A method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of energy storage power stations

Abstract

This invention discloses a method for automatic SOC calibration and inter-cluster dynamic balancing collaborative control of an energy storage power station. The method involves collecting the state-of-charge (SOC) parameters of preset sodium-ion supercapacitor clusters within the energy storage power station; calculating the corresponding rate of charge change based on the SOC parameters to determine if calibration is abnormal; when an anomaly exists, dividing the clusters into operating modes according to the SOC parameters and setting a calibration cycle for each mode; adding a local balancing buffer on a preset management terminal of the energy storage power station, allowing the return of the previous calibration value within a preset time period and recording the number of times the previous calibration value is returned; when the number of times the previous calibration value is returned exceeds a preset number, sending a calibration refresh request to the management terminal of the energy storage power station to shorten the calibration cycle, forcibly obtaining the latest SOC value, and filling in missing estimated values; optimizing the operating efficiency of the energy storage power station through an adaptive calibration algorithm and hierarchical balancing, reducing redundant operations, and compensating for missing data through historical data trends to improve system safety and reliability.

Description

Technical Field

[0001] This invention relates to the field of energy storage power station control technology, and in particular to a method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of energy storage power stations. Background Technology

[0002] Sodium-ion supercapacitors are an important energy storage technology in current energy storage systems. With their advantages of fast response and high power density, they have been widely used in power systems, electric vehicles, and other applications that require rapid energy storage and release. However, in practical applications, the instability of the State of Charge (SOC) reading accuracy and balance of sodium-ion supercapacitor clusters leads to performance fluctuations and shortened lifespan of the energy storage system, which in turn affects the overall efficiency and reliability of the energy storage system.

[0003] To address the aforementioned issues, a control method is needed that can accurately read and calibrate the SOC value of sodium-ion supercapacitor clusters and achieve dynamic equalization between clusters. Traditional calibration methods often rely on fixed calibration schedules or empirical indicators, which are ill-suited to handle the complex operating conditions of power plants, making it difficult to guarantee the accuracy and timeliness of calibration. While existing dynamic equalization mechanisms can improve the uneven SOC distribution to some extent, they still lack an effective calibration mechanism to compensate for potential errors during the equalization process. Furthermore, excessively frequent or untimely equalization operations can increase the system's burden and affect its stable operation. Summary of the Invention

[0004] In view of this, the present invention proposes a collaborative control method for automatic SOC calibration and inter-cluster dynamic balancing of energy storage power stations, which can solve the problem that existing automatic SOC balancing technology is unable to cope with the inconsistency between multiple battery clusters.

[0005] The technical solution of this invention is implemented as follows:

[0006] A method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of an energy storage power station includes the following steps:

[0007] Step S1: Collect the state of charge parameters of the preset sodium-ion supercapacitor cluster in the energy storage power station. The state of charge parameters include SOC value, charging and discharging current and temperature distribution.

[0008] Step S2: Calculate the corresponding rate of charge change based on the state of charge parameters, and determine whether the calibration is abnormal based on the rate of charge change.

[0009] Step S3: When a calibration anomaly exists, the working mode of the sodium ion supercapacitor cluster is divided according to the state of charge parameters, and a calibration cycle is set for each working mode.

[0010] Step S4: Add a local balancing buffer to the preset management terminal of the energy storage power station, allowing the return of the previous calibration value within a preset time period, and record the number of times the previous calibration value is returned;

[0011] Step S5: When the number of times the previous calibration value is returned exceeds the preset number, a calibration refresh request is sent to the management terminal of the energy storage power station;

[0012] Step S6: Based on the calibration refresh request, shorten the calibration cycle, force the acquisition of the latest SOC value, compare the latest SOC value with the previous calibration value, and fill in the missing estimated value with the latest SOC value.

[0013] Preferably, the formula for calculating the SOC value is:

[0014] ;

[0015] Where t represents the upper limit of time. Let t be the integration variable in the integral formula. The initial state of charge, For rated capacity, For the first The charging and discharging current at any given time, For the first The charging and discharging efficiency at any given time. The dynamic calibration compensation term is corrected based on temperature distribution and historical data trends.

[0016] Preferably, the state of charge parameters collected in step S1 are transmitted to the management terminal through a communication protocol format. The communication quality during the transmission process is detected by the operation log of the sodium-ion supercapacitor cluster. The operation log includes the average online rate, the number of disconnections, and the disconnection time period. If at least one of the average online rate, the number of disconnections, or the disconnection time period does not reach the preset quality threshold, the corresponding sodium-ion supercapacitor cluster is identified as a high packet loss cluster, and the reporting interval of the state of charge parameters of the high packet loss cluster is adjusted.

[0017] Preferably, the formula for calculating the rate of change of charge is:

[0018] ;

[0019] in The rate of change of charge, The SOC value at the current moment. The SOC value at the previous calibration time. For the calibration interval, if If this occurs, it is judged as a calibration anomaly.

[0020] Preferably, the specific steps of step S3 are as follows:

[0021] Set the normalized score of the SOC value SOC normalized scores corresponding to SOC values ​​ranging from 0-50%, 50%-80%, and 80%-100%. The values ​​are 0, 0.5, and 1 respectively.

[0022] Set the normalized score of the charging and discharging current. The charging and discharging current range is: 100A-300A The normalized score of the current at 300A The values ​​are 0, 0.5, and 1 respectively.

[0023] Temperature normalization score for setting temperature distribution Normalized scores for temperatures ranging from -20 to 25°C, 25 to 40°C, and 40 to 60°C. The values ​​are 0, 0.5, and 1 respectively.

[0024] Construct the scoring calculation formula: ,in Indicates the weighting coefficient;

[0025] according to S Value partitioning working mode: S A value ≥0.8 indicates a high load, while 0.4 ≤ S A value less than 0.8 indicates a medium load. S A value less than 0.4 indicates a low load.

[0026] Preferably, before adding a local equalization buffer in step S4, the state of charge update mode of the sodium ion supercapacitor cluster is obtained based on the data sampling period preset by the management terminal. The state of charge update mode includes timed sampling, event triggering, and on-demand request.

[0027] Determine whether the state of charge update mode matches the management terminal; if not, construct a balancing buffer strategy for the sodium ion supercapacitor clusters, dynamically adjust the effective time range of the balancing buffer strategy according to the frequency of state of charge updates, and restrict preset unsuitable clusters from balancing buffering according to the balancing buffer strategy; wherein, the unsuitable clusters include transient charge change clusters and high-frequency signal interference clusters.

[0028] Preferably, different operating modes require matching equalization frequencies. If they do not match, the equalization mechanism is dynamically adjusted through a hierarchical equalization structure. The hierarchical equalization structure includes a local equalization layer, a distributed equalization layer, and a cloud equalization layer. The local equalization layer is responsible for handling transient charge change clusters, and the distributed equalization layer optimizes high-frequency signal interference clusters. The equalization mechanism includes active equalization and passive equalization.

[0029] Preferably, the specific steps of step S5, which involves sending a calibration refresh request to the management terminal of the energy storage power station, are as follows: The communication mode of the sodium-ion supercapacitor cluster is detected based on its communication protocol format; it is determined whether the communication mode requires enabling a preset secure communication; if so, the corresponding calibration request content is constructed based on the communication address of the sodium-ion supercapacitor cluster; and the request interval of the calibration request content is dynamically adjusted according to the state of charge parameters. The communication address includes an IP address, a physical address, and an authentication key; the calibration request content includes specifying the target cluster ID, setting the charging time range, and increasing the request priority.

[0030] Preferably, in step S6, after comparing the latest SOC value with the previous calibration value, the missing value is calculated using a weighted moving average method based on the trend of pre-recorded historical data. The calculation formula is as follows:

[0031] ;

[0032] in For time decay weight, α The attenuation coefficient is... n =5, The SOC values ​​are the values ​​of the i sampling points before the missing time step.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] (1) This invention dynamically adjusts the calibration cycle through an adaptive calibration algorithm and obtains the latest SOC value by combining a forced refresh mechanism, which solves the problems of calibration lag and deviation exceeding the threshold in traditional methods, and ensures the consistency of cluster SOC values ​​and real-time response capability.

[0035] (2) The present invention uses a layered equalization structure to perform equalization operation, reducing redundant energy consumption. At the same time, it dynamically switches between active and passive equalization mechanisms to improve equalization efficiency under complex working conditions, reducing ineffective equalization operations by about 40% compared with the prior art.

[0036] (3) This invention allows for the temporary return of historical calibration values ​​through local balancing buffering, and fills in missing data by combining the trend analysis of historical data over the past 30 days, effectively addressing scenarios of communication anomalies or data loss. The design of communication quality monitoring and dynamic adjustment of reporting intervals further ensures the stable operation of the system under extreme conditions. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of an automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control method for an energy storage power station according to the present invention. Detailed Implementation

[0039] To better understand the technical content of this invention, a specific embodiment is provided below, and the invention will be further described in conjunction with the accompanying drawings.

[0040] See Figure 1 The present invention provides a method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of an energy storage power station, comprising the following steps:

[0041] Step S1: Collect the state of charge parameters of the preset sodium-ion supercapacitor cluster in the energy storage power station. The state of charge parameters include SOC value, charging and discharging current and temperature distribution.

[0042] The formula for calculating the SOC value is:

[0043] ;

[0044] Where t represents the upper limit of time. Let t be the integration variable in the integral formula. The initial state of charge, For rated capacity, For the first The charging and discharging current at any given time, For the first The charging and discharging efficiency at any given time. The dynamic calibration compensation term is corrected based on temperature distribution and historical data trends.

[0045] In actual operation, a sensor network connects to the sodium-ion supercapacitor cluster to collect the cluster's state of charge (SOC) parameters in real time. These parameters include SOC value, charging / discharging current (range -500A to 500A), and temperature distribution (-20℃ to 60℃). The SOC parameters are transmitted to the management terminal via communication protocols including Modbus, CAN bus, and TCP / IP. The communication quality during SOC parameter transmission is monitored by the operation log. The log records the average online rate (preset quality threshold 95%), the number of disconnections (no more than 5 per month), and the duration of each disconnection (no more than 10 minutes). If the communication quality fails to meet the preset quality threshold, for example, if the average online rate drops below 90% or the number of disconnections reaches 8 per month, the management terminal identifies the high packet loss cluster and dynamically adjusts the SOC reporting interval from 10 seconds / time to 30 seconds / time to reduce the transmission frequency.

[0046] Step S2: Calculate the corresponding rate of charge change based on the state of charge parameters, and determine whether there is a calibration anomaly based on the rate of charge change. Calibration anomalies include SOC deviation exceeding the threshold (±5%) and decreased inter-cluster consistency (standard deviation > 3%).

[0047] During the judgment process, the rate of charge change is identified based on the preset state of charge update interval (5 minutes / time) of the sodium ion supercapacitor cluster. The calculation formula is as follows:

[0048] ;

[0049] in The rate of change of charge, The SOC value at the current moment. The SOC value at the previous calibration time. For the calibration interval, if If an error occurs, it is considered a calibration anomaly. If the error is within acceptable limits, further checks are performed on data integrity, which includes both data values ​​and data format. If an abnormal cluster exists, such as three consecutive data out-of-range occurrences, a batch of charge resampling requests is sent through the management terminal, with three requests sent at a time to ensure data accuracy.

[0050] Step S3: When calibration anomalies occur, the operating modes of the sodium-ion supercapacitor cluster are divided according to the state of charge parameters, and a calibration cycle is set for each operating mode. The specific steps are as follows:

[0051] Set the normalized score of the SOC value SOC normalized scores corresponding to SOC values ​​ranging from 0-50%, 50%-80%, and 80%-100%. The values ​​are 0, 0.5, and 1 respectively.

[0052] Set the normalized score of the charging and discharging current. The charging and discharging current range is: 100A-300A The normalized score of the current at 300A The values ​​are 0, 0.5, and 1 respectively.

[0053] Temperature normalization score for setting temperature distribution Normalized scores for temperatures ranging from -20 to 25°C, 25 to 40°C, and 40 to 60°C. The values ​​are 0, 0.5, and 1 respectively.

[0054] Construct the scoring calculation formula: ,in In this embodiment, the weighting coefficient is represented. =0.5, =0.3, =0.2;

[0055] according to S Value partitioning working mode: A value ≥0.8 indicates a high load, while 0.4 ≤ A value less than 0.8 indicates a medium load. A value less than 0.4 indicates a low load.

[0056] Different operating modes correspond to different SOC calibration cycles, which are calculated using a unified time base. For example, in high-load mode, the calibration cycle is 5 minutes, while in low-load mode, the calibration cycle is 30 minutes.

[0057] Before adding a local equalization buffer, the state of charge update mode of the sodium ion supercapacitor cluster is obtained based on the data sampling period preset by the management terminal. The state of charge update mode includes timed sampling, event triggering, and on-demand request.

[0058] Determine whether the state of charge update mode matches the management terminal; if not, construct a balancing buffer strategy for the sodium ion supercapacitor clusters, dynamically adjust the effective time range of the balancing buffer strategy according to the frequency of state of charge updates, and restrict preset unsuitable clusters from balancing buffering according to the balancing buffer strategy; wherein, the unsuitable clusters include transient charge change clusters and high-frequency signal interference clusters.

[0059] Different operating modes require matching equalization frequencies: 50Hz for high load, 30Hz for medium load, and 10Hz for low load. If mismatched, the equalization mechanism is dynamically adjusted through a hierarchical equalization structure. This equalization mechanism includes active and passive equalization. The hierarchical equalization structure includes a local equalization layer, a distributed equalization layer, and a cloud equalization layer. The local equalization layer is responsible for handling transient charge change clusters with a change range within ±10%. The distributed equalization layer is optimized for high-frequency signal interference clusters (interference above 1kHz). The cloud equalization layer is mainly used for global data analysis and long-term trend prediction, with a sampling period of 24 hours.

[0060] Active and passive balancing mechanisms dynamically switch under different scenarios. For example, when inter-cluster consistency decreases, the active balancing mechanism is activated first, using the differential balancing detection module to balance only the data with discrepancies, i.e., clusters with a difference > 2%, reducing redundant operations. The inter-cluster difference is calculated using the following formula:

[0061] Difference = 100%

[0062] in, For the first i Real-time SOC values ​​of each cluster group This represents the average SOC value across all clusters. When the difference exceeds 2%, an active equalization operation is triggered. The differential equalization detection module analyzes the differences in charge state between clusters to determine the specific targets requiring equalization, thereby improving equalization efficiency.

[0063] Step S4: Add a local balancing buffer to the preset management terminal of the energy storage power station, allowing the return of the previous calibration value within a preset time period, and recording the number of times the previous calibration value is returned, in order to deal with temporary data loss or abnormal situations.

[0064] The management terminal identifies the operating status of the cluster based on preset status monitoring; it determines whether the operating status is offline; if not, it collects the timeout information of the cluster, obtains the data loss rate when updating the state of charge based on the timeout information, compares the data loss rate with the preset data receiving timestamp of the management terminal, and detects the corresponding delay and lost data.

[0065] Step S5: When the number of times the previous calibration value is returned exceeds the preset number, a calibration refresh request is sent to the management terminal of the energy storage power station;

[0066] The specific steps for sending a calibration refresh request to the management terminal of the energy storage power station are as follows: The communication mode of the sodium-ion supercapacitor cluster is detected based on its communication protocol format, including Modbus, CAN bus, and TCP / IP. It is determined whether the communication mode requires enabling preset secure communication. If so, the corresponding calibration request content is constructed based on the communication address of the sodium-ion supercapacitor cluster. The request interval of the calibration request content is dynamically adjusted according to the state of charge parameters. The communication address includes an IP address, a physical address, and an authentication key. The calibration request content includes specifying the target cluster ID, setting the charging time range, and increasing the request priority.

[0067] Step S6: Based on the calibration refresh request, shorten the calibration cycle, force the acquisition of the latest SOC value, compare the latest SOC value with the previous calibration value, and calculate the missing estimated value using the weighted moving average method based on the trend of pre-collected historical data (data of the last 30 days). The calculation formula is as follows:

[0068] ;

[0069] in For time decay weight, α This is the attenuation coefficient, with a value of 0.1. More recent data has a higher weight. n =5 (take the 5 most recent valid data points) The SOC values ​​are the values ​​of the i sampling points before the missing time step.

[0070] After obtaining the missing estimates, the latest SOC values ​​can be filled in, ensuring the integrity and consistency of the state of charge data.

[0071] In practical applications, suppose a cluster experiences abnormal state of charge (SOC) updates due to high-frequency signal interference. In this case, the acquisition module detects a decline in communication quality, the management terminal identifies the cluster as unsuitable, and restricts its equalization caching. Simultaneously, the distributed equalization layer in the hierarchical equalization structure initiates a passive equalization mechanism, using the differential equalization detection module to equalize only the affected data portion. If the anomaly persists (e.g., exceeding 10 minutes) and the previous calibration value is returned more than a preset number (5 times), the calibration refresh request module shortens the calibration interval, forcibly acquiring the latest SOC value. The historical data trend analysis module calculates missing estimates based on historical data trends to fill in the data gaps caused by the anomaly.

[0072] Furthermore, the operational requirements of the management end determine the data collection and adjustment strategy for the balancing frequency. For example, in medium load mode, if the balancing frequency does not match the state of charge (SCC) requirements, the balancing mechanism is dynamically adjusted through a hierarchical balancing structure. Active and passive balancing mechanisms switch based on the actual operating state of the clusters (e.g., switching to passive balancing when the difference is <3%), ensuring the efficiency and targeted nature of the balancing operation. Simultaneously, the data sampling period preset by the management end (1 second / time for high load, 5 seconds / time for medium load, and 10 seconds / time for low load) determines the SCC update mode for the clusters, including timed sampling, event triggering, and on-demand requests. If the SCC update mode does not match the management end's requirements, such as a 10-second sampling period in medium load mode, a balancing cache strategy is constructed to dynamically adjust the effective time range of the balancing cache.

[0073] Through the above-described embodiments, the present invention achieves the goal of automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of sodium-ion supercapacitor energy storage power stations, solves the shortcomings of existing technologies, and improves the operating efficiency, consistency and safety of energy storage power stations.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of an energy storage power station, characterized in that, Includes the following steps: Step S1: Collect the state of charge parameters of the preset sodium-ion supercapacitor cluster in the energy storage power station. The state of charge parameters include SOC value, charging and discharging current and temperature distribution. Step S2: Calculate the corresponding rate of charge change based on the state of charge parameters, and determine whether the calibration is abnormal based on the rate of charge change. Step S3: When a calibration anomaly exists, the working mode of the sodium ion supercapacitor cluster is divided according to the state of charge parameters, and a calibration cycle is set for each working mode. Step S4: Add a local balancing buffer to the preset management terminal of the energy storage power station, allowing the return of the previous calibration value within a preset time period, and record the number of times the previous calibration value is returned; Step S5: When the number of times the previous calibration value is returned exceeds the preset number, a calibration refresh request is sent to the management terminal of the energy storage power station; Step S6: Based on the calibration refresh request, shorten the calibration cycle, force the acquisition of the latest SOC value, compare the latest SOC value with the previous calibration value, and fill in the missing estimated value after calculating the latest SOC value. The formula for calculating the rate of change of charge is: ； in The rate of change of charge, The SOC value at the current moment. The SOC value at the previous calibration time. For the calibration interval, if When this occurs, it is judged as a calibration anomaly; The specific steps of step S3 are as follows: Set the normalized score of the SOC value SOC normalized scores corresponding to SOC values ​​ranging from 0-50%, 50%-80%, and 80%-100%. The values ​​are 0, 0.5, and 1 respectively. Set the current normalization score for charging and discharging currents. The charging and discharging current range is: 100A-300A The normalized score of the current at 300A The values ​​are 0, 0.5, and 1 respectively. Temperature normalization score for setting temperature distribution Normalized scores for temperatures ranging from -20 to 25°C, 25 to 40°C, and 40 to 60°C. The values ​​are 0, 0.5, and 1 respectively. Construct the scoring calculation formula: ,in Indicates the weighting coefficient; according to S Value partitioning working mode: S A value ≥0.8 indicates a high load, while 0.4 ≤ S A value less than 0.8 indicates a medium load. S A value less than 0.4 indicates a low load. Before adding a local equalization buffer in step S4, the state of charge update mode of the sodium ion supercapacitor cluster is obtained based on the data sampling period preset by the management terminal. The state of charge update mode includes timed sampling, event triggering, and on-demand request. Determine whether the state of charge update mode matches the management terminal; if not, construct a balancing buffer strategy for sodium ion supercapacitor clusters, dynamically adjust the effective time range of the balancing buffer strategy according to the frequency of state of charge update, and restrict preset unsuitable clusters from balancing buffering according to the balancing buffer strategy; wherein, the unsuitable clusters include transient charge change clusters and high-frequency signal interference clusters. Different operating modes require matching equalization frequencies. If they do not match, the equalization mechanism is dynamically adjusted through a hierarchical equalization structure. The hierarchical equalization structure includes a local equalization layer, a distributed equalization layer, and a cloud equalization layer. The local equalization layer is responsible for handling transient charge change clusters, and the distributed equalization layer is optimized for high-frequency signal interference clusters. The equalization mechanism includes active equalization and passive equalization.

2. The method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of an energy storage power station according to claim 1, characterized in that, The formula for calculating the SOC value is as follows: ； Where t represents the upper limit of time. Let t be the integration variable in the integral formula. The initial state of charge, For rated capacity, For the first The charging and discharging current at any given time, For the first The charging and discharging efficiency at any given time. The dynamic calibration compensation term is corrected based on temperature distribution and historical data trends.

3. The method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of an energy storage power station according to claim 1, characterized in that, The state of charge parameters collected in step S1 are transmitted to the management terminal through a communication protocol format. The communication quality during the transmission process is detected by the operation log of the sodium ion supercapacitor cluster. The operation log includes the average online rate, the number of disconnections, and the time period of disconnection. If at least one of the average online rate, the number of disconnections, or the time period of disconnection fails to reach the preset quality threshold, the corresponding sodium ion supercapacitor cluster is identified as a high packet loss cluster, and the reporting interval of the state of charge parameters of the high packet loss cluster is adjusted.

4. The method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of an energy storage power station according to claim 1, characterized in that, The specific steps of sending a calibration refresh request to the management terminal of the energy storage power station in step S5 are as follows: Based on the communication protocol format of the sodium-ion supercapacitor cluster, the communication mode of the sodium-ion supercapacitor cluster is detected, and it is determined whether the communication mode needs to enable preset secure communication. If so, the corresponding calibration request content is constructed according to the communication address of the sodium-ion supercapacitor cluster, and the request interval of the calibration request content is dynamically adjusted according to the state of charge parameters. The communication address includes an IP address, a physical address, and an authentication key, and the calibration request content includes specifying the target cluster ID, setting the charging time range, and increasing the request priority.

5. The method for automatic SOC calibration and inter-cluster dynamic equilibrium collaborative control of an energy storage power station according to claim 1, characterized in that, In step S6, after comparing the latest SOC value with the previous calibration value, the missing value is calculated using a weighted moving average method based on the trend of the pre-collected historical data. The calculation formula is as follows: ； in For time decay weight, α The attenuation coefficient is... n =5, The SOC values ​​are the values ​​of the i sampling points before the missing time step.

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