An energy storage controllable quantity regulation method, system, storage medium and program product

Abstract

This application provides a method, system, storage medium, and program product for controllable energy storage, relating to the field of power system technology. The method includes: substituting the electrochemical characteristic parameters of each energy storage unit into a preset battery state prediction model to predict the voltage-limited discharge amount until the terminal voltage reaches a preset cutoff voltage threshold while maintaining the current power command; identifying confined energy storage units in the energy storage cluster whose capacity loss index exceeds a preset threshold based on the voltage-limited discharge amount and the static theoretical remaining capacity; reducing the power allocation weight of the confined energy storage units and updating the capacity loss index until the capacity loss index meets a preset convergence condition; allocating the power deficit resulting from reducing the power of the confined energy storage units to unconfined energy storage units; generating the final power control command for each energy storage unit and issuing it for execution. Implementing this method can maximize the online operating time of confined units while ensuring that the total power output of the energy storage cluster meets the target.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, system, storage medium and program product for controlling energy storage quantities. Background Technology

[0002] With the rapid development of new energy power generation technologies, large-scale energy storage systems are playing an increasingly crucial role in smoothing power fluctuations, peak shaving and valley filling, and grid frequency regulation. Energy storage power stations typically consist of a large cluster of energy storage units (such as battery clusters or battery packs) connected in parallel or series. In actual operation, after receiving the total power dispatch command from the upper level, the energy storage control system needs to rationally allocate it to the various energy storage units within the cluster to ensure that the entire energy storage system can stably and continuously respond to grid demands.

[0003] In related technologies, a control strategy based on Static State of Charge (SOC) balancing is typically used when allocating power among energy storage units. This strategy assumes that the output capacity of each energy storage unit is proportional to its remaining charge. It monitors the SOC value of each energy storage unit in real time and controls units with higher SOCs to handle greater discharge power, while units with lower SOCs handle less discharge power. Through this "high-low fill" approach, the SOC of each energy storage unit is made more consistent, thereby ensuring that all units exhaust their charge at the same time and maintaining consistency among units within the energy storage cluster.

[0004] However, during the long-term operation of energy storage systems, due to differences in manufacturing processes and operating environments, the aging degree of different energy storage units gradually varies, manifesting as inconsistencies in parameters such as electrochemical internal resistance and polarization characteristics. In this situation, even if an energy storage unit has a high remaining charge (SOC), if its internal resistance is high due to aging, it will still be allocated a larger discharge power according to the relevant technology's equalization control strategy. According to Ohm's law and battery characteristics, a large current flowing through a high-internal-resistance battery will cause a significant voltage drop, leading to a rapid decrease in the terminal voltage of that energy storage unit. This physical phenomenon may cause the terminal voltage to reach the bottom cutoff voltage protection threshold before the remaining internal charge is effectively released, thus triggering the undervoltage protection mechanism to forcibly stop operation, resulting in an unexpected loss of the overall usable capacity of the energy storage system and a sudden decrease in its power support capability. Summary of the Invention

[0005] This application provides a method, system, storage medium, and program product for controlling the amount of energy stored, which addresses the problem of premature protection shutdown caused by a rapid drop in terminal voltage when the energy storage unit has sufficient remaining power but is under high power output.

[0006] In a first aspect, this application provides a method for controlling the controllable quantity of energy storage, applied to an energy storage controllable quantity system, the method comprising: The corresponding electrochemical characteristic parameters are determined based on the real-time operating status data of each energy storage unit in the energy storage cluster; Substitute the electrochemical characteristic parameters into a preset battery state prediction model to predict the voltage-limited discharge amount of each energy storage unit when maintaining the current power command until the terminal voltage reaches a preset cutoff voltage threshold, and obtain the current effective capacity of each energy storage unit. Based on the static theoretical remaining power of each energy storage unit and the current effective capacity, a capacity loss index characterizing the degree of limitation of the output capacity of each energy storage unit is calculated. Identify restricted energy storage units in the energy storage cluster whose capacity loss index exceeds a preset threshold; Before generating the final control command, the power allocation weight of the confined energy storage unit is reduced, and the estimated value corresponding to the capacity loss index is updated based on the reduced weight until the estimated value meets the preset convergence condition. The power deficit caused by reducing the power of the constrained energy storage unit is allocated to the unconstrained energy storage units in the energy storage cluster according to the priority from low to high based on the capacity loss index. The final power control command for each energy storage unit is then generated and issued for execution.

[0007] By adopting the above technical solution, the system can quantify in advance the voltage drop risk of energy storage units under the current power due to factors such as internal resistance by substituting electrochemical characteristic parameters into the prediction model. This allows for more accurate identification of constrained units with effective capacity less than theoretical capacity. By actively reducing the power weight of these constrained units and reasonably transferring the deficit, the problem of aging units triggering undervoltage protection shutdown due to excessively rapid voltage drop can be alleviated. While ensuring that the total power output of the energy storage cluster meets the standards, the system maximizes the online operating time of constrained units, thereby improving the overall available capacity and depth of discharge of the energy storage system.

[0008] In some embodiments, the step of substituting the electrochemical characteristic parameters into a preset battery state prediction model to predict the voltage-limited discharge amount of each energy storage unit under the current power command until the terminal voltage reaches a preset cutoff voltage threshold, and obtaining the current effective capacity of each energy storage unit, specifically includes: An equivalent circuit model including internal resistance parameters is constructed as the battery state prediction model; Based on the electrochemical characteristic parameters and the battery state prediction model, the terminal voltage change curve of the energy storage unit under the current power command is simulated. Based on the time point when the terminal voltage change curve drops to the preset cutoff voltage threshold, determine the voltage-limited operating time from the current moment to the time point; The current effective capacity is calculated based on the voltage-limited operating time and the operating current corresponding to the current power command.

[0009] By employing the above technical solution, the system can transform abstract electrochemical characteristics into specific terminal voltage change curves, simulating the dynamic response process of the energy storage unit under a specific power command. By calculating the time point when the terminal voltage reaches the cutoff threshold, the system directly maps voltage constraints into quantifiable voltage-constrained operating time, thereby obtaining the current effective capacity that closely reflects actual operating conditions. This method makes the evaluation of the energy storage unit's output capability more consistent with the internal physical characteristics of the battery, improving the accuracy of determining the battery discharge boundary.

[0010] In some embodiments, the step of reducing the power allocation weight of the confined energy storage unit and updating the estimated value corresponding to the capacity loss index based on the reduced weight until the estimated value meets a preset convergence condition specifically includes: The power allocation weight of the confined energy storage unit is reduced according to the preset weight adjustment step size to obtain the corrected weight value; Based on the corrected weight value, the corrected power command corresponding to the confined energy storage unit is recalculated; The battery state prediction model is invoked to predict the corrected effective capacity of the confined energy storage unit under the corrected power command, and the estimated value of the capacity loss index is updated based on the corrected effective capacity. Determine whether the updated estimated value is greater than the preset threshold; If not, then the estimated value is determined to meet the preset convergence condition; If so, the corrected weight value is updated again according to the weight adjustment step size, and a judgment is made based on the obtained new estimated value.

[0011] By adopting the above technical solution, the system does not directly cut off or blindly set the power of the restricted units. Instead, it adjusts the weights in a step-by-step manner and repeatedly calls the model to verify the adjusted voltage performance until a balance point is found that satisfies voltage safety constraints while maintaining maximum output capacity. This gradual convergence strategy achieves flexible control of the restricted units. While preventing the terminal voltage from exceeding the limit and triggering protection, it preserves the power contribution of aging units as much as possible, making full use of every available capacity, ensuring operational safety and reducing resource waste.

[0012] In some embodiments, prior to the step of substituting the electrochemical characteristic parameters into a preset battery state prediction model, the method further includes: Monitor the time-domain variation characteristics of the current power command; When it is determined that the current power command is in a high-frequency fluctuation state based on the time-domain variation characteristics, the maximum value of the absolute value of the power command within the preset sampling window is extracted as the virtual peak power. When calculating the capacity loss index, the virtual peak power is used instead of the current power command as the input parameter and substituted into the battery state prediction model.

[0013] By adopting the above technical solution, the system monitors the time-domain characteristics of commands and proactively selects a virtual peak power within the sampling window to replace the instantaneous value as the prediction input when drastic fluctuations are detected. This mechanism effectively introduces a safety margin under dynamic operating conditions, simulating the battery's terminal voltage response under the most severe load conditions. This prevents misjudgments caused by instantaneous lows in commands masking insufficient battery capacity. This ensures that the calculation of capacity loss indicators always covers the most unfavorable boundaries that may occur during operation, improving the robustness and reliability of the energy storage system in response to complex grid dispatch commands.

[0014] In some embodiments, the step of calculating a capacity loss index characterizing the degree of limitation on the output capacity of each energy storage unit based on the static theoretical remaining power of each energy storage unit and the current effective capacity specifically includes: The instantaneous calculated value of the capacity loss index is obtained based on the virtual peak power and the static theoretical remaining power. The capacity loss index determined in the previous control cycle will be used as the historical benchmark value. If the instantaneous calculated value is greater than or equal to the historical benchmark value, then the instantaneous calculated value is determined to be the capacity loss index for the current control cycle; If the instantaneous calculated value is less than the historical benchmark value, the historical benchmark value is maintained as the capacity loss indicator for the current control cycle until the cumulative duration for which the instantaneous calculated value is less than the historical benchmark value for multiple consecutive cycles reaches the preset de-jitter duration.

[0015] By adopting the above technical solution, the system incorporates de-jittering logic with time-delay characteristics when calculating the capacity loss index. The system employs a "maximum priority, decreasing delay" strategy, meaning that when the instantaneous calculated value is large, it is updated immediately, while a longer confirmation period is required when it becomes small. This one-way fast response mechanism filters out false jumps in the index caused by measurement noise or instantaneous operating condition fluctuations, ensuring the stability of the capacity loss index as a control benchmark. It alleviates the repeated fluctuations in power allocation commands caused by frequent index oscillations, thereby maintaining the stability of the energy storage cluster control process and reducing unnecessary disturbances to the system caused by adjustment actions.

[0016] In some embodiments, the step of allocating the power deficit resulting from reducing the power of the confined energy storage unit to the unconfined energy storage units in the energy storage cluster according to the priority from low to high based on the capacity loss index specifically includes: Based on the deviation between the real-time state of charge of the unconfined energy storage unit and the average state of charge of the energy storage cluster, an equilibrium correction coefficient for the capacity loss index is generated. The capacity loss index of the unconstrained energy storage unit is updated based on the equilibrium correction coefficient. The power deficit is allocated to the corresponding unconstrained energy storage units in ascending order based on the updated capacity loss index.

[0017] By adopting the above technical solution, the system generates a correction coefficient based on the state of charge deviation, dynamically adjusting the priority sequence of the power deficit received by each unconstrained unit, so that healthy units with higher state of charge take on more transferred loads. This allocation strategy not only fills the total power gap but also simultaneously promotes consistency adjustment within the unconstrained units, reduces the power difference between healthy units, prevents over-discharge or power accumulation in some units due to uneven power reception, and improves the overall consistency of the energy storage cluster over long-term operating cycles.

[0018] In some embodiments, the step of allocating the power deficit to the corresponding unconfined energy storage units in ascending order according to the updated capacity loss index specifically includes: The power deficit is virtually superimposed with the current power command of the highest priority unconstrained energy storage unit to generate the virtual test power corresponding to the unconstrained energy storage unit. The virtual test power is substituted into the battery state prediction model to simulate the terminal voltage change, and the simulation results are obtained. If the simulation results determine that the terminal voltage of the unrestricted energy storage unit touches the preset cutoff voltage threshold within the preset safety buffer time, then the maximum allowable power of the unrestricted energy storage unit is calculated based on the internal resistance parameter in the electrochemical characteristic parameters and the preset cutoff voltage threshold. The allocation value not exceeding the maximum allowable power is issued to the unconstrained energy storage unit, and the remaining power deficit is transferred to the next priority unconstrained energy storage unit.

[0019] By adopting the above technical solution, the system pre-simulates the voltage changes of unconstrained units after power deficit is added. Once the potential threshold of cutoff voltage is predicted, the maximum allowable power is immediately calculated for current limiting, and the remaining deficit is carried over to the next level unit. This progressive pre-verification mechanism can prevent originally healthy unconstrained units from exceeding voltage limits due to passively accepting excessive loads from constrained units, thus blocking the spread and transfer of risks within the cluster and ensuring that the power redistribution process is carried out in an orderly manner within the safety boundaries of all units.

[0020] Secondly, this application provides an energy storage controllable quantity regulation system, the system comprising: one or more processors and a memory; The memory is coupled to the one or more processors. The memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions so that the system can implement the energy storage controllable quantity regulation method provided in the above embodiments, which will not be described in detail here.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on an energy storage controllable quantity regulation system, enable the system to implement an energy storage controllable quantity regulation method provided in the above embodiments, which will not be elaborated further here.

[0022] Fourthly, this application provides a computer program product, including a computer program / instruction, which, when the computer program / instruction is run on an energy storage controllable quantity regulation system, enables the system to implement an energy storage controllable quantity regulation method provided in the above embodiments, which will not be elaborated here.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By simulating the terminal voltage change curve of the energy storage unit under the current power command, the system transforms the physical voltage cutoff constraint into a quantifiable "voltage-limited discharge capacity," thereby defining the "current effective capacity." This method can identify "pseudo-healthy" units that, although they have sufficient remaining charge, are prone to voltage drops due to high internal resistance caused by aging, thus deeply coupling the battery's external physical characteristics (voltage) with its internal chemical state (charge).

[0024] 2. When dealing with confined energy storage units, the system gradually reduces the power weight and repeatedly calls the model to verify the voltage response, using a "step-by-step approximation" approach to find the maximum output capacity of the confined unit without triggering undervoltage protection. This strategy, through refined dynamic probing, finds the optimal balance between ensuring safe operation and maximizing the remaining value of aged batteries, achieving ultimate and safe control of energy storage resources.

[0025] 3. Before allocating the power reduced by the restricted units to the unrestricted units, the system not only considers SOC balancing, but more importantly, introduces a virtual superposition test to simulate the voltage changes of the receiving units after adding an extra load. This "simulate first, then execute" logic can prevent originally healthy units from exceeding their voltage limits due to passively accepting power, thus fundamentally blocking the transmission and spread of overload risk within the energy storage cluster. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a method for controlling the amount of energy stored in an embodiment of this application; Figure 2 This is a flowchart illustrating the system's calculation of capacity loss indicators for each energy storage unit in an embodiment of this application. Figure 3 This is a schematic diagram of a process for power allocation to unconstrained energy storage units in an embodiment of this application; Figure 4 This is a schematic diagram of the physical device structure of an energy storage controllable quantity regulation system in the embodiments of this application. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] For ease of understanding, the method provided in this implementation is described in process below. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a method for controlling the amount of energy stored in an embodiment of this application.

[0030] S101. Determine the corresponding electrochemical characteristic parameters based on the real-time operating status data of each energy storage unit in the energy storage cluster.

[0031] Among them, an energy storage cluster refers to an overall energy storage system composed of multiple energy storage units connected by electrical means; an energy storage unit refers to the smallest power dispatching unit in the energy storage cluster that can be independently controlled for charging and discharging, which can be a single battery cluster, battery pack, or battery module; real-time operating status data refers to the measured values ​​of the operating parameters of the energy storage unit at the current moment, used to represent the instantaneous operating status of the energy storage unit, including but not limited to physical quantities such as terminal voltage, operating current, temperature, and state of charge; electrochemical characteristic parameters refer to key physical quantities that reflect the internal electrochemical performance of the energy storage unit, used to represent the internal characteristics of the energy storage unit, including but not limited to parameters such as internal resistance, polarization resistance, diffusion coefficient, and open-circuit voltage.

[0032] This step is executed at the beginning of each control cycle, triggered when the energy storage controllable quantity regulation system receives upper-level scheduling instructions and prepares to make power allocation decisions. The system needs to obtain the latest status information of each energy storage unit before allocating power in order to accurately assess the actual output capacity of each unit.

[0033] Specifically, the system reads the operating status data of the current control cycle from each energy storage unit within the energy storage cluster through a data acquisition module. This data is measured in real time by sensors distributed on each energy storage unit and reported to the control system. After receiving this raw measurement data, the system needs to perform necessary data preprocessing, including but not limited to filtering and noise reduction, outlier detection, and data verification. After the data quality check is passed, the system extracts or calculates key parameters that characterize the electrochemical properties of the energy storage unit from the real-time operating status data according to the requirements of the electrochemical model.

[0034] For example, the system can calculate the real-time internal resistance of the energy storage unit using parameter identification algorithms by combining measurements of terminal voltage and operating current with the current state of charge. For polarization characteristic parameters, the system can identify them by analyzing the dynamic characteristics of the voltage response curve under current changes. Compared to simple voltage and current measurements, these electrochemical characteristic parameters can reflect the health status and aging degree of the energy storage unit at a deeper level.

[0035] In some embodiments, the system can determine electrochemical characteristic parameters using an online parameter identification method based on Kalman filtering: the system establishes a battery state-space model including internal resistance and polarization parameters, and uses the electrochemical parameters to be identified as state variables; it collects terminal voltage and operating current data from multiple consecutive sampling points to construct an observation vector sequence; it initializes the state estimate and covariance matrix of the extended Kalman filter, and sets the statistical characteristics of process noise and measurement noise; then, it performs the prediction and update steps of the Kalman filter, continuously correcting the state estimate using the deviation between the measured value and the predicted value; finally, it extracts the optimal estimate of each electrochemical characteristic parameter from the converged state estimate results and evaluates whether the estimation accuracy meets the control requirements.

[0036] S102. Substitute the electrochemical characteristic parameters into the preset battery state prediction model to predict the voltage-limited discharge amount of each energy storage unit when maintaining the current power command until the terminal voltage reaches the preset cutoff voltage threshold, and obtain the current effective capacity of each energy storage unit.

[0037] Among them, the battery state prediction model refers to a mathematical model that can predict the future state evolution of a battery based on electrochemical characteristic parameters and operating conditions. It is used to represent a simplified expression of the internal physicochemical processes of the battery, usually using an equivalent circuit model or an electrochemical mechanism model. The current power command refers to the target power setting value issued by the upper-level scheduling system to the energy storage unit in the current control cycle. It is used to indicate the magnitude and direction of the charging or discharging power that the energy storage unit should execute. The preset cutoff voltage threshold is the lower limit value of the terminal voltage set to protect battery safety. It is used to indicate the lowest voltage boundary that the battery can reach during discharge. Below this value, the undervoltage protection mechanism will be triggered. The voltage-limited discharge capacity refers to the amount of electricity that the energy storage unit can release from the current state until the terminal voltage drops to the cutoff voltage threshold under a specific power. It is used to represent the actual usable electricity of the energy storage unit after considering voltage constraints. The current effective capacity refers to the maximum amount of electricity that the energy storage unit can actually output under the current operating conditions and electrochemical state without triggering voltage protection. It is used to represent the true usable energy reserve of the energy storage unit.

[0038] Specifically, the system inputs the obtained electrochemical characteristic parameters into a pre-established battery state prediction model. This prediction model can be constructed based on the equivalent circuit principle, including an ohmic resistor component characterizing the voltage drop caused by the battery's internal resistance, and a RC network component characterizing electrochemical polarization. The system calculates the corresponding operating current value based on the current power command and uses this current as the input excitation for the model. The model starts from the initial state at the current moment and gradually extrapolates forward according to the time step, calculating the battery's terminal voltage value at each time point. During the extrapolation process, the model considers the instantaneous voltage drop caused by the current flowing through the internal resistance, as well as the delayed voltage drop caused by polarization effects, and comprehensively obtains the dynamic trajectory of the terminal voltage change.

[0039] The system continuously monitors the simulated terminal voltage curve. When the predicted terminal voltage value at a future moment first drops to or below the preset cutoff voltage threshold, the system records the time difference between that moment and the current moment. This time difference represents the maximum duration for which the energy storage unit can operate continuously under the current power command. The system multiplies this time length by the operating current corresponding to the current power command to obtain the total charge that the energy storage unit can actually output during that time period. This charge is the voltage-limited discharge amount, which is the current effective capacity of the energy storage unit. This current effective capacity reflects the actual usable energy of the energy storage unit after considering voltage constraints, and may be significantly less than the theoretical remaining charge calculated based on the state of charge, especially for aging energy storage units with high internal resistance.

[0040] Optionally, the system can predict the current effective capacity using a numerical simulation method based on a first-order RC equivalent circuit model: The system sets the values ​​of ohmic internal resistance, polarization internal resistance, and polarization capacitance in the equivalent circuit model according to electrochemical characteristic parameters; determines the initial value of the open-circuit voltage based on the current state of charge of the energy storage unit, and calculates the operating current based on the current power command; then, it sets the simulation time step and performs iterative calculations in discrete time steps starting from the current moment, updating the voltage state of the polarization capacitor and the battery terminal voltage value at each time step; then, at each time step, it checks whether the calculated terminal voltage has dropped to a preset cutoff voltage threshold; if so, the simulation terminates and the number of elapsed time steps is recorded; subsequently, the system multiplies the number of time steps by the time step size to obtain the voltage-limited operating time, and then multiplies it by the magnitude of the operating current to obtain the voltage-limited discharge amount; finally, the system outputs this voltage-limited discharge amount as the current effective capacity of the energy storage unit. It is understood that other methods can also be used to predict the current effective capacity, such as simulation methods based on electrochemical-thermal coupling models, data-driven machine learning prediction methods, etc., which are not limited here.

[0041] S103. Based on the static theoretical remaining power and current effective capacity of each energy storage unit, calculate the capacity loss index that characterizes the degree of limitation of the output capacity of each energy storage unit.

[0042] Among them, the static theoretical remaining power refers to the theoretical available power calculated based on the current state of charge and rated capacity of the energy storage unit, which is used to represent the chemical energy stored inside the energy storage unit without considering dynamic constraints; the capacity loss index is a numerical index that quantifies the degree of loss of the actual available capacity of the energy storage unit relative to the theoretical remaining power due to voltage limitations, and is used to represent the severity of the limitation on the output capacity of the energy storage unit.

[0043] Specifically, the system acquires the static theoretical remaining capacity of each energy storage unit. This theoretical remaining capacity is typically obtained by reading the state of charge (SOC) value of the energy storage unit and multiplying it by the unit's rated capacity, representing the actual chemical energy stored within the unit. The system then acquires the current effective capacity predicted in step S102. The system compares and analyzes these two capacity values, calculates the difference or ratio between them, and generates a numerical index that quantifies the degree of capacity loss, i.e., the capacity loss index.

[0044] In some embodiments, the system can calculate the capacity loss index using a capacity difference normalization method. The system reads the current state of charge (SOC) value of the energy storage unit and obtains its rated capacity parameter. Next, it multiplies the SOC value by the rated capacity to calculate the static theoretical remaining capacity. Then, the system obtains the current effective capacity value of the energy storage unit predicted in step S102. Next, the system calculates the difference between the static theoretical remaining capacity and the current effective capacity, which represents the amount of electricity that cannot be released due to voltage limitations. Then, the system normalizes this difference by dividing it by the static theoretical remaining capacity to obtain the relative proportion of capacity loss. Finally, the system multiplies this relative proportion by a set amplification factor or performs other mathematical transformations to generate the final capacity loss index value, which is typically a dimensionless standardized index.

[0045] Understandably, when an energy storage unit is in good health and has low internal resistance, its terminal voltage will not drop rapidly even when operating at higher power. In this case, the current effective capacity is close to the static theoretical remaining capacity, and the capacity loss index is relatively small. Conversely, when the internal resistance of an energy storage unit increases due to aging, the terminal voltage will drop to the cutoff threshold more quickly at the same power, resulting in a current effective capacity significantly less than the static theoretical remaining capacity. In this case, the capacity loss index is relatively large. The system assigns a quantitative label to the health status of each energy storage unit based on the calculated capacity loss index. The larger the index value, the more severely the unit's output capability is limited by voltage under the current power command, and the more special handling is required during power allocation. This index comprehensively reflects the combined effects of multiple factors such as the energy storage unit's internal resistance level, polarization characteristics, aging degree, and current operating power, providing a quantitative basis for the system to identify restricted units that require power reduction protection.

[0046] S104. Identify restricted energy storage units in the energy storage cluster whose capacity loss index exceeds a preset threshold.

[0047] Among them, the preset threshold is a critical value of capacity loss index used to determine whether an energy storage unit is in a constrained state, and it represents the upper limit of the capacity loss that the system can accept.

[0048] Specifically, the system iterates through each energy storage unit in the energy storage cluster, reading the capacity loss index value calculated in step S103 for each unit. For each energy storage unit, the system compares its capacity loss index with a preset threshold. If the capacity loss index value of a certain energy storage unit is greater than the preset threshold, it is determined that the unit has a significant risk of voltage limitation under the current power command, and there is a large gap between the current effective capacity and the static theoretical remaining power. Continuing to maintain the current power may cause the unit to prematurely trigger undervoltage protection shutdown due to a rapid drop in terminal voltage. The system marks such units as limited energy storage units and adds them to the list of limited units to be processed.

[0049] Conversely, if the capacity loss index of an energy storage unit is less than or equal to a preset threshold, it is determined that the unit has sufficient voltage margin under the current power, and the current effective capacity is not much different from the theoretical remaining power, which can well support the execution of the current power command. The system marks such units as unrestricted energy storage units and includes them in the healthy unit list.

[0050] Through this threshold comparison screening mechanism, the system divides all units within the energy storage cluster into two groups: confined and unconfined, providing a clear object classification for subsequent differentiated power allocation strategies. This identification process is dynamic and can be re-executed in each control cycle based on changes in the energy storage unit status and operating conditions, ensuring that control decisions are always based on the latest health status assessment.

[0051] S105. Before generating the final control command, reduce the power allocation weight of the confined energy storage unit, and update the estimated value corresponding to the capacity loss index based on the reduced weight, until the estimated value meets the preset convergence condition.

[0052] Among them, the power allocation weight refers to the weight coefficient assigned to each energy storage unit when allocating power in the energy storage cluster, which is used to represent the relative share that the energy storage unit should bear in the total power; the estimated value refers to the predicted value of the capacity loss index recalculated based on the adjusted power allocation weight, which is used to represent the expected capacity loss degree of the energy storage unit after the power adjustment; the preset convergence condition refers to the condition standard for judging whether the iterative adjustment process can be terminated, which is used to represent the judgment rule that the capacity loss index has been reduced to an acceptable range.

[0053] The system initiates a power weight adjustment process for each identified constrained energy storage unit. Specifically, the system reads the weight value of the constrained unit in the initial power allocation scheme, which is typically determined based on the state of charge or other equalization strategies. The system decreases this weight value according to a preset weight adjustment step size, resulting in a smaller corrected weight value. Based on this corrected weight value, the system recalculates the power command that the constrained unit should bear under the new weight. The system substitutes this corrected power command into the battery state prediction model, resimulates the terminal voltage change process of the constrained unit after the power reduction, and calculates the current effective capacity under the new power conditions. Based on the new effective capacity and the static theoretical remaining capacity, the system recalculates the estimated capacity loss index for the constrained unit. The system compares the updated estimated value with a preset threshold to determine whether the adjusted capacity loss index has been reduced to an acceptable range.

[0054] If the estimated value still exceeds the preset threshold, it indicates that the power reduction is insufficient and the terminal voltage risk has not been completely eliminated. The system continues to reduce the power weight again according to the adjustment step size, and repeats the above calculation and judgment process. This iterative process continues until the estimated value after an adjustment first falls below the preset threshold. At this point, the system determines that the estimated value meets the preset convergence condition, and the iterative process terminates. The system saves the corrected weight value at this time as the final power allocation weight for the restricted unit. Through this gradual weight adjustment and model verification mechanism, the system can find a balance point for each restricted unit that ensures voltage safety while preserving output capability as much as possible. This flexible control strategy avoids simply and crudely reducing the power of the restricted unit to zero, maximizing the utilization of the remaining value of the aging unit, and ensuring that it will not trigger protection shutdown due to terminal voltage exceeding the limit.

[0055] S106. The power deficit caused by reducing the power of the constrained energy storage unit is allocated to the unconstrained energy storage unit in the energy storage cluster according to the priority from low to high capacity loss index. The final power control command for each energy storage unit is generated and issued for execution.

[0056] Among them, power deficit refers to the total power gap caused by reducing the power allocation of constrained energy storage units. It is used to represent the power difference that needs to be compensated by other energy storage units to ensure that the total output power of the energy storage cluster meets the scheduling requirements.

[0057] Specifically, the system calculates the total power reduction of all constrained energy storage units due to the reduction in power weight, and this total constitutes the power deficit that needs to be compensated by other units.

[0058] The system obtains a list of all unconstrained energy storage units, which have good health and sufficient voltage margin, enabling them to handle additional power. The system sorts these units according to their capacity loss index, with units having lower indexes ranked higher. This sorting strategy ensures that the units with the strongest output capacity and the most sufficient voltage margin receive power allocation first, enabling them to handle additional loads most stably. The system then begins by allocating power shortfalls to the highest-priority unconstrained unit.

[0059] During the allocation process, the system needs to ensure that the total power after allocation will not cause voltage limiting issues in the unrestricted unit. Specifically, the system superimposes the power to be allocated with the unit's current power command to form a virtual test power, and calls a battery state prediction model to simulate the unit's terminal voltage response under the new power. If the simulation results show that the unit can safely handle the load, the system formally allocates the corresponding share of the power deficit to the unit. If the simulation results show a potential risk of voltage exceeding the limit, the system calculates the maximum acceptable power for the unit without triggering voltage protection, allocates only this maximum value, and transfers the remaining power deficit to the next priority unrestricted unit.

[0060] The system processes unconstrained units one by one according to priority until all power deficits are successfully allocated. After power redistribution is completed, the system generates a final power control command for each energy storage unit, which takes into account the power reduction protection requirements of constrained units and the compensation capabilities of unconstrained units. The system sends these final power control commands to the local controllers of each energy storage unit through the communication interface. After receiving the commands, the local controllers execute the corresponding power adjustment actions to complete the power allocation task for the entire control cycle.

[0061] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is a flowchart illustrating the process of calculating the capacity loss index of each energy storage unit in an embodiment of this application.

[0062] S201. When it is determined that the current power command is in a high-frequency fluctuation state based on the time-domain variation characteristics of the current power command, the maximum value of the absolute value of the power command within the preset sampling window is extracted as the virtual peak power.

[0063] Among them, the time-domain variation characteristics refer to the dynamic change patterns and characteristics of power commands in the time dimension, used to represent the statistical characteristics such as the fluctuation amplitude, frequency, and trend of power commands over time; high-frequency fluctuation state refers to the operating state in which power commands change frequently and drastically in a short period of time, used to represent the operating condition where the fluctuation frequency of power commands exceeds a preset threshold or the fluctuation amplitude is abnormal; preset sampling window refers to the length of the time window used to extract the feature values ​​of power commands, used to represent the time range selected by the system when performing statistical analysis of power commands; virtual peak power refers to the maximum absolute value of power commands within the preset sampling window, used to represent the most stringent power demand that may occur under fluctuating operating conditions, as an input parameter for conservative prediction.

[0064] This step is performed after the system acquires the real-time operating status data and current power command of each energy storage unit, and before substituting the electrochemical characteristic parameters into the battery state prediction model. When the system faces application scenarios such as grid frequency regulation and smoothing of new energy power fluctuations, the power commands issued from the upper layer may exhibit high-frequency fluctuation characteristics. The system needs to determine the time-domain characteristics of the power commands before calculating the capacity loss index to decide whether to use the instantaneous power value or adopt a more conservative peak power for prediction.

[0065] Specifically, the system continuously collects power command sequence data within a certain time window and performs time-domain feature analysis on the collected power command sequences, calculating statistical indicators such as the rate of change, standard deviation, and fluctuation frequency of the power commands. The system compares these statistical indicators with preset high-frequency fluctuation judgment thresholds. For example, the system can calculate the number of times the power command direction changes per unit time. If this number exceeds a preset frequency threshold, or if the standard deviation of the power command exceeds a specific proportion of the average value, the system determines that the current power command is in a high-frequency fluctuation state. Once the power command is confirmed to be in a high-frequency fluctuation state, the system immediately starts the virtual peak power extraction program.

[0066] The system iterates through all power command sampling points within a preset sampling window, takes the absolute value of the power command at each sampling point, and then selects the maximum value from these absolute values. This maximum value is the virtual peak power, which represents the maximum power load that the energy storage unit may need to withstand within this time window. The system marks and stores the extracted virtual peak power as an input parameter for subsequent capacity depreciation index calculations.

[0067] S202. When calculating capacity loss indicators, the virtual peak power is used instead of the current power command for calculation.

[0068] Specifically, when preparing to calculate the capacity loss index, the system first checks the power command status flag to determine if it is currently in a high-frequency fluctuation state. If the system has already set the high-frequency fluctuation flag to true in step S201 and successfully extracted the virtual peak power, the system will no longer use the real-time acquired instantaneous power command value in subsequent calculations, but will instead call the stored virtual peak power value. The system uses the virtual peak power as an input parameter to pass to the battery state prediction model to simulate the terminal voltage change process of the energy storage unit under this peak power condition. The essence of this substitution strategy is to raise the calculation benchmark from a potentially low instantaneous power value to the maximum power value within the sampling window, which is equivalent to assuming that the energy storage unit needs to continuously withstand this maximum power load.

[0069] S203. Simulate the terminal voltage change curve of the energy storage unit under the current power command based on the electrochemical characteristic parameters and the battery state prediction model. The battery state prediction model is an equivalent circuit model that includes internal resistance parameters.

[0070] The equivalent circuit model is a simplified model that uses circuit elements to represent the internal electrochemical processes of a battery. It is used to represent the voltage-current characteristics of a battery and usually includes lumped parameter elements such as resistors and capacitors.

[0071] Specifically, the system calls a pre-established equivalent circuit model containing internal resistance parameters, then extracts the specific values ​​of each equivalent circuit element from the electrochemical characteristic parameters obtained in step S101, including ohmic internal resistance, polarization internal resistance, polarization capacitance, etc., and configures these parameters into the model. The system queries or calculates the corresponding open-circuit voltage value based on the current state of charge of the energy storage unit, using it as the initial setting for the battery electromotive force. The system obtains the current power command (virtual peak power in the case of high-frequency fluctuations) and calculates the corresponding operating current magnitude and direction based on the power and current voltage. The system sets the simulation time step, typically choosing a small time interval to ensure simulation accuracy, such as 0.1 seconds or 1 second. The system starts iterative calculations from the current moment as the simulation starting point, according to the set time step.

[0072] At each time step, the system calculates the instantaneous voltage drop across the ohmic internal resistance, which is equal to the product of the operating current and the ohmic internal resistance. The system then updates the voltage state of the polarized capacitor; the change in polarized capacitor voltage is determined by the current flowing through the polarized internal resistance and the capacitor's time constant. The system subtracts the ohmic voltage drop and then the polarized capacitor voltage from the open-circuit voltage to obtain the battery terminal voltage value for that time step. The system records the calculated terminal voltage value in the terminal voltage change curve data sequence. Simultaneously, the system updates the state of charge of the energy storage unit, calculating the amount of electricity consumed in this time step based on the operating current and time step length, and deducting it from the current state of charge. The system continues to the next time step, repeating the above calculation process until the termination condition is met.

[0073] S204. Based on the time point when the terminal voltage change curve drops to the preset cutoff voltage threshold, determine the voltage-limited operating time from the current moment to the time point, and calculate the current effective capacity by combining the operating current corresponding to the current power command.

[0074] Specifically, the system acquires the complete data of the simulated terminal voltage change curve generated in step S203 and reads the preset cutoff voltage threshold from the storage medium or memory. The system begins to traverse the data point sequence of the terminal voltage change curve, starting from the first data point at the start time and checking the terminal voltage value at each time point. The system compares the terminal voltage value of each data point with the preset cutoff voltage threshold. When the system finds that the terminal voltage value at a certain time point is less than or equal to the cutoff voltage threshold for the first time, the system considers that time point to be the critical moment when the energy storage unit reaches the voltage-limited boundary. The system records the timestamp corresponding to this critical moment and calculates the time difference between this moment and the simulation start time. This time difference is the voltage-limited operating time, representing the longest time that the energy storage unit can continuously operate under a given power condition from the current state.

[0075] If the terminal voltage does not drop to the cutoff threshold throughout the entire simulation time, the system determines that the capacity of the energy storage unit is not voltage-limited under the current power conditions. In this case, the voltage-limited operation time is equal to the maximum simulation time or set to a special value that represents no limit.

[0076] The system acquires the operating current value used in step S203, which is calculated based on the power command. The system multiplies the voltage-limited operating duration by the operating current to obtain the total charge flowing during that duration. According to the definition of charge, the charge is the product of current and time; the result of this product is the actual amount of electricity the energy storage unit can output without triggering voltage protection, i.e., the current effective capacity. The system stores the calculated current effective capacity value in the energy storage unit's state data structure for use in step S103 for calculating capacity loss indicators.

[0077] S205. The instantaneous calculated value of the capacity loss index is obtained based on the virtual peak power and the static theoretical remaining power, and the capacity loss index finally determined in the previous control cycle is used as the historical benchmark value.

[0078] The instantaneous calculated value refers to the immediate value of the capacity loss index calculated based on real-time data within the current control cycle, which is used to represent the degree of capacity loss calculated in real time based on the current operating conditions and status parameters; the historical benchmark value refers to the capacity loss index value that was finally determined and recorded in the previous control cycle, which is used to represent the historical capacity loss level as a reference for comparison in this cycle.

[0079] Specifically, the system reads the current static theoretical remaining power of the energy storage unit from the data storage module. Simultaneously, it obtains the current effective capacity value calculated in step S204. The system uses the virtual peak power as one of the input conditions, combining the static theoretical remaining power and the current effective capacity to calculate the capacity loss index. The system calculates the difference between the static theoretical remaining power and the current effective capacity; this difference represents the amount of power that cannot be effectively released due to voltage limitations. The system normalizes this difference by dividing it by the static theoretical remaining power to obtain the relative proportion of capacity loss. The system can perform mathematical transformations on this relative proportion, such as multiplying by one hundred to convert it to a percentage, or multiplying it by a specific amplification factor for subsequent processing, ultimately obtaining the instantaneous calculated value of the capacity loss index. This instantaneous calculated value reflects the degree of capacity limitation of the energy storage unit under the current control cycle and operating conditions.

[0080] The system then accesses the historical data storage area to query the capacity loss index value of the energy storage unit that was finally determined in the previous control cycle. This historical value is a stable value that has been determined after the complete calculation process of the previous cycle and may have undergone jitter reduction processing. The system extracts this value as the historical benchmark value. The system saves and marks the instantaneous calculated value obtained in this calculation with the historical benchmark value, preparing to enter the next step of the comparison and judgment process.

[0081] S206. The instantaneous calculated value is greater than or equal to the historical baseline value.

[0082] Specifically, the system extracts two values ​​from the data obtained in step S205: the instantaneous calculated value and the historical baseline value of the capacity loss index. The system performs a numerical comparison operation, determining the magnitude of the instantaneous calculated value and the historical baseline value. If the instantaneous calculated value is greater than the historical baseline value, it is determined that the capacity loss of the energy storage unit in this control cycle has increased compared to the previous cycle, possibly due to factors such as increased power demand, further increase in internal resistance, or decreased state of charge. The system determines that this deteriorating trend should be immediately reflected in the control decision, and therefore adopts a rapid response strategy. If the instantaneous calculated value is equal to the historical baseline value, it indicates that the capacity loss situation remains stable and has not changed significantly. The system also determines that no special processing is required in this case, and the current value can be directly maintained or updated.

[0083] The system categorizes the cases where the instantaneous calculated value is greater than or equal to the historical baseline value into the same processing branch. Under this branch, the system directly proceeds to step S207, using the instantaneous calculated value as the capacity loss indicator for the current control cycle. Conversely, if the instantaneous calculated value is numerically less than the historical baseline value, it indicates that the calculated capacity loss for this cycle is lower than that of the previous cycle, possibly due to reduced power demand, measurement noise, or temporary fluctuations in operating conditions. The system determines that this improvement trend may be temporary or unrealistic and should not be immediately accepted; therefore, a delayed confirmation process is required. In this case, the system guides the system to step S208, initiating a time-accumulated de-jitter verification mechanism.

[0084] S207. Determine the instantaneous calculated value as the capacity loss index for the current control cycle.

[0085] Specifically, after confirming that the instantaneous calculated value is greater than or equal to the historical benchmark value, the system immediately performs an update operation on the capacity loss index. The system reads the instantaneous calculated value from the current cycle data buffer. This value is the latest evaluation result calculated in step S205 based on the latest electrochemical characteristic parameters, current effective capacity, and static theoretical remaining capacity. The system directly assigns this instantaneous calculated value to the capacity loss index variable of the energy storage unit in the current control cycle, completing the real-time update of the index.

[0086] S208. Maintain the historical benchmark value as the capacity loss indicator for the current control cycle until the cumulative duration of the instantaneous calculated value being less than the historical benchmark value for multiple consecutive cycles reaches the preset de-jittering duration.

[0087] Among them, the preset de-jitter duration refers to the minimum duration threshold used to filter out the short-term fluctuations in the capacity loss index, which represents the observation waiting time required for the system to confirm the real improvement in capacity loss; the cumulative duration refers to the total length of time during which the instantaneous calculated value is continuously less than the historical benchmark value within multiple consecutive control cycles, which represents the cumulative amount of time for the improvement trend to continue.

[0088] Specifically, after confirming that the instantaneous calculated value is less than the historical benchmark value, the system does not immediately update the capacity loss indicator with the new, lower value. Instead, it initiates a confirmation mechanism based on time accumulation. The system first accesses the de-jitter status record of the energy storage unit to check if there is any accumulated downward trend timing data from previous cycles. If this is the first time that an instantaneous value is detected to be less than the historical value, the system initializes the de-jitter timer, setting the accumulated duration to the length of the current control cycle. If a downward trend has already been detected in a previous cycle, the system reads the accumulated duration value and adds the length of the current control cycle to the accumulated duration.

[0089] The system uses a continuous judgment logic when calculating the cumulative duration. The cumulative duration will only continue to increase when the instantaneous calculated value is less than the historical benchmark value in multiple consecutive control cycles. If an instantaneous value is greater than or equal to the historical value in any intermediate cycle, the system will reset the cumulative duration to zero in step S207 and restart the timing. The system compares the updated cumulative duration with a preset de-jittering duration threshold. The preset de-jittering duration is a system configuration parameter, set according to the application scenario of the energy storage system and the length of the control cycle. For example, it can be set to the total duration of ten control cycles or an absolute time of thirty seconds. If the cumulative duration has not yet reached the preset de-jittering duration, the system determines that the duration of the improvement trend is insufficient and further observation and verification are required.

[0090] S209. Identify restricted energy storage units in the energy storage cluster whose capacity loss index exceeds a preset threshold and adjust their power allocation.

[0091] Specifically, the system iterates through all energy storage units in the energy storage cluster, reading the final capacity derating index value for each unit in the current control cycle, as determined in step S207 or S208. The system reads a preset capacity derating index threshold from the configuration parameter library. This threshold is a judgment standard determined comprehensively based on the safety margin requirements of the energy storage system, battery characteristics, and operating experience; for example, it can be set to 20% or 30%. The system performs conditional judgment on each energy storage unit, comparing its capacity derating index with the preset threshold. If the capacity derating index value of an energy storage unit exceeds the preset threshold, it indicates that the unit's current effective capacity is significantly less than the static theoretical remaining capacity, posing a high risk of voltage limitation under the current power conditions. The system determines that this unit belongs to an energy storage unit with limited output capacity and marks it as limited. The system adds the identifier of this energy storage unit to the limited energy storage unit list. This list is a dynamic data structure used to collect all units requiring power derating protection within the current control cycle. Simultaneously, the system sets a limited flag in the status record of this energy storage unit and records the timestamp of being determined as limited and the specific value of the capacity derating index.

[0092] For energy storage units whose capacity loss indicators do not exceed the threshold, the system determines that their health status is good and they have sufficient voltage margin to support the current power command, marking them as unconstrained. The system adds the identifiers of these units to the unconstrained energy storage unit list, providing a candidate pool for subsequent power deficit allocation. After completing the traversal and classification of all energy storage units, the system generates a complete classification result containing information on constrained and unconstrained units. The system counts the number of constrained energy storage units and their proportion of total installed capacity to assess the overall health level of the current cluster. The system outputs the classification results to the power allocation adjustment module, triggering the subsequent process of reducing the power weight of constrained units and allocating power deficits to unconstrained units. The system also pushes the constrained unit identification results to the monitoring and alarm system. If the number or capacity proportion of constrained units exceeds the warning threshold, corresponding operation and maintenance alarm information is generated to prompt operation and maintenance personnel to pay attention to the aging status of the energy storage cluster.

[0093] The following is a more detailed description of the process of the method provided in this implementation. For example... Figure 3 The diagram shown is a flowchart illustrating the power allocation process of the system for unconstrained energy storage units in an embodiment of this application.

[0094] S301. Based on the preset weight adjustment step size, the current power allocation weight of the confined energy storage unit is reduced to obtain the corrected weight value.

[0095] This step is performed after the system identifies the confined energy storage unit through step S209 and before it begins to perform power derating protection on the confined unit. This step is triggered when the system detects that the capacity loss index of some energy storage units exceeds a preset threshold, and it is necessary to reduce their power allocation to avoid triggering protection due to over-limit terminal voltage.

[0096] Specifically, the system reads the pre-set weight adjustment step size parameter from the configuration parameter library. This step size parameter determines the magnitude of weight reduction in each iteration. For each energy storage unit identified as constrained, the system reads its current power allocation weight value in the initial power allocation scheme. This initial weight value is usually calculated based on the state-of-charge equalization strategy or other allocation algorithms without considering capacity degradation constraints. The system performs a numerical operation between the current weight value and the weight adjustment step size, performing a subtraction operation to obtain the reduced weight value. The system performs boundary checks on the calculated reduction result to ensure that the corrected weight value is not negative or exceeds a reasonable range. If the reduced weight value is less than zero or less than the minimum weight threshold set by the system, the system sets the corrected weight value to zero or the minimum threshold, indicating that the constrained unit may need to completely stop output in extreme cases. The system saves the weight value after boundary checks and corrections as the corrected weight value for this iteration and marks it as the weight adjustment result for the current iteration round.

[0097] S302. Recalculate the corrected power command corresponding to the confined energy storage unit based on the corrected weight value.

[0098] Specifically, the system obtains the corrected weight value of the constrained energy storage unit from step S301. Simultaneously, the system acquires the total power command issued by the upper-level scheduling system for the current control cycle. The system reads the power allocation weight data of all energy storage units in the energy storage cluster, including the corrected weight value of constrained units and the current weight value of unconstrained units. The system divides the corrected weight value of the constrained energy storage unit by the sum of the weights to obtain the power share proportion that the constrained unit should bear under the new weight allocation scheme. The system multiplies this share proportion by the total power command to calculate the corrected power command value corresponding to the constrained energy storage unit.

[0099] S303. Call the battery state prediction model to predict the corrected effective capacity of the confined energy storage unit under the maintenance corrected power command, and update the estimated value of the capacity loss index based on the corrected effective capacity.

[0100] Specifically, the system invokes the battery state prediction model used in step S203, re-inputting the electrochemical characteristic parameters of the confined energy storage unit into the prediction model. The system replaces the original power command with a corrected power command as the new input excitation condition for the model. The system calculates a new operating current value based on the corrected power command, which is lower than the current corresponding to the original power. The system drives the prediction model to start simulation calculations, starting from the initial state at the current moment and gradually deduce the dynamic change process of the terminal voltage according to the time step.

[0101] During the simulation, due to the reduced operating current, the voltage drop across the internal resistance also decreases accordingly, resulting in a slower rate of voltage drop compared to the original power condition. The system continuously monitors the simulated terminal voltage curve to identify the point in time when the terminal voltage first drops to the preset cutoff voltage threshold. The system records the time difference between this point and the current time to obtain the new voltage-constrained operating time under the corrected power condition. This time is longer than the operating time under the original power condition because the reduced power slows down the voltage drop rate. The system multiplies the new voltage-constrained operating time by the operating current corresponding to the corrected power to calculate the corrected effective capacity. This corrected effective capacity represents the actual amount of electricity that the confined unit can release after the power reduction. The system compares the corrected effective capacity with the static theoretical remaining electricity and uses the same calculation method as in step S205 to obtain a new capacity loss index value, i.e., the estimated value of the capacity loss index.

[0102] S304. Is the updated estimated value greater than the preset threshold?

[0103] Specifically, the system obtains the estimated capacity loss index of the confined energy storage unit under the corrected power condition from step S303. The system reads the preset capacity loss index threshold from the configuration parameter library, ensuring consistency with the threshold used in step S209. The system performs a numerical comparison calculation, comparing the updated estimated value with the preset threshold. If the estimated value is still greater than the preset threshold, it indicates that although the capacity loss of the confined unit has improved by reducing the power weight, the improvement is not sufficient. The unit still faces significant voltage limitation risk under the current corrected power, and the capacity loss index has not been reduced to a safe range. The system determines that the current weight adjustment is insufficient and needs to further reduce the power allocation weight to further alleviate voltage pressure. The system marks the judgment result as non-converged and guides the control flow to step S305, initiating a new round of weight reduction adjustment.

[0104] Conversely, if the estimated value is less than or equal to the preset threshold, it indicates that through this or cumulative weight adjustment, the capacity loss of the restricted unit has been reduced to an acceptable level. The unit has sufficient voltage margin under the current corrected power conditions and no longer faces the risk of triggering protection due to a rapid drop in terminal voltage. The system determines that the estimated value meets the preset convergence condition, and this round of iterative adjustment has achieved its expected goal. The system marks the judgment result as converged and guides the control flow to step S306, confirming that the current corrected weight value and corrected power command are the final power allocation scheme for the restricted unit.

[0105] S305. Adjust the step size based on the weight and update the corrected weight value again.

[0106] Specifically, the system confirms from the judgment result of step S304 that further weight reduction adjustment is needed. The system reads the corrected weight value already used in the current iteration, which is the result of the adjustment in the previous step S301. The system retrieves the weight adjustment step size parameter from the configuration parameter library again, which usually remains constant throughout the iteration. The system uses the current corrected weight value as the base weight for the new round of adjustment and performs the same reduction processing operation as in step S301. The system subtracts the weight adjustment step size from the corrected weight value to obtain a further reduced new corrected weight value.

[0107] The system can also perform boundary checks on the newly corrected weight values. If the weight has dropped to zero or close to zero, it indicates that the restricted unit may need to completely stop output to meet voltage safety requirements. In such extreme cases, the system may trigger special processing logic, such as generating an alarm message indicating that the unit's health status has severely deteriorated, or setting the weight to zero and exiting the iteration loop. If the newly corrected weight value is still within a reasonable range, the system saves it and updates the iteration counter, recording the number of iterations already performed. The system checks whether the number of iterations exceeds the preset maximum iteration limit, which is used to prevent the iteration process from looping indefinitely. If the number of iterations does not exceed the limit, the system passes the newly corrected weight value back to step S302 and restarts a complete round of weight adjustment, power calculation, capacity prediction, and convergence judgment. If the number of iterations has reached the upper limit and convergence has not yet occurred, the system determines that the restricted unit may have a serious problem that cannot be solved by conventional power adjustment. The system adopts a conservative strategy to set its weight to zero or a minimum value and generates an exception handling record.

[0108] S306. Determine that the estimated value meets the preset convergence condition.

[0109] Specifically, the system confirms from the judgment result of step S304 that the estimated capacity loss index has been reduced to below the preset threshold, and the iterative adjustment process has successfully converged. The system reads the corrected weight value obtained in the current iteration round. This weight value is the weight value that makes the estimated value meet the convergence condition for the first time after one or more decreasing adjustments. The system formally confirms this corrected weight value as the final power allocation weight of the confined energy storage unit in the current control cycle.

[0110] The system checks if any other restricted units have not yet completed the weight adjustment process. If so, it repeats steps S301 to S306 for the next restricted unit. If all restricted units have completed the weight adjustment and converged, the system summarizes the total power deficit of all restricted units and prepares to proceed with the process of allocating the power deficit to unrestricted units.

[0111] S307. Based on the deviation between the real-time state of charge of the unconstrained energy storage unit and the average state of charge of the energy storage cluster, generate the equilibrium correction coefficient for the capacity loss index.

[0112] Among them, real-time state of charge (SOC) refers to the SOC value of an unconfined energy storage unit at the current moment; the average SOC of the energy storage cluster refers to the arithmetic mean of the SOC of all energy storage units in the cluster; the deviation value is the difference between the real-time SOC of an unconfined energy storage unit and the average SOC of the energy storage cluster, used to indicate the degree of deviation of the unit's energy level from the cluster average level. The balancing correction coefficient is a correction factor generated based on the SOC deviation value to adjust the capacity loss index.

[0113] Specifically, the system iterates through all energy storage units marked as unconfined in the energy storage cluster and reads the real-time state of charge (SOC) value of each unit from its SOC data. Simultaneously, the system collects SOC data from all energy storage units in the cluster, including both confined and unconfined units, and calculates the arithmetic mean of the SOC of all units to obtain the average SOC of the energy storage cluster.

[0114] The system calculates the deviation between the real-time state of charge (SOC) of each unconfined energy storage unit and the average SOC of the energy storage cluster. If the SOC of an unconfined unit is above the average level, the deviation is positive, indicating that the unit is relatively overcharged and has a stronger discharge capacity and the potential to handle additional power. If the SOC of an unconfined unit is below the average level, the deviation is negative, indicating that the unit is relatively undercharged and its power load should be reduced to avoid over-discharge.

[0115] Based on the calculated deviation value, the system generates the equalization correction coefficient corresponding to the unconstrained cell using a preset mapping function or transformation rule. The design principle of this correction coefficient is that cells with higher state of charge receive larger correction coefficients, thereby obtaining higher priority in power allocation and assuming more power deficits. Conversely, cells with lower state of charge receive smaller correction coefficients, even reduction coefficients less than one, thereby lowering their priority in power allocation.

[0116] S308. Update the capacity loss index of unconstrained energy storage units based on the balance correction coefficient.

[0117] Specifically, the system reads the original capacity loss index value calculated in step S103 or step S209 for each unconstrained energy storage unit. The system extracts the equalization correction coefficient corresponding to each unconstrained unit from the correction coefficient table generated in step S307. For each unconstrained unit, the system performs mathematical operations on its original capacity loss index and equalization correction coefficient, usually using division, that is, dividing the original capacity loss index by the equalization correction coefficient to obtain the updated capacity loss index.

[0118] S309. The power deficit is virtually superimposed with the current power command of the highest priority unconstrained energy storage unit to generate the virtual test power corresponding to the unconstrained energy storage unit.

[0119] Virtual overlay refers to a simulation operation that adds two power values ​​at the computational level without actually executing the calculation, and is used to represent a predictive power combining process.

[0120] Specifically, the system sorts unconstrained energy storage units according to the updated capacity loss index, identifying the unit with the lowest capacity loss index as the highest priority unconstrained energy storage unit. The system reads the current power command value of the unconstrained unit in the initial power allocation scheme. Simultaneously, it obtains the power deficit value to be allocated, which can be the entire remaining deficit or a portion of the deficit planned for allocation to the current unit. The system performs a virtual overlay operation in the computing environment, adding the current power command to the power deficit to obtain a hypothetical total power value, i.e., the virtual test power. This virtual test power represents the total power load that the unit would actually need to perform if the power deficit were allocated to it.

[0121] S310. Substitute the virtual test power into the battery state prediction model to simulate the terminal voltage change and obtain the simulation results.

[0122] Specifically, the system extracts the latest electrochemical characteristic parameters from the state data of the unconfined energy storage unit. The system uses virtual test power as the input excitation for the model and calculates the corresponding operating current value based on the virtual test power and the current terminal voltage. The system configures the initial conditions for the simulation, including the current terminal voltage, state of charge, polarization state, etc., to ensure that the simulation starts from the actual operating state. The system sets the simulation time range, which is typically set long enough to observe the complete evolution of the terminal voltage, or set as a preset safety buffer time.

[0123] The system initiates simulation calculations, progressively tracing the dynamic changes in the terminal voltage according to the time step. At each time step, the model calculates the voltage drop caused by current flowing through the internal resistance, the voltage change due to polarization effects, and the decrease in state of charge, comprehensively obtaining the terminal voltage value for that time step. The system compares the terminal voltage value at each time step with a preset cutoff voltage threshold to determine whether the terminal voltage has reached or fallen below this threshold. If the terminal voltage remains above the safe range throughout the simulation time, the system determines that the unconstrained unit can safely bear the virtual test power, and the simulation result is considered passed. If the terminal voltage drops to the cutoff threshold at a certain time point, the system records this time point and determines that there is a risk of voltage exceeding the limit, and the simulation result is considered failed.

[0124] S311. If the simulation results determine that the terminal voltage of the unconfined energy storage unit touches the preset cutoff voltage threshold within the preset safety buffer time, the maximum allowable power of the unconfined energy storage unit is calculated based on the internal resistance parameter in the electrochemical characteristic parameters and the preset cutoff voltage threshold.

[0125] The preset safety buffer time refers to the minimum allowable time interval set by the system from the current moment until the terminal voltage reaches the cutoff threshold, which is used to represent the time margin requirement to ensure the safe operation of the energy storage unit. The maximum allowable power received refers to the maximum power increment that an unconstrained energy storage unit can accept under the premise of meeting the terminal voltage safety constraints, which is used to represent the upper limit of the unit's ability to receive power deficits.

[0126] Specifically, the system extracts key information from the simulation results obtained in step S310 to determine the time point when the terminal voltage reaches the cutoff voltage threshold under virtual test power conditions. The system compares the time difference between this time point and the current time with a preset safety buffer time. The preset safety buffer time is a system configuration parameter, usually set to several minutes to tens of minutes, to ensure that even after the power allocation scheme is implemented, the energy storage unit has sufficient time margin to complete adjustment or response before triggering undervoltage protection.

[0127] If the simulation results show that the terminal voltage reaches the threshold earlier than the end of the safety buffer time, it indicates that allocating the entire power deficit to the unrestricted unit would cause it to face the risk of voltage over-limit within a very short time, failing to meet the safety operation requirements. The system determines that the power allocation to this unit needs to be limited and cannot be performed according to the virtual test power. The system initiates the calculation program for the maximum allowable power.

[0128] The system extracts the internal resistance parameter from the electrochemical characteristic parameters of the unconstrained cell, which is a key factor determining the relationship between power and voltage drop. The system reads the current terminal voltage and a preset cutoff voltage threshold, calculates the voltage difference between them, and obtains the usable voltage margin. Based on Ohm's law and battery characteristics, the system knows that the voltage margin needs to compensate for the voltage drop caused by current flowing through the internal resistance, as well as part of the polarization voltage drop. The system uses a simplified calculation method, mainly considering the influence of the internal resistance voltage drop, to calculate the maximum current that the cell can withstand under a given voltage margin and safety buffer time constraint. The system multiplies this maximum current by the current terminal voltage to obtain the corresponding maximum allowable power. The system subtracts the power command already undertaken by the cell from the maximum allowable power to obtain the maximum additional power increment that the cell can accept, i.e., the maximum allowable power.

[0129] S312. Allocate a power limit not exceeding the maximum allowable power to the unconstrained energy storage units and transfer the remaining power deficit to the next priority unconstrained energy storage units.

[0130] Specifically, the system determines the power acceptance capacity of the current unrestricted unit based on the evaluation results of steps S310 and S311. If the simulation results of step S310 show that the unit can safely accommodate the entire power deficit to be allocated, the system directly uses the entire power deficit as the allocation value. If step S311 calculates the maximum allowable power, it indicates that the unit can only accommodate a portion of the power deficit, and the system uses the maximum allowable power as the allocation value to ensure that the allocation value does not exceed the safe carrying capacity of the unit.

[0131] The system generates a power increment command for the unrestricted unit, which includes the unit's identifier and the amount of power to be increased. This power increment command is then combined with the unit's initial power command to calculate the unit's final total power control command. This final power control command is marked as pending issuance, ready to be issued and executed uniformly after power allocation is completed for all units.

[0132] The system subtracts the allocated value already assigned to the current unit from the total power deficit to be allocated, calculating the remaining power deficit. If the remaining power deficit has decreased to zero or close to zero, it indicates that all power deficits have been successfully allocated, and the system ends the power allocation process and prepares to issue instructions. If the remaining power deficit is still significantly greater than zero, it indicates that there is still a power gap that needs to be allocated, and the system identifies the next priority unconstrained energy storage unit from the capacity loss index ranking list. The system transmits the remaining power deficit and the information of the next priority unit to a new power allocation cycle, repeating the processing steps S309 to S312.

[0133] The energy storage controllable quantity regulation system of this invention is applied to electronic devices. Figure 4 A schematic diagram of the architecture of an electronic device suitable for implementing embodiments of the present invention is shown.

[0134] It should be noted that, Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0135] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions (computer programs), or by instructions (computer programs) controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein the storage medium stores multiple instructions that can be loaded by the processor to execute any step of the method provided in the embodiments of the present invention.

[0136] Specifically, the storage medium and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more signal lines. The storage medium stores computer-executable instructions that implement data access control methods, including at least one software functional module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The storage medium stores the program, and the processor executes the program after receiving the execution instructions.

[0137] Furthermore, the software programs and modules within the aforementioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.) and can communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip with signal processing capabilities. The aforementioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc., which can implement or execute the methods, steps, and logic flowcharts disclosed in this embodiment. The general-purpose processor may be a microprocessor or any conventional processor.

[0138] Since the instructions stored in the storage medium can execute the steps in any of the methods provided in the embodiments of the present invention, the beneficial effects of any of the methods provided in the embodiments of the present invention can be achieved, as detailed in the preceding embodiments, and will not be repeated here.

[0139] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling the quantity of energy storage, applied to an energy storage controllable quantity control system, characterized in that, The method includes: The corresponding electrochemical characteristic parameters are determined based on the real-time operating status data of each energy storage unit in the energy storage cluster; Substitute the electrochemical characteristic parameters into a preset battery state prediction model to predict the voltage-limited discharge amount of each energy storage unit when maintaining the current power command until the terminal voltage reaches a preset cutoff voltage threshold, and obtain the current effective capacity of each energy storage unit. Based on the static theoretical remaining power of each energy storage unit and the current effective capacity, a capacity loss index characterizing the degree of limitation of the output capacity of each energy storage unit is calculated. Identify restricted energy storage units in the energy storage cluster whose capacity loss index exceeds a preset threshold; Before generating the final control command, the power allocation weight of the confined energy storage unit is reduced, and the estimated value corresponding to the capacity loss index is updated based on the reduced weight until the estimated value meets the preset convergence condition. The power deficit caused by reducing the power of the constrained energy storage unit is allocated to the unconstrained energy storage units in the energy storage cluster according to the priority from low to high based on the capacity loss index. The final power control command for each energy storage unit is then generated and issued for execution.

2. The method according to claim 1, characterized in that, The step of substituting the electrochemical characteristic parameters into a preset battery state prediction model to predict the voltage-limited discharge amount of each energy storage unit under the current power command until the terminal voltage reaches a preset cutoff voltage threshold, and obtaining the current effective capacity of each energy storage unit, specifically includes: An equivalent circuit model including internal resistance parameters is constructed as the battery state prediction model; Based on the electrochemical characteristic parameters and the battery state prediction model, the terminal voltage change curve of the energy storage unit under the current power command is simulated. Based on the time point when the terminal voltage change curve drops to the preset cutoff voltage threshold, determine the voltage-limited operating time from the current moment to the time point; The current effective capacity is calculated based on the voltage-limited operating time and the operating current corresponding to the current power command.

3. The method according to claim 1, characterized in that, The step of reducing the power allocation weight of the confined energy storage unit and updating the estimated value corresponding to the capacity loss index based on the reduced weight until the estimated value meets the preset convergence condition specifically includes: The power allocation weight of the confined energy storage unit is reduced according to the preset weight adjustment step size to obtain the corrected weight value; Based on the corrected weight value, the corrected power command corresponding to the confined energy storage unit is recalculated; The battery state prediction model is invoked to predict the corrected effective capacity of the confined energy storage unit under the corrected power command, and the estimated value of the capacity loss index is updated based on the corrected effective capacity. Determine whether the updated estimated value is greater than the preset threshold; If not, then the estimated value is determined to meet the preset convergence condition; If so, the corrected weight value is updated again according to the weight adjustment step size, and a judgment is made based on the obtained new estimated value.

4. The method according to claim 1, characterized in that, Before the step of substituting the electrochemical characteristic parameters into a preset battery state prediction model, the method further includes: Monitor the time-domain variation characteristics of the current power command; When it is determined that the current power command is in a high-frequency fluctuation state based on the time-domain variation characteristics, the maximum value of the absolute value of the power command within the preset sampling window is extracted as the virtual peak power. When calculating the capacity loss index, the virtual peak power is used instead of the current power command as the input parameter and substituted into the battery state prediction model.

5. The method according to claim 4, characterized in that, The step of calculating the capacity loss index, which characterizes the degree of limitation of the output capacity of each energy storage unit, based on the static theoretical remaining power of each energy storage unit and the current effective capacity, specifically includes: The instantaneous calculated value of the capacity loss index is obtained based on the virtual peak power and the static theoretical remaining power. The capacity loss index determined in the previous control cycle will be used as the historical benchmark value. If the instantaneous calculated value is greater than or equal to the historical benchmark value, then the instantaneous calculated value is determined to be the capacity loss index for the current control cycle; If the instantaneous calculated value is less than the historical benchmark value, the historical benchmark value is maintained as the capacity loss indicator for the current control cycle until the cumulative duration for which the instantaneous calculated value is less than the historical benchmark value for multiple consecutive cycles reaches the preset de-jitter duration.

6. The method according to claim 1, characterized in that, The step of allocating the power deficit caused by reducing the power of the confined energy storage unit to the unconfined energy storage units in the energy storage cluster according to the priority from low to high based on the capacity loss index specifically includes: Based on the deviation between the real-time state of charge of the unconfined energy storage unit and the average state of charge of the energy storage cluster, an equilibrium correction coefficient for the capacity loss index is generated. The capacity loss index of the unconstrained energy storage unit is updated based on the equilibrium correction coefficient. The power deficit is allocated to the corresponding unconstrained energy storage units in ascending order based on the updated capacity loss index.

7. The method according to claim 6, characterized in that, The step of allocating the power deficit to the corresponding unconstrained energy storage units in ascending order based on the updated capacity loss index specifically includes: The power deficit is virtually superimposed with the current power command of the highest priority unconstrained energy storage unit to generate the virtual test power corresponding to the unconstrained energy storage unit. The virtual test power is substituted into the battery state prediction model to simulate the terminal voltage change, and the simulation results are obtained. If the simulation results determine that the terminal voltage of the unrestricted energy storage unit touches the preset cutoff voltage threshold within the preset safety buffer time, then the maximum allowable power of the unrestricted energy storage unit is calculated based on the internal resistance parameter in the electrochemical characteristic parameters and the preset cutoff voltage threshold. The allocation value not exceeding the maximum allowable power is issued to the unconstrained energy storage unit, and the remaining power deficit is transferred to the next priority unconstrained energy storage unit.

8. An energy storage controllable quantity regulation system, characterized in that, The system includes: one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the energy storage controllable quantity regulation system, the system performs the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are run on the energy storage controllable quantity regulation system, the system performs the method as described in any one of claims 1-7.

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