Monitoring control method for energy storage device in micro-grid

Abstract

The invention discloses a monitoring control method for energy storage devices in a micro-grid, and relates to the technical field of micro-grids, and the method comprises the steps: carrying out the grouping of the energy storage devices according to the analysis of an initial power utilization strategy result, and enabling the energy storage devices to comprise a high-level group, a middle-level group and a low-level group; when communication interruption is detected, performing local preliminary droop control by using a high-dependent group to form a voltage reference and a frequency reference; local measurement results of the middle leaning group and the low leaning group are monitored in real time, difference values are calculated respectively, if the difference values are smaller than an error range, a power supply system is accessed, and power balance is achieved through comprehensive droop control; through the collaborative design of dynamic marshalling, local droop control and topology dynamic reconstruction, the defects of an existing energy storage system in the aspects of communication interruption, circulation loss and black-start capability are overcome, through the differentiated design of a high dependent group, a middle dependent group and a low dependent group, autonomous power supply and circulation suppression in a communication-free mode are achieved, and the reliability of the system is improved. And the reliability and the efficiency of the energy storage system under extreme working conditions are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and particularly to a method for monitoring and controlling energy storage devices in a microgrid. Background Art

[0002] With the continuous evolution of distributed energy technologies, microgrids, as a key energy supply mode, have been continuously expanding their application scope in the power system. However, the layout of microgrids in the monitored area (such as residential communities or industrial parks) shows a decentralized characteristic, leading to challenges in centralized coordinated control. Moreover, problems such as voltage fluctuations and power imbalances frequently occur during actual operation.

[0003] In traditional energy storage systems, the centralized control mode relies on a communication network to maintain voltage / frequency stability. However, when the communication is interrupted, the system is prone to instability due to the inability to generate a unified reference value, with weak black start ability and long recovery time. In addition, when energy storage devices operate in parallel under a fixed topological structure, circulating currents are likely to occur, causing energy loss and equipment overheating. Moreover, the ungrouped unified control strategy is difficult to take into account the reliability differences of different devices, resulting in poor SOC balance, local overload, and high risk of fault spread. Summary of the Invention

[0004] This application solves the problem in the prior art that energy storage devices cannot actively adapt to sudden environmental changes, resulting in reduced service life, and realizes the technical effect of improving the dynamic adaptability of energy storage devices through hierarchical control in multiple optimization intervals, thereby improving the service life of energy storage devices.

[0005] This application provides a method for monitoring and controlling energy storage devices in a microgrid, including: S1: Obtain the renewable energy volatility and load prediction error rate based on the basic data set and the load prediction model, and set the deviation threshold and adjustment margin based on the mapping relationship; S2: Determine the charge level according to the real-time state of charge of each energy storage device, and calculate the single-cycle capacity attenuation rate; determine the optimal charge and discharge rate of each energy storage device according to the single-cycle capacity attenuation rate and the charge level, and generate an initial power consumption strategy in combination with the deviation threshold and the adjustment margin; S3: Group the energy storage devices according to the analysis results of the initial power consumption strategy, including the high-reliance group, the medium-reliance group, and the low-reliance group; when a communication interruption is detected, use the high-reliance group for local preliminary droop control to form a voltage reference and a frequency reference; monitor the local measurement results of the medium-reliance group and the low-reliance group in real time, and calculate the differences from the voltage reference and the frequency reference respectively. If both differences are less than the error range, connect to the power supply system and achieve power balance through comprehensive droop control.

[0006] Further, the method further includes: S4: Monitor the state of charge and circulating current in real time. When it is detected that the circulating current exceeds the preset circulating current threshold, several devices are switched to a series structure by the medium proximity group through a solid-state circuit breaker to reduce the circulating current path; S5: Based on the physical parameters and topological structure of the energy storage device, integrate the historical fault database and real-time status monitoring data to establish a digital twin model; when a fault risk is detected, obtain the grouping status and state of charge distribution data, re-determine the grouping scheme based on the digital twin model, simulate the performance indicators under different grouping strategies, and generate an evaluation result; generate a topological reconstruction path according to the evaluation result, and predict potential fault points and the probability of fault occurrence.

[0007] Furthermore, step S2 further includes: If the single-cycle capacity attenuation rate is higher than the attenuation threshold, it is marked as a risk point; set the monitoring period and record the total number of risk points within the monitoring period; obtain the stable value of each energy storage device within the monitoring period according to the change in the state of charge level; determine the optimal charge and discharge rate of each energy storage device according to the total number of risk points and the stable value.

[0008] Furthermore, step S3 further includes: initializing the droop coefficient and topological reconstruction strategy; The initialization of the droop coefficient includes: determining the microgrid system parameters, setting the droop coefficient range and grouping to set the corresponding droop coefficients; The initialization of the topological reconstruction strategy includes: real-time monitoring of the communication status. When the communication is interrupted, disconnect the connections of the medium proximity group and the low proximity group to the bus, and only retain the power supply of the high proximity group. Based on the high proximity group, form a voltage reference and a frequency reference through local droop control.

[0009] Furthermore, the evaluation result includes the state of charge balance, power support ability, and circulating current suppression effect of each grouping scheme; the performance indicators include power distribution, circulating current path, voltage fluctuation, and frequency fluctuation.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages: Through the collaborative design of dynamic grouping, local droop control, and topological dynamic reconstruction, the deficiencies of the existing energy storage system in terms of communication interruption, circulating current loss, and black start ability are solved. Through local measurement and droop control, the voltage / frequency self-stabilization of the energy storage device in the non-communication mode is realized to maintain the power supply of critical loads; through the topological dynamic reconstruction technology, the circulating current loss in the off-grid state is suppressed, and the energy utilization efficiency of the energy storage system is improved; the grouping strategy not only optimizes the system resource allocation, but also realizes autonomous power supply and circulating current suppression in the non-communication mode through the differential design of the high proximity group, medium proximity group, and low proximity group, significantly improving the reliability and efficiency of the energy storage system under extreme working conditions. Description of the Drawings

[0011] Figure 1 This is a schematic flow chart of a monitoring and control method for an energy storage device in a microgrid according to an embodiment of the present invention. Specific embodiments

[0012] To facilitate the understanding of the present invention, the present application will be described more comprehensively with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, however, the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs; the terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0014] Embodiment 1: As Figure 1 shown, a monitoring and control method for an energy storage device in a microgrid, the method comprising: S1: Obtain a basic data set and a load prediction model to obtain the renewable energy volatility and the load prediction error rate, and set a deviation threshold and an adjustment margin based on the mapping relationship.

[0015] The basic data set includes energy storage basic data, power data, and environmental data; the energy storage basic data includes the state of charge, charge and discharge power, charge and discharge efficiency, health status (such as battery internal resistance, temperature characteristics, number of cycles), and charge and discharge rate of the energy storage device; the state of charge refers to the current power state of the energy storage device, usually expressed as a percentage; the charge and discharge efficiency is the energy conversion efficiency of the energy storage device during the charge and discharge process; the health status includes battery internal resistance, temperature characteristics, number of cycles, etc., reflecting the overall performance and service life of the energy storage device; the charge and discharge rate is the charge and discharge rate limit of the energy storage device under different states of charge (SOC). The energy storage device mentioned in this application is an energy storage battery or an energy storage battery pack, and several energy storage devices may be included in the microgrid.

[0016] The power data includes load data, new energy generation data, and electricity price signals; the load data refers to the real-time monitored power load, including peak load, average load, and load volatility; the new energy generation data refers to the power generation and its fluctuation data of new energy such as solar energy and wind energy; the electricity price signal refers to the electricity price and the electricity price change in different time periods, which is used to optimize the charge and discharge strategy of the energy storage device.

[0017] The environmental data includes temperature and humidity. The temperature refers to the temperature of the environment where the energy storage device is located, and different temperatures have varying degrees of impact on the performance and lifespan of the battery. Humidity is for targeted analysis of certain energy storage devices. If there are no energy storage devices that are highly sensitive to humidity, only the normal humidity range needs to be monitored.

[0018] The regulation margin refers to the range of dynamic adjustment capabilities reserved by the energy storage device under the charge-discharge strategy to cope with the fluctuations in the power generation of renewable energy. It allows the energy storage device to absorb or release additional electricity within a short period to smooth out the instability of renewable energy generation.

[0019] When the power generation of renewable energy changes rapidly (such as a sudden decrease in solar energy due to cloud cover), the energy storage device can compensate within the regulation margin by adjusting its charge-discharge rate, thereby maintaining a stable output of the power grid.

[0020] The regulation margin provides a larger operating space for the system, enabling the microgrid to respond more flexibly to changes in the power generation of renewable energy and reducing its dependence on the traditional power grid.

[0021] There is a positive correlation between the regulation margin and the volatility of renewable energy. The higher the volatility of renewable energy, the greater the uncertainty in power generation. Therefore, a larger regulation margin is required to cope with this uncertainty. By monitoring the volatility of renewable energy, the size of the regulation margin is dynamically adjusted to ensure that the energy storage device has sufficient adjustment capabilities to maintain the stability of the power grid during power generation fluctuations.

[0022] Specifically, the volatility of renewable energy is obtained based on the basic dataset, including: extracting the renewable energy generation data from the basic dataset, calculating the volatility of renewable energy (such as the minute-level change rate of solar / wind power generation), obtaining the power value of renewable energy at a certain moment, and then obtaining the power value at the next moment. The percentage of the ratio between the two is used as the volatility of renewable energy. In practical applications, the average power value within a certain period can also be selected as the reference value, and compared with subsequent moments respectively to obtain the volatility of renewable energy at subsequent moments. When the volatility is lower than the preset volatility threshold (such as 10%), the regulation margin is reduced (such as ±5%). When the volatility increases, according to the coupling relationship between the volatility and the margin (such as when the volatility increases by 5%, the margin increases by 20%), the regulation margin is dynamically expanded to tolerate larger power deviations.

[0023] In high-volatility scenarios, using the expanded regulation margin, the energy storage device is allowed to adjust its charge and discharge within a larger range to smooth out the fluctuations in new energy generation. The state of charge (SOC) and charge-discharge rate of the energy storage device are monitored in real time to ensure that the adjustment process does not exceed the device limits.

[0024] Set the deviation threshold and adjustment margin based on the mapping relationship, which is based on the preset fluctuation threshold range, error threshold range, deviation reference value, and adjustment reference value. Adjust the adjustment reference value corresponding to the proportional relationship between the renewable energy volatility and the fluctuation threshold range to determine the adjustment margin; adjust the deviation reference value corresponding to the proportional relationship between the load prediction error rate and the error threshold range to determine the deviation threshold.

[0025] Specifically, it includes: If the renewable energy volatility is within the fluctuation threshold range, set the adjustment reference value as the adjustment margin; if the renewable energy volatility is not within the fluctuation threshold range, judge the floating direction; if the floating direction is up (i.e., greater than the upper limit value of the fluctuation threshold range), calculate the floating ratio of the renewable energy volatility to the upper limit value of the fluctuation threshold range, and adjust the adjustment reference value upward according to the floating ratio (i.e., increase the corresponding ratio, such as adjusting the adjustment reference value by 5% for every 1% exceeding the floating ratio), and set the adjusted adjustment reference value as the adjustment margin; conversely, if the floating direction is down (i.e., less than the lower limit value of the fluctuation threshold range), calculate the floating ratio of the renewable energy volatility to the lower limit value of the fluctuation threshold range, and adjust the adjustment reference value downward according to the floating ratio (i.e., reduce the corresponding ratio, such as adjusting the adjustment reference value by 2% for every 1% reduction in the floating ratio), and set the adjusted adjustment reference value as the adjustment margin. Use the absolute value of the volatility for comparison and calculate the floating ratio.

[0026] If the load prediction error rate is within the error threshold range, set the deviation reference value as the deviation threshold; if the load prediction error rate is not within the error threshold range, judge the floating direction; if the floating direction is up (i.e., greater than the upper limit value of the error threshold range), calculate the floating ratio of the load prediction error rate to the upper limit value of the error threshold range, and adjust the deviation reference value upward according to the floating ratio (i.e., increase the corresponding ratio, such as adjusting the deviation reference value by 7% for every 1% exceeding the floating ratio), and set the adjusted deviation reference value as the deviation threshold; conversely, if the floating direction is down (i.e., less than the lower limit value of the error threshold range), calculate the floating ratio of the load prediction error rate to the lower limit value of the error threshold range, and adjust the deviation reference value downward according to the floating ratio (i.e., reduce the corresponding ratio, such as adjusting the deviation reference value by 3% for every 1% reduction in the floating ratio), and set the adjusted deviation reference value as the deviation threshold. The corresponding ratios mentioned in the above adjustments are not the same ratio and need to be set according to the historical data analysis of the specific energy storage device. The above corresponding ratio relationship is only an example for understanding and not real data.

[0027] For example, a microgrid system includes a solar power station and an energy storage battery pack. The power of the solar power station fluctuates greatly due to weather conditions. To maintain the stability of the power grid, it is necessary to dynamically adjust the adjustment margin of the energy storage battery pack according to the volatility of the solar power.

[0028] Set the reference adjustment margin to ±5% of the rated power (assuming the rated power of the energy storage battery pack is 100 kW, then the reference margin is ±5 kW).

[0029] Set the fluctuation threshold range to [10%, 15%], and set the mapping relationship: when the volatility exceeds the low volatility threshold (10%) but is lower than the high volatility threshold (15%), use the reference adjustment margin; when the volatility exceeds the upper limit of the fluctuation threshold range, for every 1% exceeded, the adjustment margin is increased by 5%, and when the volatility is lower than the lower limit of the fluctuation threshold range, for every 1% exceeded, the adjustment margin is decreased by 2%.

[0030] For example, at time T1, the solar power is 80 kW, and the energy storage battery pack outputs 20 kW (to maintain the total grid power at 100 kW) to calculate the volatility. Assuming the power at the previous moment was 75 kW, then the volatility is 6.67%. Since the volatility is lower than the lower limit of the fluctuation threshold range, the adjusted margin is reduced to the reference value of ±4.5 kW.

[0031] At time T2, the solar power is 60 kW, and the output of the energy storage battery pack increases to 40 kW (to maintain the total grid power at 100 kW) to calculate the volatility. The power at time T1 was 80 kW, so the volatility is -25% (the absolute value of the volatility is used for comparison); according to the mapping relationship, the margin is increased by 50%, that is, the new adjusted margin is ±7.5 kW (150% of the original ±5 kW, which may be rounded or adjusted according to system rules in actual adjustment).

[0032] With the new adjusted margin of ±6 kW, the energy storage battery pack can adjust its output within the range of 34 kW to 46 kW (considering the original output of 40 kW). In fact, since the solar power has dropped to 60 kW, the output of the energy storage battery pack increasing to 40 kW is sufficient to maintain grid stability, but the additional margin (±7.5 kW) provides a buffer for possible further fluctuations in the future. Assume the solar power gradually recovers to 70 kW. Recalculate the volatility and adjust the adjusted margin according to the new volatility. If the volatility drops below the low volatility threshold, the adjusted margin returns to the reference value of ±5 kW. The values given in the above examples are not real operating data and are only for illustrative purposes for easy understanding.

[0033] The deviation threshold refers to the upper limit of the allowable instantaneous power deviation, which is dynamically adjusted according to the change of the load prediction error rate and is used to control the power deviation of the energy storage device during the charging and discharging processes to ensure the stable operation of the power grid. Restricting the power deviation of the energy storage device during the charging and discharging processes reduces the risk of dispatching failure caused by excessive deviation. The deviation threshold can be dynamically adjusted according to the change of the load prediction error rate, thereby maintaining the stability of the system under different operating conditions. The higher the load prediction error rate, the greater the deviation between the prediction result and the actual load. Therefore, it is necessary to lower the deviation threshold to tighten the control and reduce the risk of dispatching failure. By monitoring the load prediction error rate and adjusting the deviation threshold, a closed-loop optimization mechanism can be formed to continuously improve the accuracy of load prediction.

[0034] Due to various factors (such as weather changes, uncertainties in user behavior, etc.), there are often errors in load prediction. To reduce the risk of dispatching failure caused by load prediction errors, it is necessary to dynamically adjust the deviation threshold to adapt to different prediction error situations.

[0035] When performing load forecasting, historical load data, real-time load data, and external influencing factor data are collected as initial data; specifically including: collecting microgrid load data for a past period (such as the past week, month), including daily and hourly load values; obtaining the real-time load data of the current microgrid for updating the forecasting model; collecting external factor data that may affect the load, such as weather forecasts (temperature, humidity, wind speed, light intensity, etc.), holiday information, special events, etc.; preprocessing the initial data, which includes missing value filling, outlier handling, and data normalization. The missing value filling uses linear interpolation to complete the data for missing periods, the outlier handling is to remove a small amount of abnormal data (such as extremely low load data during typhoon days), and the data normalization means scaling the load values to the interval [0,1]; dividing the processed initial data into a training set and a test set according to a ratio of 7:3, extracting key features, where the key features include time features, weather features, and economic features (real-time electricity price signals), and constructing a feature sequence; using a long short-term memory network basic model, inputting the historical feature sequence, and outputting the load forecasting value for a future period (such as 1 hour or 24 hours). Preset the model parameters as initial parameters, including the number of neurons in the hidden layer, the loss function, and the optimizer. The loss function uses the mean squared error. Input the training set to train the basic model and continuously optimize the model parameters, calculate the mean absolute percentage error. If the mean absolute percentage error is less than the preset threshold; use the validation set to validate the model, and judge the stability of the model according to the validation mechanism, and use the successfully validated model as the load forecasting model. Specifically, according to the load forecasting model, obtain the load forecasting error rate, input the real-time load data and the latest external influencing factor data into the trained load forecasting model, and output the load forecasting value for a future period (24 hours). At the end of each hour, compare the actual load data with the forecasting value to calculate the load forecasting error rate.

[0036] In this embodiment, the actual load is compared with the forecasted load, and the load forecasting error rate (the ratio of the absolute value of the difference between the actual load value and the forecasted value to the actual load value) is calculated. When the error rate is lower than the preset threshold (such as 5%), appropriately reduce or maintain the deviation threshold at the reference value (such as ±3%). When the error rate increases, according to the negative feedback relationship between the error rate and the threshold (such as when the error rate is 10%, the threshold is reduced to below ±3%, and the specific value is adjusted according to the system characteristics), reduce the deviation threshold to tighten the control and reduce the risk of dispatching failure.

[0037] In a high error rate scenario, strictly abide by the reduced deviation threshold, and reduce the impact of instantaneous power deviation on the power grid by finely controlling the charge and discharge of the energy storage device. Utilize the electricity price signal to preferentially charge during low electricity price periods and preferentially discharge during high electricity price periods to optimize the economy of the energy storage device.

[0038] Feed the adjustment margin adjustment data and threshold adjustment data back to the load forecasting model for closed-loop optimization to reduce the prediction error. Regularly evaluate key indicators such as the voltage volatility rate, curtailment rate of wind and solar power, and the life of energy storage devices of the microgrid to verify the effectiveness of the control scheme. According to the system performance evaluation results, timely adjust the margin adjustment strategy and threshold adjustment strategy to achieve the continuous optimization of the microgrid.

[0039] In this embodiment, the renewable energy volatility rate and the load forecasting error rate are obtained from the basic data set and the load forecasting model, the deviation threshold and the adjustment margin are dynamically set, an initial power consumption strategy is generated, and the energy storage device in the microgrid is controlled to charge and discharge according to the initial power consumption strategy.

[0040] In this embodiment, in the high volatility scenario, by expanding the adjustment margin, the fluctuations of new energy power generation are effectively smoothed, and the impact on the power grid is reduced; by dynamically matching the deviation tolerance, the curtailment rate of wind and solar power is reduced, and the utilization rate of renewable energy is improved. By reducing the deviation threshold, the risk of dispatching failure is reduced, and the stability and reliability of the power grid are improved; by optimizing the charge and discharge strategy of the energy storage device, the number of unnecessary operations is reduced, the life of the energy storage device is extended, and the operation cost is reduced.

[0041] The purpose of this embodiment is to dynamically adjust the adjustment margin and deviation threshold of the energy storage device by monitoring key data elements such as the renewable energy volatility rate and the load forecasting error rate, so as to optimize the power dispatching and energy storage management of the microgrid. Specifically, when the renewable energy volatility rate increases, the adjustment margin of the energy storage device is expanded to tolerate a larger power deviation; when the load forecasting error rate increases, the deviation threshold is reduced to tighten the control. Through this coupling mechanism, the microgrid can operate efficiently and stably under complex operating conditions.

[0042] Embodiment 2: In Embodiment 1, however, the differences in the charging of the energy storage battery at different powers affect the service life of the energy storage device. This embodiment makes further improvements on the basis of the above embodiment.

[0043] In the above content, by monitoring key data elements such as the renewable energy volatility rate and the load forecasting error rate, the adjustment margin and deviation threshold of the energy storage device are dynamically adjusted to optimize the power dispatching and energy storage management of the microgrid. However, when the energy storage battery is charged at different powers, its performance shows differences, which will affect the service life of the energy storage device.

[0044] The method further includes S2: determining the state of charge level according to the real-time state of charge of each energy storage device, and calculating the single-cycle capacity attenuation rate; determining the optimal charge and discharge rate of each energy storage device according to the single-cycle capacity attenuation rate and the state of charge level, and generating an initial power consumption strategy in combination with the deviation threshold and the adjustment margin.

[0045] The charged level includes a core layer, a buffer layer, and a risk layer; the core layer refers to the state of charge being in the range of [65%, 100%], the buffer layer refers to the state of charge being in the range of [60%, 65%), and the risk layer refers to the state of charge being less than 60%. The interval boundaries need to be dynamically calibrated according to the battery type and the ambient temperature.

[0046] The formula for calculating the single-cycle capacity attenuation rate is as follows:

[0047] Where, is the single-cycle capacity attenuation rate, is the current capacity, is the capacity at the end of the previous cycle, is the initial rated capacity. In the case of extremely special situations, that is, when the current capacity is exactly equal to the capacity at the end of the previous cycle, it means that the single-cycle capacity attenuation rate of the current cycle cannot be detected (such as insufficient detection accuracy), and the data of the current cycle is discarded. The attenuation threshold is set in advance according to historical data to judge the change of the single-cycle capacity attenuation rate calculated by the energy storage device at different times.

[0048] Step S2 further includes: if the single-cycle capacity attenuation rate is higher than the attenuation threshold, it is marked as a risk point; the monitoring period is set, and the total number of risk points within the monitoring period is recorded; the stability value of each energy storage device within the monitoring period is obtained according to the change of the charged level; the optimal charge and discharge rate of each energy storage device is determined according to the total number of risk points and the stability value.

[0049] In some embodiments, the risk point refers to the situation where the single-cycle capacity attenuation rate is higher than the attenuation threshold. Its function is to mark the abnormal attenuation that may occur during the charge and discharge process of the energy storage device, indicating that the charge and discharge strategy needs to be adjusted to avoid further deterioration of the battery performance. By detecting the risk points in time, corresponding measures can be taken to slow down the battery capacity attenuation rate, extend the battery life, and improve the stability and reliability of the microgrid.

[0050] In some embodiments, the stable value is obtained by adjusting a preset initial value according to the change in the charge level, which is used to reflect the stability of the charge state change of the energy storage device over a period of time and provide a reference for determining the optimal charge and discharge rate. If the charge state of the energy storage device remains stable over a period of time, it indicates that the current charge and discharge strategy is relatively reasonable; conversely, if the charge state fluctuates greatly, the charge and discharge rate needs to be adjusted. The initial value needs to be evaluated based on historical data; during the analysis process, the average value of the final stable value in the historical data, as well as the magnitude of increase and decrease during the adjustment process, need to be obtained for setting. If the increase amplitude is large, the value of the initial value should be set small, and one-third of the average value of the stable value in the historical data can be used as the initial value, which specifically needs to be dynamically set according to the actual situation. A basic step size and a basic duration are preset, the change trend is judged, and the stable value is obtained according to the change in the charge level, the basic step size, and the basic duration. The stable value can more accurately evaluate the operating state of the energy storage device, thereby formulating a more reasonable charge and discharge strategy and improving the usage efficiency and lifespan of the energy storage device.

[0051] The change in the charge level includes a positive change and a negative change. For a positive change, points are added to the initial value; for a negative change, points are subtracted from the initial value. The basic step size refers to the minimum unit value of the score adjustment each time (such as 1 point); the basic duration refers to the standard duration that persists after the level change. If it does not exceed the basic duration, points are added according to the basic step size. If it exceeds the standard duration, points are added proportionally according to the corresponding relationship between the basic duration and the basic step size.

[0052] For example, if it changes from the risk layer to the core layer and maintains a basic duration (such as 30 minutes), then one basic step size (such as 1 point) is added; if it changes from the risk layer to the core layer and maintains for 45 minutes, then 1.5 points are added, that is, points are added proportionally. The charge state of the energy storage battery is 70% (core layer), and the single-cycle capacity attenuation rate is 0.0005% (lower than the threshold of 0.001%). After a certain charge and discharge cycle, the calculated single-cycle capacity attenuation rate is 0.0015% (higher than the threshold of 0.001%). A correction strategy is set: Since the attenuation rate is higher than the threshold but still in the core layer, it is decided to reduce the charge and discharge current from the original 10 A to 8 A and increase the rest time after each charge and discharge by 10 minutes. After implementing the correction strategy, the charge and discharge data and the capacity attenuation rate of the energy storage battery are monitored in real time.

[0053] After a period of monitoring, it is found that the single-cycle capacity attenuation rate drops to 0.0008% (lower than the threshold of 0.001%), indicating that the correction strategy is effective. According to the monitoring results, the charge and discharge current and the rest time are adjusted in a timely manner to ensure that the energy storage battery always maintains the optimal working state, thereby improving the service life of the energy storage battery.

[0054] In some embodiments, the specific steps for determining the optimal charge-discharge rate of each energy storage device according to the total number of risk points and the stability value are as follows: If the single-cycle capacity attenuation rate is higher than the attenuation threshold, it is marked as a risk point. Set a monitoring period, for example, 1 hour. Record the total number of risk points within the monitoring period. Obtain the stability value of each energy storage device within the monitoring period according to the change in the charge level.

[0055] Preset a risk point quantity threshold. If the total number of risk points is greater than the risk point quantity threshold, first set the charge-discharge rate according to the risk point status, and then adjust it according to the stability value. If the total number of risk points is large (i.e., greater than the risk point quantity threshold, for example, exceeding 3), it indicates that the capacity attenuation of the energy storage device is relatively serious, and the charge-discharge rate needs to be reduced to reduce the burden on the battery. At the same time, further adjustment is made according to the size of the stability value. Preset a stability value threshold. If the stability value is low (for example, less than 5 points), it indicates that the charge state fluctuates greatly, and the charge-discharge rate needs to be further reduced; if the stability value is high (for example, greater than 5 points), it indicates that the charge state is relatively stable, and the charge-discharge rate can be appropriately increased.

[0056] If the total number of risk points is small (for example, less than or equal to 3), it indicates that the capacity attenuation of the energy storage device is acceptable, and the charge-discharge rate can be determined according to the size of the stability value. If the stability value is low, the charge-discharge rate can be appropriately reduced; if the stability value is high, the charge-discharge rate can be appropriately increased.

[0057] Suppose there is an energy storage battery. Within a 1-hour monitoring period, the situation where the single-cycle capacity attenuation rate is higher than the attenuation threshold occurs 2 times, that is, the total number of risk points is 2.

[0058] Within this monitoring period, the charge state of the energy storage battery changes from the risk layer (55%) to the core layer (70%) and maintains for 40 minutes. According to the calculation rule of the stability value, the initial value is set to 5 points, and 1.5 points are added for positive change, so the stability value is 6.5 points.

[0059] Since the total number of risk points is small (2), and the stability value is high (6.5 points), the charge-discharge rate can be appropriately increased. Suppose the original charge-discharge rate is 10A, and according to the actual situation, the charge-discharge rate can be increased to 12A.

[0060] The specific steps for generating the initial power consumption strategy according to the optimal charge-discharge rate, deviation threshold, and adjustment margin are as follows: Determine the optimal charge-discharge rate of the energy storage device, obtain the load prediction error rate and the renewable energy volatility. During the charge-discharge process, the charge-discharge rate of the energy storage device should be controlled near the optimal charge-discharge rate, and at the same time, the requirements of the deviation threshold and the adjustment margin should be met.

[0061] When the power generation of renewable energy changes rapidly, the energy storage device can adjust its charge and discharge within the allowed range according to the regulation margin to smooth the instability of renewable energy generation. For example, when the volatility exceeds the high volatility threshold, the regulation margin is increased by 20%, that is, the new regulation margin is ±6kW, and the energy storage device can be adjusted within the range of ±6kW of the rated power.

[0062] During the charge and discharge process, the power deviation of the energy storage device should be monitored in real time to ensure that it does not exceed the deviation threshold. If the power deviation approaches or exceeds the deviation threshold, the charge and discharge rate should be adjusted in a timely manner to reduce the risk of scheduling failure.

[0063] Suppose the optimal charge and discharge rate of the energy storage device is 12A, and the rated power is 100kW (assuming that the current is proportional to the power, and the power corresponding to 12A is 12kW. For the convenience of calculation, it is assumed here that the power is linearly related to the current).

[0064] The current volatility of renewable energy is 18%, exceeding the high volatility threshold of 15%. According to the setting rule of the regulation margin, the regulation margin is increased by 50%, and the new regulation margin is ±7.5kW.

[0065] The load forecasting error rate is 8%, exceeding the preset threshold of 5%. According to the setting rule of the deviation threshold, the deviation threshold is reduced to ±2.5%.

[0066] The initial power consumption strategy is: the charge and discharge power of the energy storage device is controlled near 12kW, and at the same time, the following conditions should be met: When the power generation of renewable energy fluctuates, the energy storage device can be adjusted within the range of 100kW±6kW (i.e., 94kW - 106kW) to smooth the fluctuations of new energy generation.

[0067] During the charge and discharge process, the power deviation of the energy storage device is monitored in real time to ensure that it does not exceed ±2.5kW. If the power deviation approaches or exceeds ±2.5kW, the charge and discharge rate should be adjusted in a timely manner, for example, the charge and discharge power is adjusted from 12kW to 11kW or 13kW, to reduce the risk of scheduling failure.

[0068] In this embodiment, during the long-term use of the energy storage battery, due to the irreversibility of chemical reactions, phenomena such as oxidation, corrosion, and thermal runaway will occur inside the battery, resulting in a gradual decrease in the battery capacity. Calculating the single-cycle capacity decay rate can track the capacity decay rate of each energy storage battery in real time, provide a basis for subsequent correction strategies, thereby slowing down the battery capacity decay rate and extending the battery service life.

[0069] In this embodiment, by determining the charge level according to the real-time state of charge of each energy storage device, calculating the single-cycle capacity attenuation rate, determining the optimal charge and discharge rate of each energy storage device based on the single-cycle capacity attenuation rate and the charge level, and generating an initial power consumption strategy in combination with the deviation threshold and the adjustment margin, the power dispatching and energy storage management of the microgrid are further optimized. In a high-volatility scenario, by expanding the adjustment margin, the fluctuations of new energy power generation are effectively smoothed, and the impact on the power grid is reduced; by dynamically matching the deviation tolerance, the wind and light abandonment rate is reduced, and the utilization rate of renewable energy is improved. By reducing the deviation threshold, the risk of dispatching failure is reduced, and the stability and reliability of the power grid are improved; by optimizing the charge and discharge strategies of energy storage devices, the number of unnecessary operations is reduced, the service life of energy storage devices is extended, and the operation cost is reduced. At the same time, by calculating the single-cycle capacity attenuation rate and determining the optimal charge and discharge rate, the battery capacity attenuation speed can be slowed down, the battery service life can be extended, and the overall performance of the microgrid can be improved.

[0070] Embodiment 3: On the basis of the above content, this embodiment groups the energy storage devices to solve the problems of insufficient autonomous power supply capacity under communication interruption, the inability of the energy storage system to quickly resume power supply to critical loads, too long or failed black start time, large differences in the SOC of energy storage devices, and local overload caused by uneven power distribution, resulting in overcharge or over-discharge.

[0071] In the prior art, traditional energy storage systems rely on the central controller to issue voltage / frequency references. When the communication is interrupted, they cannot generate reference values autonomously, resulting in system collapse. The central controller cannot perceive the local state (such as SOC, power output) in real time, resulting in control delay and misjudgment. The topological structure is fixed and lacks dynamic adjustment. The energy storage devices always operate in parallel, and the circulating current path cannot be cut off, resulting in energy loss and shortened equipment life. There is a lack of real-time monitoring and fast response mechanism for circulating current, and the topology cannot be dynamically reconfigured when the circulating current exceeds the limit.

[0072] The device reliability and function positioning are not distinguished, and all energy storage devices are treated equally. They are not grouped according to indicators such as equipment failure rate and response delay, resulting in the high-reliability group being dragged down by the low-reliability group. The black start ability refers to the ability of the energy storage system to autonomously resume power supply to critical loads through local droop control and topology reconfiguration in the case of communication interruption or complete power outage of the power grid. When black starting, the high-reliability group is not started preferentially, resulting in slow response speed and being vulnerable to faults. The traditional droop control uses a fixed droop coefficient and cannot be dynamically adjusted according to SOC and power demand, resulting in poor SOC balance and uneven power distribution. The characteristic differences of different groups are not considered, resulting in poor control effect.

[0073] The method further includes: S3: Grouping the energy storage devices according to the analysis of the initial power consumption strategy results, including high-reliability group, medium-reliability group and low-reliability group; initializing the droop coefficient and the topology reconfiguration strategy.

[0074] In some embodiments, based on the risk points (such as equipment failure rate, response delay) and stability values (such as SOC fluctuation range, charge-discharge efficiency) in steps S1 and S2, and the analysis of the initial power consumption strategy results, the actual application results of different energy storage devices are determined, and the energy storage devices are grouped into a high-reliance group, a medium-reliance group, and a low-reliance group. Specifically, grouping indicators are set, including but not limited to: equipment failure rate, response delay, SOC fluctuation range, charge-discharge efficiency, average number of risk points, and average stability value within multiple monitoring cycles. Division is carried out according to the grouping indicators. For example, for the high-reliance group, the equipment failure rate ≤ 5%, the response delay ≤ 50 ms, the SOC fluctuation range is within [-5%, 5%], the charge-discharge efficiency ≽ 95%, the average number of risk points within multiple monitoring cycles is less than 3, and the average stability value is greater than 7 points.

[0075] In some embodiments, the droop coefficient is the core parameter of droop control, which directly affects the voltage / frequency stability and power distribution of the system. Initializing the droop coefficient requires comprehensive consideration of factors such as system capacity, SOC range, and communication ability. The voltage droop coefficient represents the change in output voltage (unit: V / %) when the SOC changes by 1%. The frequency droop coefficient represents the change in output frequency (unit: Hz / kW) when the power output changes by 1 kW. Droop control is a distributed control strategy that dynamically adjusts the output voltage / frequency through local measurements (such as SOC, power output) to enable the system to spontaneously form a reference in a communication-free mode.

[0076] Initializing the droop coefficient includes: determining system parameters: rated voltage, rated frequency, SOC range, and total energy storage device capacity; setting the droop coefficient range: the voltage droop coefficient usually has a value range of [0.1, 1] (unit: V / %). For example, the high-reliance group requires fast response and takes a larger value (such as 0.5 V / %); the low-reliance group requires slow response and takes a smaller value (such as 0.2 V / %); the frequency droop coefficient usually has a value range of [0.005, 0.02]. For example, the high-reliance group takes a larger value (such as 0.01 Hz / kW) to quickly balance power. Grouping is set with corresponding droop coefficients.

[0077] In some embodiments, topology reconfiguration refers to dynamically adjusting the connection mode of energy storage devices according to the system state (such as SOC, power demand, communication state) to optimize system performance. The goal of topology reconfiguration is to ensure that the high-reliance group is preferentially connected to form a stable voltage / frequency reference in case of communication interruption. After communication is restored, power distribution is optimized to avoid overload or underload.

[0078] The initialization topology reconstruction strategy includes: all energy storage devices are connected to the bus through local switches, but only the high-reliability group is activated when communication is interrupted. Topology reconstruction during communication interruption: Step 1: Real-time monitor the communication status and detect communication interruption (such as not receiving instructions from the central controller for more than 1 second). Step 2: Disconnect the medium-reliability group and the low-reliability group from the bus, and only keep the high-reliability group powered. Step 3: The high-reliability group forms voltage reference and frequency reference through local droop control.

[0079] Gradually connect the medium-reliability group and the low-reliability group: Step 1: The medium-reliability group and the low-reliability group measure local SOC and voltage / frequency. Step 2: Adjust the output voltage / frequency according to the droop coefficient and gradually approach the reference value of the high-reliability group. Step 3: When the output voltage / frequency of the medium-reliability group and the low-reliability group are close to that of the high-reliability group (such as the error < 1V and 0.01Hz), close the switch to connect to the system.

[0080] Topology reconstruction after communication recovery: Step 1: Detect communication recovery (such as receiving instructions from the central controller). Step 2: The central controller reallocates the droop coefficient according to the global SOC and power demand. Step 3: Optimize power distribution to avoid overloading of the high-reliability group.

[0081] Step S3 also includes: when communication interruption is detected, use the high-reliability group for local preliminary droop control to form voltage reference and frequency reference; real-time monitor the local measurement results of the medium-reliability group and the low-reliability group, and calculate the differences from the voltage reference and frequency reference respectively. If both differences are less than the error range, then connect to the power supply system and achieve power balance through comprehensive droop control.

[0082] In the power system, the voltage reference is the voltage stability value that the microgrid system expects to maintain, usually the rated voltage (such as 400V). It is the basis for the stable operation of the system, and the output voltage of all energy storage devices should fluctuate around this reference value. The frequency reference is the frequency stability value that the microgrid system expects to maintain, usually the rated frequency (such as 50Hz or 60Hz). It is the frequency reference for the output electrical energy of synchronous generators or energy storage systems, and the output frequency of all devices should fluctuate around this reference value.

[0083] In the traditional centralized control mode, the voltage / frequency reference is generated by the central controller and sent to each energy storage device. When communication is interrupted, the central controller cannot send the reference value, and the energy storage devices will lose the unified voltage / frequency reference, resulting in system instability (such as voltage collapse or frequency drift). In this embodiment, by grouping according to the pre-set grouping scheme and selecting the corresponding group to generate the voltage / frequency reference, the accuracy is further improved.

[0084] The high-reliability group has the most stable SOC and lower power output. Its output voltage / frequency is closer to the rated value. At the same time, with a high droop coefficient, it can quickly adjust the output voltage / frequency to suppress system fluctuations. The rest of the energy storage devices follow the voltage / frequency of the high-reliability group through droop control to form a unified reference benchmark.

[0085] The droop control includes voltage droop and frequency droop. The voltage droop means that the output voltage of the energy storage device is adjusted according to the state of charge (SOC). The higher the state of charge (SOC), the higher the output voltage. When the SOC is high, increasing the output voltage can reduce the charging current, thus avoiding overcharging of the battery. Through voltage droop, the device with a high SOC has a high output voltage and discharges first; the device with a low SOC has a low output voltage and charges first, achieving SOC balance. The higher the SOC, the higher the output voltage and the smaller the charging current, avoiding overcharging.

[0086] The frequency droop means that the output frequency of the energy storage device is adjusted according to the power output. The greater the power output, the lower the frequency. When the power output is large, reducing the frequency can reduce the power output of other devices (according to the power-frequency characteristic), thus avoiding system overload. Through frequency droop, the device with a large power output has a low frequency, and the device with a small power output has a high frequency, achieving power balance. The greater the power output, the lower the frequency, reducing the power output of other devices and avoiding overload.

[0087] The local measurement results of the medium-reliability group and the low-reliability group are monitored in real time, and the differences from the voltage reference and the frequency reference are calculated respectively. If both differences are less than the error range, they will be connected to the power supply system, and power balance is achieved through comprehensive droop control. For example, the initial conditions before connecting to the power supply system: Status of Group H (high-reliability group): H1: SOC = 75%, output voltage 395V, output frequency 49.95Hz; H2: SOC = 80%, output voltage 392.5V, output frequency 49.92Hz; Status of Group M (medium-reliability group): M1: SOC = 70%, initially not connected to the system; Status of Group L (low-reliability group): L1: SOC = 50%, initially not connected to the system; Droop coefficients: voltage droop coefficient 0.5V / %; frequency droop coefficient 0.01Hz / kW; M1 needs to be gradually connected to the system. Its target voltage / frequency should be close to the reference value of Group H. Assume that the initial connection voltage of M1 is the average voltage of Group H, i.e., 393.75V; the initial connection frequency of M1 is the average frequency of Group H, i.e., 49.935Hz; M1 adjusts its output voltage / frequency in small steps (such as 1V and 0.01Hz per second) to gradually approach the target value. When the output voltage / frequency of M1 is close to that of Group H (e.g., within the error range when the error < 1V and 0.01Hz), it is officially connected to the system; The system state after M1 is connected includes H1, H2, and M1. M1 adjusts its frequency according to the local power output. As the load increases, the power output of M1 increases and the frequency gradually decreases; L1 is gradually connected to the system. The target voltage / frequency of L1 should be lower than that of Group H and Group M (because its SOC is lower and the output voltage should be lower). Calculate the target voltage corresponding to L1 according to the voltage droop coefficient of 0.5V / %. The calculation formula for the target voltage is as follows:

[0088] where, is the target voltage of L1, is the rated voltage, which is 400V here; is the voltage droop coefficient; is the maximum capacitance, which is 100%; is the capacitance of L1; the calculated target voltage is 375V.

[0089] Calculate the target frequency corresponding to L1 according to the frequency droop coefficient of 0.01Hz / kW. The calculation formula for the target frequency is as follows:

[0090] where, is the target frequency of L1; is the rated frequency, which is set to 50Hz here; is the frequency droop coefficient; is the average output frequency in Group H; the target frequency is 49.501Hz and is adjusted according to the power output later. L1 adjusts its output voltage / frequency in small steps (such as 1V and 0.01Hz per second) to gradually approach the target value. When the output voltage / frequency of L1 is close to the target value (e.g., within the error range when the error < 1V and 0.01Hz), it is officially connected to the system; The frequencies of all devices fluctuate around 49.935 Hz (adjusted according to power output). The high-SOC devices (Group H) discharge first, and the low-SOC devices (Group L) charge first or have a low power output. Through the above steps, Group M and Group L can be safely and stably connected to the system, and black start and autonomous power supply can be achieved during communication interruption. In specific actual detections, the droop coefficient and topology are dynamically adjusted, and the monitoring and control of energy storage devices are continuously optimized to generate an optimal regulation scheme.

[0091] S4: Real-time monitor the state of charge and circulating current. When the detected circulating current exceeds the pre-set circulating current threshold, use the middle-close group to switch several devices to a series structure through a solid-state circuit breaker to reduce the circulating current path.

[0092] In some embodiments, a solid-state circuit breaker (SSCB) is used to suppress the circulating current by quickly switching the series-parallel structure of energy storage devices. Off-grid mode: Switch the energy storage devices to a series structure to increase the output voltage and reduce the circulating current path. Grid-connected mode: Switch to a parallel structure to improve the power output capacity. Circulating current suppression refers to dynamically adjusting the topology by real-time monitoring the circulating current. For example, when the circulating current exceeds the threshold, some devices are switched to series.

[0093] The middle-close group is preferentially used to deploy the SSCB to achieve topological dynamic reconstruction and suppress the circulating current loss in the off-grid state. The high-close group and the low-close group are used to selectively deploy the SSCB according to actual needs to balance cost and performance.

[0094] In some embodiments, the method further includes: dynamically adjusting the droop coefficient to balance the output power of each device and avoid local overload. Regularly evaluate the effect of circulating current suppression and optimize the reconstruction strategy.

[0095] In this implementation, each energy storage device is equipped with a voltage sensor, a current sensor, and an SOC sensor, which are used to real-time monitor the output voltage (for voltage droop control), the output current (for frequency droop control), and the remaining power (i.e., the state of charge, for voltage droop and SOC balancing), respectively.

[0096] The high-close group is preferentially used for black start and power supply to critical loads, equipped with a high-precision local measurement module and a fast-response solid-state circuit breaker, forming the core of autonomous power supply in the non-communication mode, and maintaining voltage / frequency stability through local droop control; The middle-close group is used for power balance and circulating current suppression in the off-grid state, has the ability of topological reconstruction, and realizes circulating current suppression through topological reconstruction to improve the energy efficiency in the off-grid state; The low-close group is used for long-term energy storage and low-frequency fluctuation regulation, equipped with a low-cost local control module to reduce the system cost, and at the same time reduce the risk of fault spread through group isolation.

[0097] In some embodiments, parameters such as SOC, voltage, and current are monitored in real time by local sensors, and the grouping configuration is dynamically adjusted. For example, when the SOC of a certain device is lower than the threshold, it is downgraded from the high-reliance group to the medium-reliance group.

[0098] For the autonomous power supply ability under communication interruption, the technical solution of this embodiment has the following effects: the power supply recovery time for critical loads is shortened to within 10 seconds, and the voltage / frequency fluctuation range is controlled within ±5%. For the circulating current suppression effect: the circulating current loss in the off-grid state is reduced by more than 50%, and the system efficiency is increased by 10%-15%. For the black start success rate: in extreme working conditions, the black start success rate is increased to more than 90%. For system stability: the risk of fault spread is reduced by more than 40%, and the SOC balance is increased by 20%.

[0099] In this embodiment, through the collaborative design of dynamic grouping, local droop control, and topological dynamic reconfiguration, the deficiencies of the existing energy storage system in terms of communication interruption, circulating current loss, and black start ability are solved. Through local measurement and droop control, the voltage / frequency self-stabilization of the energy storage device in the non-communication mode is realized to maintain the power supply for critical loads; through the topological dynamic reconfiguration technology, the circulating current loss in the off-grid state is suppressed, and the energy utilization efficiency of the energy storage system is improved; the grouping strategy not only optimizes the system resource allocation, but also realizes autonomous power supply and circulating current suppression in the non-communication mode through the differential design of the high-reliance group, medium-reliance group, and low-reliance group, significantly improving the reliability and efficiency of the energy storage system in extreme working conditions.

[0100] Embodiment 4: This embodiment makes further improvements on the basis of the above content.

[0101] The method further includes: S5: Based on the physical parameters and topological structure of the energy storage device, integrating the historical fault database and real-time status monitoring data, establish a digital twin model; when a fault risk is detected, obtain the grouping status and the state of charge distribution data, re-determine the grouping scheme based on the digital twin model, simulate to obtain the performance indicators under different grouping strategies, and generate an evaluation result; generate a topological reconfiguration path according to the evaluation result, and predict potential fault points and the probability of fault occurrence.

[0102] In some embodiments, the evaluation result includes the state of charge (SOC) balance, power support ability, and circulating current suppression effect of each grouping scheme; the performance indicators include power distribution, circulating current path, voltage fluctuation, and frequency fluctuation.

[0103] In some embodiments, the historical fault database includes all fault conditions that occurred over a past period of time, as well as the corresponding energy storage devices, time nodes when the faults occurred, and treatment solutions, etc. The digital twin realizes real-time status monitoring, fault prediction, and optimization decision-making by constructing a virtual mirror of the physical system. The core functions of the digital twin model are determined for the topology reconstruction optimization, fault prediction, and SOC balance analysis of the energy storage system, so as to improve the service life of the energy storage device; for the energy storage system, a closed-loop process of "real-time status synchronization - fault risk rehearsal - reconstruction strategy generation" needs to be realized. The efficient management and fault rehearsal of the energy storage system are realized, significantly improving the system reliability and operation and maintenance efficiency.

[0104] In some embodiments, when the system detects load fluctuations or fault risks, the digital twin model generates multiple grouping schemes (such as adjusting the boundary between the H group and the M group, adding L group isolation devices) according to the current SOC distribution and health status. Simulate the power distribution, circulating current path, and voltage / frequency fluctuations under different grouping strategies, and evaluate the SOC balance, power support ability, and circulating current suppression effect of each scheme. For potential circulating current paths or overload risks found during the rehearsal, the digital twin model generates a topology reconstruction path (such as the switch switching sequence, parallel device recombination scheme). Verify the feasibility of the reconstruction path through simulation, and select the path with the minimum voltage fluctuation and the best circulating current suppression effect.

[0105] Combining historical fault data with the real-time status, the digital twin model predicts potential fault points (such as devices with SOH lower than the threshold, switches on frequently circulating current paths). Before topology reconstruction, the digital twin model simulates fault isolation schemes (such as cutting off the parallel branch of the faulty device, adjusting the grouping boundary), and evaluates the stability of the system after isolation. If the predicted fault risk exceeds the threshold, the isolation operation is preferentially executed to prevent the fault from spreading to other groups.

[0106] In this embodiment, the dynamic grouping strategy of the energy storage device is deeply integrated with the digital twin technology. By constructing a digital twin model for the entire life cycle of the energy storage system, the joint simulation verification of the grouping strategy and topology reconstruction is realized. The digital twin model synchronizes the grouping status, SOC distribution, and historical fault data of the physical system in real time, rehearses the voltage / frequency stability, circulating current suppression effect, and fault spread risk under different grouping strategies before topology reconstruction, and dynamically optimizes the grouping scheme and reconstruction path to improve the adaptive ability of the system under complex working conditions.

[0107] In this embodiment, the digital twin rehearsal is used to avoid circulation or overload problems caused by blind grouping, and to improve the rationality of the grouping strategy. For example, when the load suddenly increases, the digital twin model can quickly verify the feasibility of temporarily merging some of the M group devices into the H group, while predicting the circulation risk and optimizing the switch switching sequence. Combining historical fault data with real-time SOH, high-risk devices are isolated in advance to prevent the fault from spreading to the entire group. For example, when the SOH of a certain L group device is lower than 50%, the digital twin model can simulate cutting it off from the parallel branch and redistributing the power of the remaining devices. Dynamically adjust the grouping threshold and reconstruction strategy to extend the system life. For example, in response to the capacity attenuation of lithium batteries, the model can gradually lower the SOC lower limit threshold of the H group to avoid power support failure due to insufficient capacity.

[0108] This embodiment uses digital twin technology to deeply couple dynamic grouping with topology reconstruction, achieving an upgrade from passive response to active rehearsal. While improving system robustness, it reduces operation and maintenance costs and equipment losses, providing key technical support for the intelligent management of large-scale distributed energy storage systems.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A monitoring and control method for energy storage devices in a microgrid, characterized in that, Including: S1: Obtain the renewable energy volatility and load forecasting error rate based on the basic dataset and the load forecasting model, and set the deviation threshold and regulation margin based on the mapping relationship; S2: Determine the charge level according to the real-time state of charge of each energy storage device, and calculate the single-cycle capacity attenuation rate; Determine the optimal charge and discharge rate of each energy storage device according to the single-cycle capacity attenuation rate and the charge level, and generate an initial power consumption strategy in combination with the deviation threshold and the regulation margin; S3: Group the energy storage devices according to the analysis of the initial power consumption strategy results, including the high-reliance group, the medium-reliance group, and the low-reliance group; When a communication interruption is detected, use the high-reliance group to perform local primary droop control to form a voltage reference and a frequency reference; Real-time monitor the local measurement results of the medium-reliance group and the low-reliance group, and calculate the differences from the voltage reference and the frequency reference respectively. If the differences are both less than the error range, connect to the power supply system and achieve power balance through comprehensive droop control.

2. The monitoring and control method of an energy storage device in a microgrid according to claim 1, characterized in that, The method further includes: S4: Real-time monitor the state of charge and the circulating current. When it is detected that the circulating current exceeds the pre-set circulating current threshold, use the medium-reliance group to switch several devices to a series structure through a solid-state circuit breaker to reduce the circulating current path; S5: Based on the physical parameters and topological structure of the energy storage device, integrate the historical fault database and the real-time status monitoring data to establish a digital twin model; When a fault risk is detected, obtain the grouping status and the state of charge distribution data, re-determine the grouping scheme based on the digital twin model, simulate the performance indicators under different grouping strategies, and generate an evaluation result; Generate a topological reconstruction path according to the evaluation result, and predict potential fault points and the probability of fault occurrence.

3. The monitoring and control method of an energy storage device in a microgrid according to claim 1, characterized in that, The basic dataset includes energy storage basic data, power data, and environmental data; The mapping relationship is based on the pre-set fluctuation threshold range, error threshold range, deviation reference value, and regulation reference value. Adjust the regulation reference value according to the proportional relationship between the renewable energy volatility and the fluctuation threshold range to determine the regulation margin; Adjust the deviation reference value according to the proportional relationship between the load forecasting error rate and the error threshold range to determine the deviation threshold; The regulation margin refers to the dynamic adjustment ability range reserved for the fluctuation of the renewable energy power generation under the charge and discharge strategy of the energy storage device; The deviation threshold refers to the upper limit of the allowable instantaneous power deviation.

4. The monitoring and control method of an energy storage device in a microgrid according to claim 1, wherein Step S2 further includes: If the single-cycle capacity attenuation rate is higher than the attenuation threshold, mark it as a risk point; Set the monitoring period and record the total number of risk points within the monitoring period; Obtain the stable value of each energy storage device within the monitoring period according to the change of the charge level; Determine the optimal charge and discharge rate of each energy storage device according to the total number of risk points and the stable value.

5. The monitoring and control method of an energy storage device in a microgrid according to claim 1, characterized in that, The charge level includes the core layer, the buffer layer, and the risk layer; The core layer refers to the state of charge in the range of [65%, 100%], the buffer layer refers to the state of charge in the range of [60%, 65%), and the risk layer refers to the state of charge less than 60%.

6. The monitoring and control method of an energy storage device in a microgrid according to claim 4, characterized in that, The stable value is obtained by adjusting the pre-set initial value according to the change of the charge level, and is used to reflect the stability of the state of charge change of the energy storage device within a period of time; The change in the charged level includes a positive change and a negative change. The positive change adds points to the initial value; the negative change subtracts points from the initial value. A basic step size and a basic duration are preset, and a stable value is obtained based on the change in the charged level, the basic step size, and the basic duration. The basic step size refers to the minimum unit value of the score adjustment each time. The basic duration refers to the standard duration that persists after the level change. If it does not exceed the basic duration, points are added according to the basic step size. If it exceeds the standard duration, points are added proportionally according to the corresponding relationship between the basic duration and the basic step size.

7. The monitoring and control method of an energy storage device in a microgrid according to claim 4, characterized in that, Determine the optimal charge-discharge rate of each energy storage device according to the total number of risk points and the stable value, including: preset a risk point quantity threshold and a stable value threshold, and compare them respectively. If the total number of risk points is greater than the risk point quantity threshold, first set the charge-discharge rate according to the risk point state, and then adjust it according to the stable value.

8. The monitoring and control method of an energy storage device in a microgrid according to claim 1, characterized in that, The voltage reference is the voltage stable value that the microgrid expects to maintain, and the frequency reference is the frequency stable value that the microgrid expects to maintain. The droop control includes voltage droop and frequency droop. The voltage droop means that the output voltage of the energy storage device is adjusted according to the change in the state of charge. The higher the state of charge, the higher the output voltage. The frequency droop means that the output frequency of the energy storage device is adjusted according to the change in the power output. The greater the power output, the lower the frequency.

9. The monitoring and control method of an energy storage device in a microgrid according to claim 1, characterized in that Step S3 also includes: initializing the droop coefficient and the topology reconfiguration strategy. The initialization of the droop coefficient includes: determining the parameters of the microgrid system, setting the range of the droop coefficient, and grouping and setting the corresponding droop coefficients. The initialization of the topology reconfiguration strategy includes: real-time monitoring of the communication status. When the communication is interrupted, disconnect the connections of the medium-reliance group and the low-reliance group to the bus, and only retain the power supply of the high-reliance group. Based on the high-reliance group, form the voltage reference and the frequency reference through local droop control.

10. The monitoring and control method of an energy storage device in a microgrid according to claim 2, characterized in that, The evaluation results include the state-of-charge balance, power support ability, and circulating current suppression effect of each grouping scheme. The performance indicators include power distribution, circulating current path, voltage fluctuation, and frequency fluctuation.

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