated into the application or run as a standalone utility application. The specific steps of the direct control method for energy storage units based on frequency regulation mileage are described in detail below.

[0069] S201: Establish and update the state model of heterogeneous battery cells in the energy storage power station. The state model of heterogeneous battery cells records the current state of charge, health status and historical cumulative frequency regulation mileage of each battery cell.

[0070] For example, this step aims to construct a high-precision digital panoramic view. By integrating static configuration parameters, real-time telemetry data, and long-term operation and maintenance statistics, each physically independent battery cell in the energy storage power station, exhibiting significant performance differences due to varying degrees of aging, is mapped as a virtual node with an independent status label. This not only breaks the assumption in traditional control that battery clusters are treated as a homogeneous whole, but also provides a real-time, accurate, and historically traceable decision-making basis for subsequent implementation of differentiated power allocation based on refined status.

[0071] In one possible implementation, establishing and updating the state model of heterogeneous battery cells in the energy storage power station specifically includes: establishing a state model of heterogeneous battery cells using the basic physical parameters of each battery cell, including the maximum charge and discharge power; collecting the dynamic operating parameters of each battery cell and updating the state model of heterogeneous battery cells in real time using the dynamic operating parameters, including the real-time temperature and the current state of charge; and updating the lifetime statistics record in the state model of heterogeneous battery cells based on the cumulative number of cycles, the number of days of use, and the historical cumulative frequency regulation mileage of the battery cells after each frequency regulation sub-task is completed.

[0072] In this embodiment, the heterogeneous battery cell state model refers to a data structure or digital twin image mapped into the control system's storage space, used to digitally characterize the real-time operating characteristics and full life-cycle state of each physical battery cell in an energy storage power station. Its heterogeneous nature represents the inconsistency in physical properties and aging levels of each battery cell in the energy storage power station due to differences in commissioning time, batches, or operating conditions. For example, this model can be represented as an array of structures or a database table stored in memory, where each row corresponds to all state dimensions of a physical battery cell, providing a data foundation for subsequent precise control.

[0073] Specifically, the establishment and maintenance of heterogeneous battery cell state models is integrated throughout the entire lifecycle management of energy storage power stations. During system initialization or the connection of new battery cells, the basic physical parameters of each battery cell (including maximum charge / discharge power P) are read or entered. max Rated capacity C rated This completes the instantiation of the model. These fundamental physical parameters serve as static benchmarks describing battery capabilities. The maximum charge / discharge power defines the upper and lower limits of the power a battery cell can provide within a safe range, determining the theoretical boundary for subsequent power allocation. During system operation, dynamic operating parameters of each battery cell are acquired at millisecond intervals (e.g., 100ms-500ms) via real-time communication interfaces such as CAN (Controller Area Network) bus or Ethernet. Real-time temperature (T...) is used... curr Update the thermal state field of the model using the current state of charge (SOC). curr The energy state field is updated to ensure that the synchronization delay between the digital model and the physical entity is controlled within the communication cycle. An event-triggered update mechanism is adopted, that is, after each independent frequency regulation subtask is completed, the loss caused by the task is calculated, and the lifetime statistics in the model are updated accordingly. This includes accumulating the cumulative number of cycles to characterize the degree of electrochemical fatigue, updating the number of days of use to characterize calendar aging, and accumulating the historical cumulative frequency regulation mileage to accurately quantify the actual total work done and cumulative wear of the battery cell in past frequency regulation services, thereby obtaining a heterogeneous battery cell state model containing the latest lifetime state.

[0074] S202: In response to the received automatic generation control frequency modulation command, extract the time-frequency domain features of the automatic generation control frequency modulation command, and decompose the automatic generation control frequency modulation command into multiple frequency modulation sub-tasks containing different frequency components in the time dimension based on the time-frequency domain features.

[0075] For example, this step employs an adaptive signal decoupling and reconstruction strategy. Faced with AGC commands issued by the power grid, which exhibit strong randomness and non-stationarity, the system does not use a single, fixed mechanical segmentation method. Instead, it first analyzes the physical attributes of the command segment using time-frequency analysis technology (i.e., determining whether it focuses on high-frequency power regulation or low-frequency energy throughput). Based on the diagnostic results, an intelligently matched differential decomposition algorithm is used to transform the continuous and complex long-term command stream into a series of discrete, appropriately granular, and clearly defined physical characteristics (such as short-term power-type or long-term energy-type) standardized sub-tasks. This transforms the macro-level power grid dispatching requirements into independent task packages executable by micro-level battery units, laying the data foundation for subsequent independent and precise control based on individual state differences.

[0076] In one possible implementation, the automatic generation control frequency modulation command is decomposed into multiple frequency modulation sub-tasks containing different frequency components in the time dimension based on time-frequency domain characteristics. Specifically, this includes: counting the number of zero-crossings and the number of power direction changes of the automatic generation control frequency modulation command within a preset time window, and determining the frequency type category of the automatic generation control frequency modulation command based on the number of zero-crossings and the number of power direction changes; if the frequency type category is determined to be a high-frequency signal that meets preset high-frequency conditions, then a truncation window is constructed with a preset time window length, and the automatic generation control frequency modulation command is processed according to a preset overlap step size. The time axis is shifted to capture a window, and multiple short-term frequency modulation subtasks are captured sequentially based on the shifted window. If the frequency type is determined to be a low-frequency signal that meets the preset low-frequency conditions, the automatic generation control frequency modulation command is evenly decomposed according to the preset fixed number of segments or the preset long time interval to obtain multiple long-term energy type subtasks. If the frequency type is determined to be a mixed signal, the power zero-crossing time in the automatic generation control frequency modulation command is identified, the command segment located between two adjacent power zero-crossing times is obtained, and each command segment is constructed into a mixed-type frequency modulation subtask.

[0077] In this embodiment, time-frequency domain features refer to statistical indicators extracted from a continuously changing automatic generation control frequency modulation command sequence that can quantify the intensity of fluctuations, frequency of direction changes, and energy persistence characteristics of the command signal in the time dimension. These indicators serve as the basis for subsequent decision-making in deconstructing long commands into discrete task units. For example, these features may include the frequency at which the signal waveform crosses the zero-power axis per unit time (zero-crossing rate) and the frequency at which the power amplitude changes from increasing to decreasing or from decreasing to increasing (extreme point distribution density).

[0078] Specifically, the system preprocesses and analyzes the received long-term instruction data. To improve the robustness of feature extraction, a noise threshold mechanism is introduced during statistical analysis. For example, if the collected AGC instruction sequence is P(t), a noise threshold is applied only when the power product P(t) of adjacent time points...i )·P(t i-1 ) < 0 and the absolute value of power change |P(t) i )-P(t i-1 Only when the noise level exceeds a preset noise threshold δ (e.g., 0.5 MW) is it counted as a valid zero-crossing count N. zc , P(t i ) indicates that the instruction sequence is in t i The power value at time t (i.e., the specific power value required by the AGC command at the i-th sampling time); similarly, the number of power direction changes N is only counted when the sign of the first-order difference sequence is flipped. dir The system will collect statistics on the above N within a preset time window (e.g., 5 minutes). zc and N dir These two statistical values ​​are then compared with a preset threshold matrix. For example, if N zc >30 and N dir If N > 50, it is determined to be a high-frequency signal; if N zc If the value is less than 5, it is determined to be a low-frequency signal; otherwise, it is determined to be a mixed signal. For scenarios where the signal is determined to be high-frequency (usually indicating severe grid frequency fluctuations requiring rapid power support), to capture transient changes and ensure task continuity, a short, preset time window is constructed (e.g., 1.5 to 2 times the command's main frequency period, such as 30 seconds). A preset overlap step size (e.g., 20%-30% of the window length, such as 6-9 seconds) is then used to shift the signal along the command's time axis. This overlap ensures that command features at the edges of the time slice are not missed, guaranteeing smooth control and resulting in a series of short-duration frequency-modulated subtasks with overlapping temporal intervals and rich high-frequency information.

[0079] Furthermore, for scenarios identified as low-frequency signals (which typically imply a unidirectional high-energy throughput requirement), to reduce control fragmentation, the command is uniformly divided according to a preset fixed number of segments or a longer preset time interval, thereby obtaining a long-term energy-type subtask that is easy to schedule for continuous charging and discharging. For scenarios identified as mixed signals, adaptive segmentation is performed using the physical characteristics of the signal itself. By accurately identifying the power zero-crossing point (i.e., the natural switching point between charging and discharging states) in the command waveform, the complete waveform data located between two adjacent power zero-crossing points is directly extracted as the command segment. This ensures that the power direction remains consistent within each task segment, ultimately constructing a more adaptable mixed-mode frequency modulation subtask.

[0080] S203: Calculate the load balancing value corresponding to the historical cumulative frequency regulation mileage, and calculate the comprehensive evaluation score of each battery cell by combining the load balancing value, health status and current state of charge.

[0081] For example, this step aims to construct a battery cell evaluation system that balances historical fairness with current adaptability. The system performs load balancing calculations based on the historical cumulative workload of each battery cell. By dynamically adjusting competition weights, it suppresses the calling frequency of high-wear cells and increases the activity of low-wear cells, thereby achieving average loss distribution across all equipment in the station. The system further integrates multi-dimensional state information such as the current state of charge (SOC), state of health (SOH), and thermal operating environment of the battery cells. Using a multi-parameter fusion algorithm, it maps the complex physical states into a single comprehensive evaluation score. This score intuitively quantifies the recommendation level for each battery cell to undertake frequency regulation tasks at the current moment, providing a quantitative basis for subsequent precise scheduling based on optimal target matching.

[0082] In one possible implementation, calculating the load balancing value corresponding to the historical cumulative frequency regulation mileage specifically includes: obtaining the statistical distribution characteristics of the historical cumulative frequency regulation mileage of all battery units participating in frequency regulation within the energy storage power station; calculating the degree of deviation between the historical cumulative frequency regulation mileage of each battery unit and the preset mean index in the statistical distribution characteristics; if the historical cumulative frequency regulation mileage is lower than the mean index, calculating a load balancing value greater than the preset benchmark value based on the degree of deviation; if the historical cumulative frequency regulation mileage is higher than or equal to the mean index, calculating a load balancing value less than or equal to the preset benchmark value based on the degree of deviation.

[0083] In this embodiment, the load balancing value refers to a gain or attenuation coefficient calculated based on the historical workload of a battery cell, used to dynamically adjust the competitive weight of the battery cell in the current task allocation algorithm. It represents the priority of the current battery cell in participating in frequency regulation tasks at the next moment, from the perspective of overall equipment lifespan balancing and long-term health maintenance. For example, this value is typically set as a dimensionless floating-point number fluctuating around a baseline value (such as 1.0). A higher value indicates that the system is more inclined to schedule the battery cell, while a lower value tends to reduce the battery cell's calling priority or put it in standby mode.

[0084] Specifically, the system performs a full data scan of all available battery cells participating in frequency regulation services within the energy storage power station, obtaining the statistical distribution characteristics of their historical cumulative frequency regulation mileage. This distribution primarily focuses on preset mean indicators (e.g., the arithmetic mean of all battery cell mileage) that reflect the overall average wear level of the power station. Next, for each specific battery cell, the system calculates the numerical difference between its individual historical cumulative frequency regulation mileage and the group's mean indicator, and normalizes this difference by incorporating the magnitude of the mean itself, thereby quantifying the degree of deviation of that battery cell from the overall station average. A wear-based approach is employed. The reverse negative feedback adjustment strategy is used to calculate the final value: If it is determined that the historical cumulative frequency regulation mileage of a certain battery cell is lower than the average index, it means that the battery's historical cumulative workload is small or its wear level is lower than the average level. In order to encourage it to take on more tasks to share the overall pressure, a positive gain is given to it based on the degree of deviation, and a load balancing value greater than the preset benchmark value is calculated. Conversely, if it is determined that the historical cumulative frequency regulation mileage is higher than or equal to the average index, it means that the battery's cumulative workload is large and its wear is heavy. In order to protect it, its weight is reduced based on the degree of deviation, and a load balancing value less than or equal to the preset benchmark value is calculated.

[0085] In one possible implementation, a comprehensive evaluation score for each battery cell is calculated by integrating the load balancing values, health status, and current state of charge. Specifically, this includes: calculating the absolute value of the difference between the current state of charge and a preset target state of charge, and generating a state of charge preference score based on the absolute value; generating a health score characterizing the degree of aging of the battery cell based on the cycle count parameter and calendar life parameter in the health status; acquiring the real-time temperature of the battery cell and calculating a temperature deviation score based on the real-time temperature, which characterizes the closeness of the real-time temperature to a preset optimal operating temperature range; assigning preset weight coefficients to the load balancing values, state of charge preference score, health score, and temperature deviation score respectively; and summing the weighted load balancing values, state of charge preference score, health score, and temperature deviation score to obtain the comprehensive evaluation score.

[0086] In this embodiment, the comprehensive evaluation score refers to a composite quantitative index derived from multi-dimensional physical state parameters through a weighted algorithm. This index represents the adaptation priority of a specific battery cell relative to other cells within the energy storage power station in undertaking a new frequency regulation task at the current moment. For example, this score is typically expressed as a standardized numerical value (e.g., 0 to 100 points). A higher score indicates a more ideal overall state for the battery cell in terms of charge maintenance, health degradation, temperature safety, and load balancing, making it more likely to be selected as the target for the system's actions.

[0087] Specifically, the calculation process focuses on the regulation requirements of electrical energy. To eliminate the influence of dimensions, the system uses a Gaussian function or an inverted U-shaped function for normalization. For example, let the target SOC be SOC. target (e.g., 50%), the current SOC is SOC curr State of charge preference score Where k is the control sensitivity coefficient. By calculating the absolute value of the difference and substituting it into the above function, the degree to which the current battery charge deviates from the ideal center is quantified. The smaller the deviation, the higher the score, thus generating a state of charge preference score. Simultaneously, to consider extending battery life, an aging assessment is performed based on the cycle count parameter and calendar life parameter in the health status record. Low-loss batteries with fewer cycle counts and longer remaining lifespan are assigned higher scores, thereby generating a health score. For example, the health score S... health S can be calculated using the following formula: health =100×(w) c ·(N max -N curr ) / N max +w t ·(T max -T curr ) / T max ), where S health N represents the calculated health score. curr N represents the current cumulative number of cycles of the battery cell. max T represents the maximum number of cycles (e.g., 6000) in the battery cell design over its entire lifespan. curr T represents the current number of days the battery cell has been used (calendar life). max This indicates the maximum calendar lifespan (e.g., 3650 days) of the battery cell design. c w t These represent the weighting coefficients of cyclic lifespan and calendar lifespan in health assessment (e.g., w). c =0.8, w t =0.2, and w c +w t =1).

[0088] Furthermore, considering thermal management safety, the real-time temperature of the battery cell is acquired via a real-time acquisition interface, and the deviation of this temperature from the preset optimal operating temperature range (e.g., 25℃ ± 5℃) is calculated. A segmented penalty logic is adopted: full marks are awarded if the temperature is within the range; if it exceeds the range, a penalty is applied according to S... temp =100-λ×|T curr -T boundary Points will be deducted, among which S temp This represents the calculated temperature deviation score, where λ is the penalty coefficient, and T currThis indicates the real-time temperature of the battery cell, T. boundary This represents the reference boundary value for the preset optimal operating temperature range; if it approaches the thermal runaway alarm threshold, this score is forced to 0 (triggering the circuit breaker logic). The closer the temperature is to the center of the optimal range, the safer and more efficient the thermal state, and the higher the temperature deviation score generated accordingly. For load balancing values, Min-Max inverse normalization is used, assuming the maximum mileage of the entire station is M. max The minimum mileage is M min The current mileage is M i Then the load balancing score S load =100×(M max -M i ) / (M max -M min It is important to note that when M... max =M min The default value for the load balancing score (e.g., 100) is set when the load balancing is first put into operation (e.g., during initial commissioning).

[0089] Furthermore, based on the actual operating strategy (such as prioritizing lifespan protection or power balance), the system assigns pre-defined weighting coefficients (e.g., ω1, ω2, ω3, ω4) to the load balancing values, state of charge preference scores, health scores, and temperature deviation scores obtained in the previous steps. These four sub-scores are then multiplied by their respective weights and linearly superimposed and summed to obtain the final comprehensive evaluation score (e.g., comprehensive evaluation score S). total =ω1·S load +ω2·S SOC +ω3·S health +ω4·S temp ).

[0090] S204: Based on the comprehensive evaluation score, determine the target battery cell that matches each frequency modulation subtask from the battery cells, and generate power execution instructions for controlling the target battery cell.

[0091] For example, this step aims to construct a "selective adaptation" scheduling mechanism based on multi-dimensional state awareness. The system will identify several battery cells with the optimal current state (e.g., minimum lifespan loss, optimal power, and healthiest temperature) based on the aforementioned calculated comprehensive evaluation score, and dynamically reorganize them into a virtual execution cluster capable of covering the power requirements of the current frequency regulation subtask. Based on the individual characteristics of each battery cell in this execution cluster, the system will differentially calculate and generate power execution commands, ensuring that battery cells with higher comprehensive evaluation scores and better power regulation capabilities undertake the main regulation tasks. This will maximize the overall health and operating efficiency of the entire station's battery bank while accurately responding to the grid's frequency regulation needs.

[0092] In one possible implementation, target battery cells matching each frequency modulation subtask are determined from each battery cell based on the comprehensive evaluation score, and power execution instructions for controlling the target battery cells are generated. Specifically, this includes: sorting the battery cells in descending order of the comprehensive evaluation score; selecting the top-ranked battery cells as target battery cells according to the sorting results, until the total adjustable power of all selected target battery cells meets the total power requirement of the frequency modulation subtask; allocating the total power requirement of the frequency modulation subtask to each target battery cell based on the maximum charge / discharge power of each target battery cell and the proportion of the comprehensive evaluation score, and generating corresponding power execution instructions.

[0093] In this embodiment, the target battery cell refers to a specific set of physical entities selected from numerous available battery clusters in an energy storage power station, designated to undertake the frequency regulation task at the current moment. It represents the subset of devices that are optimal in terms of overall performance score and, when combined, can meet the power demand of the power grid. For example, in a power station containing 50 battery clusters, the algorithm selects the 8 battery clusters to perform a 5MW frequency regulation task at a certain moment. The power execution command represents a digital control signal sent via the communication bus to the Power Conversion System (PCS) or Battery Management System (BMS), containing a specific power value (kW / MW) and the direction of action (charging or discharging). The adjustable total power represents the sum of the maximum power output capabilities that the currently selected group of battery cells can collectively provide, considering their own maximum power limitations and safety boundaries.

[0094] Specifically, to ensure that the frequency modulation task is executed by the device in optimal condition, the system sorts each battery cell according to the quantitative scoring results obtained from previous calculations, in descending order of comprehensive evaluation scores, thus establishing a priority queue reflecting the quality of the battery cells. Following the principle of priority ranking and capacity matching, the system sequentially selects the top-ranked battery cells as target battery cells based on the ranking results. During the selection process, the rated power or current available power limit of the selected cells is accumulated in real time until the total adjustable power of all selected target battery cells meets the total power requirement of the frequency modulation sub-task. At this point, the selection stops, thus determining the final list of devices participating in the response. To prevent high-performance battery cells from overloading and causing accelerated local aging, and to achieve refined control, the total power is not simply divided equally. Instead, a weighting coefficient is calculated based on the maximum charge / discharge power of each target battery cell and its proportion of the comprehensive evaluation score. The total power requirement of the frequency modulation sub-task is then allocated to each target battery cell according to the weight, allowing cells with high scores and high power to undertake more tasks. The specific power value that each cell should deliver is calculated, and a corresponding power execution command is generated accordingly. During this process, the system also performs boundary constraint checks and dead zone handling: First, capability verification. Determine the calculated power output value P ref Does it exceed the maximum allowable charge / discharge power P reported by the BMS in real time for this battery cell? max If P ref >P max Then P is forcibly restricted. ref =P max The resulting power shortfall ΔP is rolled back to the allocation pool and redistributed to the second-best rated battery cells. Second, dead-zone handling. Considering the minimum operating power limit of the PCS (e.g., 1% of rated power), if the calculated P... ref If the power demand is less than the dead zone threshold, the system performs zeroing (i.e., no action is taken) and adds the small power demand to the next higher-scoring battery cell to prevent the device from frequently starting and stopping in the low-power region. Finally, the corresponding power execution command is generated based on this.

[0095] S205: Drive the target battery cell to execute the power execution command and add the frequency modulation mileage generated in this execution to the target battery cell's historical cumulative frequency modulation mileage.

[0096] In this embodiment, the historical cumulative frequency regulation mileage refers to the cumulative statistical value recorded in the heterogeneous battery cell state model, which characterizes the total power fluctuation or regulation amplitude generated by a specific battery cell participating in frequency regulation services since its commissioning. It represents the degree of mechanical wear and electrochemical aging accumulated by the battery cell due to frequent charge-discharge switching during long-term operation. For example, this parameter can be represented as a monotonically increasing floating-point counter. Its value not only reflects the total power regulation actually undertaken by the battery but also implicitly maps the stress fatigue state of the internal electrode materials, serving as a core data benchmark for subsequent load balancing calculations and wear equalization allocation strategies.

[0097] Specifically, after the control command is generated, the power execution command is precisely sent to the corresponding energy storage converter or battery management system via industrial fieldbus or Ethernet communication protocol. This drives the target battery unit to output power in strict accordance with the power setpoint and slope specified in the command, responding to the frequency correction requirements of the power grid through physical charging and discharging actions. Simultaneously with or within a very short time after the action is executed, the frequency regulation mileage generated by this execution is quantified in real time by calculating the absolute integral of the actual output power waveform or the sum of the absolute values ​​of the power changes using high-frequency sampled power data streams. To obtain the true physical wear and tear and eliminate sensor noise interference, the system introduces a noise shielding mechanism during calculation. Let the sampling period be Δt, and the measured power at time k be P(k). Only when the power change |P(k) - P(k-1)| at adjacent times is greater than a preset noise threshold (e.g., 0.5% of the rated power) is this change included in the mileage accumulation; otherwise, it is considered sensor noise and ignored. Immediately afterwards, the state model's write interface is invoked to read the current mileage record of the target battery cell. The newly calculated mileage value is summed with the original recorded value, thereby adding the frequency-modulated mileage generated in this execution to the target battery cell's historical cumulative frequency-modulated mileage. This completes the digital update of the battery's wear and tear status, providing the latest state input for the next round of task allocation. In addition, to prevent data overflow and reduce memory read / write wear, the historical mileage can adopt a "dual-layer storage architecture," that is, high-frequency accumulation is performed in RAM (Random Access Memory), and only at fixed intervals (such as 1 hour) or when the accumulated value reaches a threshold, is it written to non-volatile memory for persistent storage.

[0098] Optionally, the method further includes: real-time monitoring of the current operating status and current task queue of the battery cell; when the current operating status is detected to be idle and the current task queue is empty, determining whether the current state of charge deviates from the preset healthy state of charge range; if the current state of charge deviates from the healthy state of charge range, generating a charging command or discharging command with a power value equal to the preset maintenance power value according to the direction of deviation, and using the charging command or discharging command as an active balancing control command; driving the battery cell to execute the active balancing control command, and stopping the execution of the active balancing control command when the current state of charge is detected to be within the healthy state of charge range.

[0099] In this embodiment, the healthy charge range refers to a pre-defined range of state of charge (SOC) values ​​that ensures the electrochemical activity within the battery cell is at its most stable state or the rate of lifespan degradation is slowest. It represents the optimal storage capacity range that the battery should maintain as much as possible during non-operational periods to minimize the aging process. For example, for lithium iron phosphate batteries, this range is typically set to 45% to 55% SOC. Within this range, the rate of side reactions within the battery is lowest, effectively preventing irreversible capacity degradation caused by prolonged storage at high (fully charged) or low (discharged) charge levels.

[0100] Specifically, to fully utilize the non-continuous idle periods between frequency modulation tasks for self-maintenance, the system initiates a background daemon process to monitor the current operating status of the battery unit (such as standby, running, fault, etc.) and the backlog of the current task queue in real time. Only when the system simultaneously detects that the current operating status is idle and the current task queue is empty, confirming that the battery unit is currently free from both external command interference and internal pending tasks, does it trigger active maintenance logic, read BMS data, and determine whether the current state of charge deviates from the preset healthy state of charge range. If the current state of charge deviates from the healthy state of charge range, the system will reverse the direction of deviation. Adjustment decision – if the charge level is above the upper limit of the range, discharge is required; if the charge level is below the lower limit of the range, charging is required. To avoid large current surges, the generated command power value is equal to the preset maintenance power value (usually set to a very small multiple, such as 0.05C). The charging or discharging command is issued as an active balancing control command. The battery cell executes the active balancing control command, causing the battery charge to slowly move towards the healthy range. During the execution process, the SOC value is continuously polled. Once the current state of charge is detected to be in the healthy state of charge range, the maintenance process is immediately interrupted, the active balancing control command is stopped, and the battery is kept in the optimal state to wait for the next frequency adjustment task.

[0101] This embodiment also discloses an electronic device, as shown in the reference. Figure 3 The electronic device may include: at least one processor 301, at least one communication bus 302, user interface 303, network interface 304, and at least one memory 305.

[0102] The communication bus 302 is used to enable communication between these components.

[0103] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0104] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0105] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0106] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a direct control method of energy storage units based on frequency modulation mileage.

[0107] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call the application program stored in the memory 305 for a direct control method of energy storage unit based on frequency modulation mileage. When executed by one or more processors 301, the electronic device executes one or more methods as described in the above embodiments.

[0108] 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 preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0109] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0110] In some embodiments of this application, a computer-readable storage medium is provided, including instructions that, when executed on the electronic device, cause the electronic device to perform a direct control method for an energy storage unit based on frequency modulation mileage, as described in this application.

[0111] In some embodiments of this application, a computer program product is also provided, which, when run on an electronic device, causes the electronic device to execute a direct control method for an energy storage unit based on frequency modulation mileage, as described in the embodiments of this application.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory 305 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0116] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the disclosure in this specification. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope of this application is defined by the claims.

Claims

1. A direct control method for an energy storage unit based on frequency regulation mileage, characterized in that, The method includes: Establish and update the state model of heterogeneous battery cells in the energy storage power station. The state model of heterogeneous battery cells records the current state of charge, health status and historical cumulative frequency regulation mileage of each battery cell. In response to the received automatic power generation control frequency modulation command, the time-frequency domain features of the automatic power generation control frequency modulation command are extracted, and the automatic power generation control frequency modulation command is decomposed into multiple frequency modulation sub-tasks containing different frequency components in the time dimension based on the time-frequency domain features. Calculate the load balancing value corresponding to the historical cumulative frequency regulation mileage, and combine the load balancing value, the health status and the current state of charge to calculate the comprehensive evaluation score of each battery cell; Based on the comprehensive evaluation score, a target battery cell matching each frequency modulation subtask is determined from each of the battery cells, and a power execution command for controlling the target battery cell is generated; The target battery cell is driven to execute the power execution command, and the frequency modulation mileage generated in this execution is added to the historical cumulative frequency modulation mileage of the target battery cell.

2. The method according to claim 1, characterized in that, The establishment and updating of the heterogeneous battery cell state model of the energy storage power station specifically includes: A state model of the heterogeneous battery cell is established using the basic physical parameters of each battery cell, including the maximum charge and discharge power. The dynamic operating parameters of each battery cell are collected, and the state model of the heterogeneous battery cell is updated in real time using the dynamic operating parameters, which include real-time temperature and the current state of charge. After each completion of the frequency modulation subtask, the lifetime statistics record in the heterogeneous battery cell state model is updated based on the cumulative number of cycles, usage days, and historical cumulative frequency modulation mileage of the battery cell.

3. The method according to claim 1, characterized in that, The step of decomposing the automatic power generation control frequency modulation command into multiple frequency modulation sub-tasks containing different frequency components in the time dimension based on the time-frequency domain characteristics specifically includes: The number of zero-crossings and the number of power direction changes of the automatic power generation control frequency regulation command within a preset time window are counted, and the frequency type category of the automatic power generation control frequency regulation command is determined based on the number of zero-crossings and the number of power direction changes. If the frequency type is determined to be a high-frequency signal that meets the preset high-frequency conditions, a capture window is constructed with a preset time window length, and the capture window is shifted on the time axis of the automatic power generation control frequency modulation command according to a preset overlap step size. Based on the shifted capture window, multiple short-time frequency modulation sub-tasks are captured sequentially. If the frequency type category is determined to be a low-frequency signal that meets the preset low-frequency conditions, the automatic power generation control frequency modulation command is evenly decomposed according to the preset fixed number of segments or the preset long time interval to obtain multiple long-time energy type sub-tasks. If the frequency type is determined to be a mixed signal, the power zero-crossing time in the automatic power generation control frequency modulation command is identified, the command segment located between two adjacent power zero-crossing times is obtained, and each command segment is constructed as a mixed frequency modulation subtask.

4. The method according to claim 1, characterized in that, The calculation of the load balancing value corresponding to the historical cumulative frequency modulation mileage specifically includes: Obtain the statistical distribution characteristics of the historical cumulative frequency regulation mileage of all battery cells participating in frequency regulation within the energy storage power station; Calculate the degree of deviation between the historical cumulative frequency modulation mileage of each battery cell and the preset mean index in the statistical distribution characteristics; If the historical cumulative frequency regulation mileage is lower than the average index, the load balancing value is calculated based on the degree of deviation and is greater than the preset benchmark value. If the historical cumulative frequency regulation mileage is higher than or equal to the average index, the load balancing value is calculated based on the degree of deviation and is less than or equal to the preset benchmark value.

5. The method according to claim 4, characterized in that, The comprehensive evaluation score for each battery cell is calculated by combining the load balancing values, the health status, and the current state of charge, specifically including: Calculate the absolute value of the difference between the current state of charge and the preset target state of charge, and generate a state of charge preference score based on the absolute value; Based on the cycle count parameter and calendar life parameter in the health status, a health score characterizing the aging degree of the battery cell is generated; The real-time temperature of the battery cell is obtained, and a temperature deviation score is calculated based on the real-time temperature. The temperature deviation score represents the degree to which the real-time temperature is close to the preset optimal operating temperature range. Each of the load balancing value, the state of charge preference score, the health score, and the temperature deviation score is assigned a preset weighting coefficient. The weighted load balancing value, the state of charge preference score, the health score, and the temperature deviation score are summed to obtain the comprehensive evaluation score.

6. The method according to claim 5, characterized in that, The step of determining the target battery cell matching each frequency modulation subtask from the battery cells based on the comprehensive evaluation score, and generating power execution instructions for controlling the target battery cell, specifically includes: The battery cells are sorted in descending order of their comprehensive evaluation scores. Based on the sorting results, the battery cells with the highest ranking are selected as the target battery cells in sequence until the total adjustable power of all the selected target battery cells meets the total power requirement of the frequency modulation subtask. Based on the maximum charge / discharge power of each target battery cell and the proportion of the comprehensive evaluation score, the total power requirement of the frequency modulation subtask is allocated to each target battery cell, and the corresponding power execution command is generated.

7. The method according to claim 1, characterized in that, The method further includes: Real-time monitoring of the current operating status and current task queue of the battery unit; When it is detected that the current running state is in an idle state and the current task queue is empty, it is determined whether the current charge state deviates from the preset healthy charge range; If the current state of charge deviates from the healthy state of charge range, a charging command or discharging command with a power value equal to the preset maintenance power value is generated according to the direction of deviation, and the charging command or the discharging command is used as an active balancing control command. The battery cell is driven to execute the active balancing control command, and when the current state of charge is detected to be within the healthy state of charge range, the execution of the active balancing control command is stopped.

8. An electronic device, characterized in that, Including processor and memory; The memory is used to store computer program code, the computer program code including computer instructions, and the processor invokes the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed on the electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on an electronic device, it causes the electronic device to perform the method as described in any one of claims 1-7.

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