A monitoring and control method for energy storage device in microgrid

Abstract

The present application discloses a monitoring and control method for energy storage devices in a microgrid, which relates to the field of microgrid technology, including: grouping the energy storage devices according to the results of the initial power consumption strategy, including a high-reliability group, a medium-reliability group and a low-reliability group; when a communication interruption is detected, using the high-reliability group to perform local preliminary droop control to form a voltage reference and a frequency reference; real-time monitoring of the local measurement results of the medium-reliability group and the low-reliability group, and calculating the difference respectively. If the difference is less than the error range, the power supply system will be connected to achieve power balance through comprehensive droop control; through the collaborative design of dynamic grouping, local droop control and dynamic topology reconstruction, the deficiencies of the existing energy storage system in terms of communication interruption, circulation loss and black start capability are solved, and through the differentiated design of the high-reliability group, the medium-reliability group and the low-reliability group, autonomous power supply and circulation suppression in the non-communication mode are achieved, significantly improving the reliability and efficiency of the energy storage system under extreme working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and in particular to a monitoring and control method for an energy storage device in a microgrid. Background Art

[0002] With the continuous evolution of distributed energy technologies, microgrids, as a key energy supply model, continue to expand their application in power systems. However, the decentralized layout of microgrids within monitoring areas (such as residential communities or industrial parks) poses challenges to centralized coordinated control. Furthermore, voltage fluctuations and power imbalances frequently arise during actual operation.

[0003] In traditional energy storage systems, centralized control relies on communication networks to maintain voltage and frequency stability. However, when communication is interrupted, the inability to generate a unified reference value can easily lead to system instability, resulting in weak black start capabilities and long recovery times. Furthermore, parallel operation of energy storage devices in a fixed topology is prone to circulating currents, causing energy loss and equipment overheating. Ungrouped, unified control strategies struggle to account for the varying reliability of different devices, leading to poor SOC balance, localized overloads, and a high risk of fault propagation. 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 a reduced service life, by providing a monitoring and control method for energy storage devices in microgrids. It achieves the technical effect of multi-optimization interval hierarchical control to enhance the dynamic adaptability of energy storage devices, thereby increasing the service life of energy storage devices.

[0005] The present application provides a monitoring and control method for an energy storage device in a microgrid, comprising:

[0006] S1: Obtain renewable energy fluctuation rate and load forecast error rate based on the basic data set and load forecast model, and set the deviation threshold and regulation margin based on the mapping relationship;

[0007] S2: Determine the charge level based on the real-time state of charge of each energy storage device and calculate the single-cycle capacity decay rate. Determine the optimal charge and discharge rate for each energy storage device based on the single-cycle capacity decay rate and charge level, and generate an initial power utilization strategy based on the deviation threshold and adjustment margin.

[0008] S3: The energy storage devices are grouped into high-reliability, medium-reliability, and low-reliability groups based on the analysis of the initial power consumption strategy results. When a communication interruption is detected, the high-reliability group is used for preliminary local droop control to form a voltage reference and frequency reference. The local measurement results of the medium-reliability and low-reliability groups are monitored in real time, and the differences between them and the voltage reference and frequency reference are calculated respectively. If the differences are all within the error range, they will be connected to the power supply system to achieve power balance through comprehensive droop control.

[0009] Furthermore, the method further comprises:

[0010] S4: Real-time monitoring of the state of charge and circulating current. When it is detected that the circulating current exceeds the preset circulating current threshold, the middle lean group switches several devices into a series structure through a solid-state circuit breaker to reduce the circulating current path;

[0011] S5: Based on the physical parameters and topological structure of the energy storage device, the historical fault database and real-time status monitoring data are integrated to establish a digital twin model. If a fault risk is detected, the grouping status and state of charge distribution data are obtained, and the grouping scheme is re-determined based on the digital twin model. The performance indicators under different grouping strategies are simulated and evaluation results are generated. Based on the evaluation results, a topology reconstruction path is generated, and potential fault points and the probability of fault occurrence are predicted.

[0012] Furthermore, step S2 further includes:

[0013] If the capacity decay rate of a single cycle is higher than the decay threshold, it will be marked as a risk point; a monitoring period is set, and the total number of risk points within the monitoring period is recorded; the stable value of each energy storage device within the monitoring period is obtained based on the change in the charge level; and the optimal charge and discharge rate of each energy storage device is determined based on the total number of risk points and the stable value.

[0014] Furthermore, step S3 further includes: initializing the droop coefficient and the topology reconstruction strategy;

[0015] Initializing the droop coefficient includes: determining microgrid system parameters, setting a droop coefficient range, and setting corresponding droop coefficients in groups;

[0016] The initialization topology reconstruction strategy includes: real-time monitoring of communication status, disconnecting the middle and low-reliability groups from the busbar when communication is interrupted, retaining only the high-reliability group for power supply, and forming a voltage reference and a frequency reference based on the high-reliability group through local droop control.

[0017] Furthermore, the evaluation results include the charge state balance, power support capability and circulation suppression effect of each grouping scheme; the performance indicators include power distribution, circulation path, voltage fluctuation and frequency fluctuation.

[0018] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0019] Through the collaborative design of dynamic grouping, local droop control, and dynamic topology reconstruction, the shortcomings of existing energy storage systems in terms of communication interruption, circulating current loss, and black start capability are resolved. Through local measurement and droop control, the voltage and frequency of the energy storage device are self-stabilized in non-communication mode, maintaining power supply to critical loads. Through dynamic topology reconstruction technology, circulating current loss in the off-grid state is suppressed, improving the energy storage system's energy utilization efficiency. The grouping strategy not only optimizes system resource allocation, but also achieves autonomous power supply and circulating current suppression in non-communication mode through the differentiated design of high-reliability groups, medium-reliability groups, and low-reliability groups, significantly improving the reliability and efficiency of the energy storage system under extreme operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 2 is a flow chart of a monitoring and control method for an energy storage device in a microgrid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To facilitate understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains; the terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only 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 associated listed items.

[0023] Example 1: Figure 1 As shown, a monitoring and control method for an energy storage device in a microgrid, the method comprising:

[0024] S1: Obtain the basic data set and load forecasting model to obtain the renewable energy volatility and load forecast error rate, and set the deviation threshold and adjustment margin based on the mapping relationship.

[0025] The basic data set includes basic energy storage data, power data, and environmental data; the basic energy storage 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 state of charge 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 referred to in this application is an energy storage battery or energy storage battery pack, and a microgrid may include several energy storage devices.

[0026] The power data includes load data, renewable energy power generation data and electricity price signals; the load data refers to the real-time monitored power load, including peak load, average load and load fluctuation rate; the renewable energy power generation data refers to the power generation of renewable energy such as solar energy and wind energy and its fluctuation data; the electricity price signal refers to the electricity price and electricity price changes in different time periods, which is used to optimize the charging and discharging strategy of the energy storage device.

[0027] The environmental data includes temperature and humidity. Temperature refers to the temperature of the environment where the energy storage device is located. Different temperatures have different degrees of impact on the performance and life of the battery. Humidity is a targeted analysis of certain energy storage devices. If there is no energy storage device that is highly sensitive to humidity, it is only necessary to monitor the normal humidity range.

[0028] The regulation margin refers to the dynamic adjustment capacity range reserved by the energy storage device under the charging and discharging strategy to cope with fluctuations in renewable energy power generation, allowing the energy storage device to absorb or release additional electricity in a short period of time to smooth out the instability of renewable energy power generation.

[0029] When renewable energy generation changes rapidly (such as a sudden decrease in solar energy due to cloud cover), energy storage devices can compensate within the regulation margin by adjusting their charge and discharge rates, thereby maintaining a stable output of the grid.

[0030] The regulation margin provides the system with a larger operating space, enabling the microgrid to respond more flexibly to changes in renewable energy generation and reduce its dependence on the traditional power grid.

[0031] The regulation margin is positively correlated with renewable energy volatility. Higher renewable energy volatility means greater uncertainty in power generation, necessitating a larger regulation margin to account for this uncertainty. By monitoring renewable energy volatility and dynamically adjusting the regulation margin, the energy storage device has sufficient capacity to maintain grid stability when power generation fluctuates.

[0032] Specifically, obtaining the renewable energy volatility based on the basic data set involves extracting renewable energy generation data from the basic data set, calculating the renewable energy volatility (e.g., the minute-by-minute change rate of solar / wind power generation), obtaining the renewable energy power value at one moment, and then obtaining the power value at the next moment, with the percentage of the ratio between the two being used as the renewable energy volatility. In practical applications, the average power value over a certain period of time can also be selected as a baseline value, and the baseline value can be compared with subsequent moments to obtain the renewable energy volatility at subsequent moments. When the volatility falls below a preset fluctuation threshold (e.g., 10%), the regulation margin is reduced (e.g., ±5%). When the volatility increases, the regulation margin is dynamically expanded based on the coupling relationship between the volatility and the margin (e.g., a 5% increase in volatility results in a 20% increase in the margin) to accommodate larger power deviations.

[0033] In high-volatility scenarios, the expanded regulation margin allows energy storage devices to adjust their charge and discharge over a wider range to smooth fluctuations in renewable energy generation. Real-time monitoring of the energy storage device's state of charge (SOC) and charge and discharge rate ensures that the device's limits are not exceeded during the adjustment process.

[0034] The deviation threshold and adjustment margin are set based on a mapping relationship. The mapping relationship is based on a pre-set fluctuation threshold range, error threshold range, deviation reference value and adjustment reference value. The adjustment reference value is adjusted accordingly according to the proportional relationship between the renewable energy fluctuation rate and the fluctuation threshold range to determine the adjustment margin; the deviation reference value is adjusted accordingly according to the proportional relationship between the load forecast error rate and the error threshold range to determine the deviation threshold.

[0035] Specifically, if the renewable energy volatility is within the fluctuation threshold, the adjustment base value is set as the adjustment margin. If the renewable energy volatility is not within the fluctuation threshold, the floating direction is determined. If the floating direction is upward (i.e., greater than the upper limit of the fluctuation threshold), the floating ratio between the renewable energy volatility and the upper limit of the fluctuation threshold is calculated, and the adjustment base value is adjusted upward based on the floating ratio (i.e., increasing the corresponding ratio; for example, the adjustment base value is increased by 5% for every 1% increase in the floating ratio). The adjusted adjustment base value is set as the adjustment margin. Conversely, if the floating direction is downward (i.e., less than the lower limit of the fluctuation threshold), the floating ratio between the renewable energy volatility and the lower limit of the fluctuation threshold is calculated, and the adjustment base value is adjusted downward based on the floating ratio (i.e., decreasing the corresponding ratio; for example, the adjustment base value is decreased by 2% for every 1% decrease in the floating ratio). The adjusted adjustment base value is set as the adjustment margin. The floating ratio is calculated using the absolute value of the volatility.

[0036] If the load forecast error rate falls within the error threshold range, the deviation base value is set as the deviation threshold. If the load forecast error rate does not fall within the error threshold range, the floating direction is determined. If the floating direction is upward (i.e., greater than the upper limit of the error threshold range), the floating ratio of the load forecast error rate to the upper limit of the error threshold range is calculated. The deviation base value is adjusted upward based on the floating ratio (i.e., the corresponding ratio is increased; for example, the deviation base value is increased by 7% for every 1% increase in the floating ratio). The adjusted deviation base value is set as the deviation threshold. Conversely, if the floating direction is downward (i.e., less than the lower limit of the error threshold range), the floating ratio of the load forecast error rate to the lower limit of the error threshold range is calculated. The deviation base value is adjusted downward based on the floating ratio (i.e., the corresponding ratio is decreased; for example, the deviation base value is decreased by 3% for every 1% decrease in the floating ratio). The adjusted deviation base value is set as the deviation threshold. The corresponding ratios described in the above adjustments are not necessarily the same and need to be set based on historical data analysis of the specific energy storage device. The above corresponding ratio relationships are only examples for understanding and do not represent actual data.

[0037] For example, consider a microgrid system consisting of a solar power plant and a battery storage system. The power output of the solar power plant fluctuates significantly due to weather conditions. To maintain grid stability, the regulation margin of the battery storage system must be dynamically adjusted based on the fluctuation rate of solar power.

[0038] The benchmark regulation margin is set to ±5% of the rated power (assuming the rated power of the energy storage battery pack is 100kW, the benchmark margin is ±5kW).

[0039] Set the volatility threshold range [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%), the benchmark adjustment margin is used; when the volatility exceeds the upper limit of the volatility threshold range, the adjustment margin increases by 5% for every 1% above the upper limit; when the volatility falls below the lower limit of the volatility threshold range, the adjustment margin decreases by 2% for every 1% above the upper limit.

[0040] For example, at time T1, the solar power is 80kW, and the energy storage battery group outputs 20kW (to maintain the total power of the grid at 100kW). Calculating the fluctuation rate, assuming that the power at the previous moment was 75kW, the fluctuation rate is 6.67%, which is lower than the lower limit of the fluctuation threshold range, and the regulation margin is reduced to the baseline value of ±4.5kW.

[0041] At time T2, the solar power is 60kW, and the output of the energy storage battery group increases to 40kW (to maintain the total grid power at 100kW). Calculating the fluctuation rate, the power at time T1 is 80kW, so the fluctuation rate is -25%, which is higher than the upper limit of the fluctuation threshold (the absolute value of the fluctuation rate is used for comparison). According to the mapping relationship, the margin is increased by 50%, that is, the new regulation margin is ±7.5kW (150% of the original ±5kW, which may be rounded up or adjusted according to system rules in actual adjustments).

[0042] With the new ±6kW regulation margin, the energy storage battery pack can adjust its output within a range of 34kW to 46kW (considering the original output of 40kW). In practice, since solar power has dropped to 60kW, increasing the energy storage battery pack's output to 40kW is sufficient to maintain grid stability, but the additional margin (±7.5kW) provides a buffer against possible further fluctuations in the future. Assume that solar power gradually recovers to 70kW. The volatility is recalculated, and the regulation margin is adjusted based on the new volatility. If the volatility falls below the low volatility threshold, the regulation margin returns to the baseline value of ±5kW. The values ​​given in the above example are not actual operating data; they are provided for ease of understanding.

[0043] The deviation threshold refers to the upper limit of the instantaneous power deviation allowed, which is dynamically adjusted according to the changes in the load forecast error rate and is used to control the power deviation of the energy storage device during the charging and discharging process to ensure the stable operation of the power grid. Limiting the power deviation of the energy storage device during the charging and discharging process reduces the risk of scheduling failure due to excessive deviation. The deviation threshold can be dynamically adjusted according to the changes in the load forecast error rate, thereby maintaining the stability of the system under different operating conditions. The higher the load forecast error rate, the greater the deviation between the forecast result and the actual load, so it is necessary to lower the deviation threshold to tighten control and reduce the risk of scheduling failure. By monitoring the load forecast error rate and adjusting the deviation threshold, a closed-loop optimization mechanism can be formed to continuously improve the accuracy of load forecasting.

[0044] Load forecasts often contain errors due to various factors (such as weather changes and user behavior uncertainty). To reduce the risk of scheduling failure caused by load forecast errors, it is necessary to dynamically adjust the deviation threshold to adapt to different forecast error situations.

[0045] When making load forecasts, historical load data, real-time load data, and external influencing factor data are collected as initial data. Specifically, this includes: collecting microgrid load data for a period of time (such as the past week or month), including daily and hourly load values; obtaining the current microgrid load data in real time to update the forecast 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, including missing value filling, outlier processing, and data normalization. The missing value filling uses linear interpolation to fill in the missing period data. The outlier processing is to eliminate a small amount of abnormal data (such as abnormally low load data on typhoon days). The data normalization refers to scaling the load value to [0,1 ] interval; the processed initial data is divided into a training set and a test set in a ratio of 7:3, and key features are extracted, including time features, weather features, and economic features (real-time electricity price signals), and a feature sequence is constructed; the long short-term memory network basic model is used as input, the historical feature sequence is input, and the load forecast value for the future period (such as 1 hour or 24 hours) is output. The model parameters are pre-set as initial parameters, including the number of hidden layer neurons, the loss function, and the optimizer. The loss function uses the mean square error. The training set is input to train the basic model and the model parameters are continuously optimized. The mean absolute percentage error is calculated. If the mean absolute percentage error is less than the preset threshold; the model is verified using the validation set, and the stability of the model is judged according to the validation mechanism. The successfully verified model is used as the load forecast model. Specifically, the load forecast error rate is obtained according to the load forecast model, and the real-time load data and the latest external influencing factor data are input into the trained load forecast model. The load forecast value for the future period (24 hours) is output. At the end of each hour, the actual load data is compared with the predicted value to calculate the load forecast error rate.

[0046] In this embodiment, the actual load is compared with the predicted load, and the load forecast error rate (the ratio of the absolute value of the difference between the actual load value and the predicted value to the actual load value) is calculated. When the error rate falls below a preset threshold (e.g., 5%), the deviation threshold is appropriately lowered or maintained at a baseline value (e.g., ±3%). When the error rate increases, the deviation threshold is lowered to tighten control and reduce the risk of scheduling failure based on the negative feedback relationship between the error rate and the threshold (e.g., when the error rate is 10%, the threshold is reduced to below ±3%, with the specific value adjusted based on system characteristics).

[0047] In high-error-rate scenarios, the system strictly adheres to the reduced deviation threshold and precisely controls the charging and discharging of energy storage devices, reducing the impact of instantaneous power deviations on the grid. Using electricity price signals, it prioritizes charging during low-price periods and discharging during high-price periods, optimizing the economic efficiency of energy storage devices.

[0048] Feedback margin and threshold adjustment data into the load forecasting model for closed-loop optimization to reduce forecast errors. Regularly evaluate key microgrid indicators such as voltage fluctuation, wind and solar curtailment rates, and energy storage device lifespan to verify the effectiveness of the control plan. Based on system performance evaluation results, timely adjust margin and threshold adjustment strategies to achieve continuous optimization of the microgrid.

[0049] In this embodiment, the basic data set and load forecasting model are obtained to obtain the renewable energy volatility and load forecasting error rate, the deviation threshold and adjustment margin are dynamically set, and an initial power consumption strategy is generated. The energy storage device in the microgrid is regulated to charge and discharge according to the initial power consumption strategy.

[0050] In this embodiment, in high-volatility scenarios, by expanding the regulation margin, fluctuations in renewable energy generation are effectively smoothed, reducing the impact on the power grid. By dynamically matching the deviation tolerance, the wind and solar power curtailment rate is reduced, improving the utilization rate of renewable energy. By lowering the deviation threshold, the risk of scheduling failure is reduced, improving the stability and reliability of the power grid. By optimizing the charging and discharging strategies of energy storage devices, unnecessary operations are reduced, the life of the energy storage devices is extended, and operating costs are reduced.

[0051] This embodiment aims to optimize the microgrid's power dispatch and energy storage management by dynamically adjusting the energy storage device's regulation margin and deviation threshold by monitoring key data elements such as renewable energy volatility and load forecast error. Specifically, when renewable energy volatility increases, the energy storage device's regulation margin is increased to accommodate greater power deviations; when load forecast error increases, the deviation threshold is lowered to tighten control. This coupling mechanism enables efficient and stable microgrid operation under complex operating conditions.

[0052] Embodiment 2: In embodiment 1, however, the energy storage battery generates differences when being charged at different powers, which affects the service life of the energy storage device. This embodiment makes further improvements based on the above embodiment.

[0053] In the above content, by monitoring key data elements such as renewable energy fluctuation rate and load forecast error rate, the regulation margin and deviation threshold of the energy storage device are dynamically adjusted to optimize the power dispatch and energy storage management of the microgrid. However, the performance of energy storage batteries varies when charging at different power levels, which affects the service life of the energy storage device.

[0054] The method also includes S2: determining a charge level according to the real-time state of charge of each energy storage device and calculating a single-cycle capacity decay rate; determining an optimal charge and discharge rate for each energy storage device according to the single-cycle capacity decay rate and the charge level, and generating an initial power usage strategy in combination with a deviation threshold and an adjustment margin.

[0055] The charge levels include a core layer, a buffer layer, and a risk layer. The core layer refers to a state of charge between [65%, 100%], the buffer layer refers to a state of charge between [60%, 65%], and the risk layer refers to a state of charge less than 60%. The boundaries of these intervals need to be dynamically calibrated based on the battery type and ambient temperature.

[0056] The formula for calculating the single cycle capacity attenuation rate is as follows:

[0057]

[0058] in, is the single cycle capacity decay rate, is the current capacity, is the capacity at the end of the last cycle, This is the initial rated capacity. In the unlikely event that the current capacity is exactly equal to the capacity at the end of the previous cycle, the capacity decay rate for that cycle cannot be detected (e.g., due to insufficient detection accuracy), and the data for that cycle is discarded. A decay threshold is pre-set based on historical data to determine the change in the single-cycle capacity decay rate calculated at different times for the energy storage device.

[0059] Step S2 also includes: if the single-cycle capacity decay rate is higher than the decay threshold, it is marked as a risk point; setting a monitoring period and recording the total number of risk points within the monitoring period; obtaining the stable value of each energy storage device within the monitoring period based on the change in the charge level; and determining the optimal charge and discharge rate of each energy storage device based on the total number of risk points and the stable value.

[0060] In some embodiments, the risk point refers to a situation where the capacity decay rate per single cycle exceeds a decay threshold. This serves to flag abnormal decay that may occur during the charge and discharge process of the energy storage device, prompting adjustments to the charge and discharge strategy to prevent further deterioration of battery performance. By promptly identifying risk points, appropriate measures can be taken to slow the rate of battery capacity decay, extend battery life, and improve the stability and reliability of the microgrid.

[0061] In some embodiments, the stable value is obtained by adjusting a pre-set initial value based on changes in the charge level. This value reflects the stability of the energy storage device's state of charge over a period of time and provides a reference for determining the optimal charge and discharge rate. If the energy storage device's state of charge remains stable over a period of time, the current charge and discharge strategy is reasonable. Conversely, if the state of charge fluctuates significantly, the charge and discharge rate needs to be adjusted. The initial value is evaluated based on historical data. During the analysis process, the average of the final stable values ​​in the historical data and the magnitude of the increase and decrease during the adjustment process are obtained to set the initial value. If the increase is large, the initial value should be set smaller. One-third of the average of the stable values ​​in the historical data can be used as the initial value. The specific value needs to be dynamically set based on actual conditions. A basic step size and basic duration are pre-set to determine the trend of change. The stable value is then determined based on the charge level change, basic step size, and basic duration. The stable value can more accurately assess the operating status of the energy storage device, thereby formulating a more reasonable charge and discharge strategy and improving the efficiency and lifespan of the energy storage device.

[0062] The charge level change includes positive changes and negative changes. Positive changes add points to the initial value; negative changes subtract points from the initial value; the basic step refers to the minimum unit value of each score adjustment (such as 1 point); the basic duration refers to the standard duration after the level change. If it does not exceed the basic duration, points are added according to the basic step; if it exceeds the standard duration, points are added in proportion based on the correspondence between the basic duration and the basic step.

[0063] For example, if a battery changes from the risk layer to the core layer and maintains a basic duration (e.g., 30 minutes), a basic step size (e.g., 1 point) is added. If a battery changes from the risk layer to the core layer and maintains this duration for 45 minutes, 1.5 points are added, i.e., points are added proportionally. For example, the energy storage battery's state of charge is 70% (core layer), and its single-cycle capacity decay rate is 0.0005% (below the threshold of 0.001%). After a certain charge and discharge cycle, the calculated single-cycle capacity decay rate is 0.0015% (above the threshold of 0.001%). A correction strategy is set: Since the decay rate is above the threshold but still in the core layer, the charge and discharge current is reduced from 10A to 8A, and a 10-minute rest period is added after each charge and discharge. After implementing the correction strategy, the battery's charge and discharge data and capacity decay rate are monitored in real time.

[0064] After a period of monitoring, the single-cycle capacity decay rate was found to have dropped to 0.0008% (below the threshold of 0.001%), demonstrating the effectiveness of the correction strategy. Based on the monitoring results, the charge and discharge currents and rest periods were adjusted appropriately to ensure that the energy storage battery always maintains optimal operating conditions, thereby extending its service life.

[0065] In some embodiments, the specific steps for determining the optimal charge and discharge rate for each energy storage device based on the total number of risk points and the stability value are as follows: If the single-cycle capacity decay rate exceeds the decay threshold, mark it as a risk point. Set a monitoring period, for example, 1 hour. Record the total number of risk points during the monitoring period. Determine the stability value of each energy storage device during the monitoring period based on the change in charge level.

[0066] A threshold for the number of risk points is pre-set. If the total number of risk points exceeds the threshold, the charge and discharge rates are first set based on the risk point status, and then adjusted based on the stability value. If the total number of risk points is large (i.e., greater than the threshold for the number of risk points, for example, more than 3), it indicates that the capacity of the energy storage device has declined significantly, and the charge and discharge rates need to be reduced to reduce the burden on the battery. At the same time, further adjustments are made based on the size of the stability value. A threshold for the stability value is pre-set. If the stability value is low (e.g., less than 5 points), it indicates that the state of charge fluctuates significantly, and the charge and discharge rate needs to be further reduced. If the stability value is high (e.g., greater than 5 points), it indicates that the state of charge is relatively stable, and the charge and discharge rate can be appropriately increased.

[0067] If the total number of risk points is small (for example, less than or equal to 3), the capacity decay of the energy storage device is acceptable, and the charge and discharge rate can be determined based on the stability value. If the stability value is low, the charge and discharge rate can be appropriately reduced; if the stability value is high, the charge and discharge rate can be appropriately increased.

[0068] Suppose there is an energy storage battery. Within a 1-hour monitoring period, the single-cycle capacity decay rate exceeds the decay threshold twice, that is, the total number of risk points is 2.

[0069] During this monitoring period, the battery's state of charge (SOC) changed from the risk level (55%) to the core level (70%), maintaining this level for 40 minutes. According to the stability value calculation rules, the initial value is set at 5 points, and 1.5 points are added for each positive change, resulting in a stability value of 6.5 points.

[0070] Since the total number of risk points is relatively small (2) and the stability value is relatively high (6.5 points), the charge and discharge rate can be appropriately increased. Assuming the original charge and discharge rate is 10A, the charge and discharge rate can be increased to 12A based on actual conditions.

[0071] The specific steps for generating the initial power consumption strategy based on the optimal charge and discharge rate, deviation threshold, and regulation margin are as follows: determine the optimal charge and discharge rate of the energy storage device, obtain the load forecast error rate and renewable energy fluctuation rate, and during the charge and discharge process, the charge and discharge rate of the energy storage device should be controlled near the optimal charge and discharge rate, while meeting the requirements of the deviation threshold and regulation margin.

[0072] When renewable energy generation fluctuates rapidly, the energy storage device can adjust its charge and discharge within the permitted range based on the regulation margin to smooth out the instability of renewable energy generation. For example, when the fluctuation rate exceeds the high fluctuation rate threshold, the regulation margin is increased by 20%, meaning the new regulation margin is ±6kW, and the energy storage device can adjust within the rated power range of ±6kW.

[0073] During the charging and discharging 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.

[0074] Assume that the optimal charge and discharge rate of the energy storage device is 12A and the rated power is 100kW (assuming that current is proportional to power, 12A corresponds to 12kW of power. For ease of calculation, it is assumed here that power and current are linearly related).

[0075] The current volatility of renewable energy is 18%, exceeding the high volatility threshold of 15%. According to the setting rules of the regulation margin, the regulation margin is increased by 50%, and the new regulation margin is ±7.5kW.

[0076] The load forecast error rate is 8%, which exceeds the preset threshold of 5%. According to the setting rules of the deviation threshold, the deviation threshold is reduced to ±2.5%.

[0077] The initial power usage strategy is to control the charging and discharging power of the energy storage device to around 12kW, while also meeting the following conditions:

[0078] When renewable energy power generation fluctuates, the energy storage device can be adjusted within the range of 100kW±6kW (i.e. 94kW-106kW) to smooth out the fluctuations in renewable energy power generation.

[0079] During the charging and discharging 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, adjusting the charge and discharge power from 12kW to 11kW or 13kW to reduce the risk of scheduling failure.

[0080] In this embodiment, over extended use, energy storage batteries can experience internal oxidation, corrosion, thermal runaway, and other phenomena due to the irreversible nature of chemical reactions, leading to a gradual decrease in battery capacity. Calculating the single-cycle capacity decay rate tracks the capacity decay rate of each energy storage battery in real time, providing a basis for subsequent correction strategies, thereby slowing the rate of battery capacity decay and extending battery life.

[0081] In this embodiment, the charge level is determined according to the real-time state of charge of each energy storage device, and the single-cycle capacity decay rate is calculated. The optimal charge and discharge rate of each energy storage device is determined according to the single-cycle capacity decay rate and the charge level, and the initial power consumption strategy is generated in combination with the deviation threshold and the adjustment margin, thereby further optimizing the power dispatching and energy storage management of the microgrid. In high-volatility scenarios, by expanding the adjustment margin, the fluctuations of renewable energy power generation are effectively smoothed and the impact on the power grid is reduced; by dynamically matching the deviation tolerance, the wind and solar power abandonment rate is reduced and the utilization rate of renewable energy is improved. By lowering the deviation threshold, the risk of scheduling 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 actions is reduced, the life of the energy storage device is extended, and the operating cost is reduced. At the same time, by calculating the single-cycle capacity decay rate and determining the optimal charge and discharge rate, the battery capacity decay rate can be slowed down, the battery service life can be extended, and the overall performance of the microgrid can be improved.

[0082] Example 3: Based on the above content, this example groups energy storage devices to address the problems of insufficient autonomous power supply capacity during communication interruptions, the inability of the energy storage system to quickly restore power to critical loads, prolonged black start time or failure, large differences in the SOC of energy storage devices, and uneven power distribution leading to local overload, overcharging, or over-discharging.

[0083] Conventional energy storage systems rely on a central controller to issue voltage and frequency references. When communication is interrupted, they are unable to autonomously generate reference values, leading to system crashes. The central controller is unable to perceive local conditions (such as SOC and power output) in real time, resulting in control delays and misjudgments. The topology is fixed and lacks dynamic adjustment, so energy storage devices always operate in parallel. The circulation path cannot be disconnected, resulting in energy loss and shortened equipment lifespan. The lack of real-time monitoring and rapid response mechanisms for circulation makes it impossible to dynamically reconfigure the topology when circulation exceeds the limit.

[0084] Failure to differentiate between device reliability and functional positioning resulted in all energy storage devices being treated equally. Grouping was not performed based on indicators such as device failure rate and response delay, resulting in the high-reliability group being dragged down by the low-reliability group. Black start capability refers to the ability of the energy storage system to autonomously restore power supply to critical loads through local droop control and topology reconstruction in the event of a communication outage or a complete power outage in the grid. High-reliability groups were not prioritized during black starts, resulting in slow response and susceptibility to failures. Traditional droop control uses a fixed droop coefficient and cannot be dynamically adjusted based on SOC and power demand, resulting in poor SOC balance and uneven power distribution. Failure to consider the differences in characteristics between different groups resulted in poor control effectiveness.

[0085] The method further includes: S3: grouping the energy storage devices into a high-reliability group, a medium-reliability group, and a low-reliability group according to the analysis of the initial power consumption strategy results; and initializing the droop coefficient and the topology reconstruction strategy.

[0086] In some embodiments, based on the risk points (such as equipment failure rate and response delay) and stability values ​​(such as SOC fluctuation range and charge and discharge efficiency) of steps S1 and S2 and the analysis of the results of the initial power consumption strategy, an analysis of the actual application results of different energy storage devices is determined, and the energy storage devices are grouped into a high-reliability group, a medium-reliability group, and a low-reliability group. Specifically, grouping indicators are set, including but not limited to: equipment failure rate, response delay, SOC fluctuation range, charge and discharge efficiency, the average number of risk points in multiple monitoring cycles, and the average stability value. The division is carried out according to the grouping indicators, for example, the equipment failure rate of the high-reliability group is ≤5%, the response delay is ≤50ms, the SOC fluctuation range is between [-5%, 5%], the charge and discharge efficiency is ≽95%, the average number of risk points in multiple monitoring cycles is less than 3, and the average stability value is greater than 7 points.

[0087] In some embodiments, the droop coefficient is a core parameter of droop control, directly affecting the system's voltage / frequency stability and power distribution. Initializing the droop coefficient requires comprehensive consideration of factors such as system capacity, SOC range, and communication capabilities. The voltage droop coefficient represents the change in output voltage (in V / %) for every 1% change in SOC; the frequency droop coefficient represents the change in output frequency (in Hz / kW) for every 1kW change in power output. Droop control is a distributed control strategy that dynamically adjusts the output voltage / frequency based on local measurements (such as SOC and power output), allowing the system to spontaneously form a reference in non-communication mode.

[0088] Initializing the droop coefficient involves determining system parameters: rated voltage, rated frequency, SOC range, and total energy storage device capacity; setting the droop coefficient range. The voltage droop coefficient typically ranges from [0.1, 1] (in V / %). For example, a high-reliability group requires a fast response, so a larger value (such as 0.5 V / %) is used; a low-reliability group requires a slow response, so a smaller value (such as 0.2 V / %) is used. The frequency droop coefficient typically ranges from [0.005, 0.02]. For example, a high-reliability group requires a larger value (such as 0.01 Hz / kW) for faster power balancing. The corresponding droop coefficients are set for each group.

[0089] In some embodiments, topology reconfiguration dynamically adjusts the energy storage device connection based on system status (e.g., SOC, power demand, and communication status) to optimize system performance. The goal of topology reconfiguration is to prioritize access to high-reliability groups during communication interruptions, ensuring a stable voltage / frequency reference. Once communication is restored, power distribution is optimized to avoid overload or underload.

[0090] The initial topology reconfiguration strategy involves connecting all energy storage devices to the busbar via local switches, but only activating the high-voltage backup group during a communication interruption. Topology reconfiguration during a communication interruption: Step 1: Real-time monitoring of communication status to detect a communication interruption (e.g., no central controller command for more than one second). Step 2: Disconnecting the mid-voltage backup group and the low-voltage backup group from the busbar, leaving only the high-voltage backup group powered. Step 3: The high-voltage backup group establishes a voltage and frequency reference through local droop control.

[0091] Gradually connect the mid- and low-bank groups: Step 1: The mid- and low-bank groups measure their local SOC and voltage / frequency. Step 2: Adjust the output voltage / frequency based on the droop factor, gradually approaching the reference value of the high-bank group. Step 3: When the output voltage / frequency of the mid- and low-bank groups approaches that of the high-bank group (e.g., errors < 1V and 0.01Hz), close the switch to connect to the system.

[0092] Topology Reconfiguration After Communication Restoration: Step 1: Detect communication restoration (e.g., receiving a command from the central controller). Step 2: The central controller reallocates droop coefficients based on global SOC and power requirements. Step 3: Optimize power distribution to avoid overloading high-reliability groups.

[0093] Step S3 also includes: when a communication interruption is detected, using the high-reliability group to perform local preliminary droop control to form a voltage reference and a frequency reference; real-time monitoring of the local measurement results of the medium-reliability group and the low-reliability group, and calculating the difference between the voltage reference and the frequency reference respectively. If the difference is less than the error range, the power supply system will be connected to achieve power balance through comprehensive droop control.

[0094] In power systems, the voltage reference is the voltage stability value that a microgrid system is expected to maintain, typically the rated voltage (e.g., 400V). It is the foundation for stable system operation, and the output voltage of all energy storage devices should fluctuate around this reference value. The frequency reference is the frequency stability value that a microgrid system is expected to maintain, typically the rated frequency (e.g., 50Hz or 60Hz). It serves as the frequency benchmark for the output power of synchronous generators or energy storage systems, and the output frequencies of all devices should fluctuate around this reference value.

[0095] In traditional centralized control, a central controller generates and distributes voltage / frequency references to each energy storage device. If communication is interrupted, the central controller cannot distribute the reference values, and the energy storage devices lose their unified voltage / frequency benchmark, leading to system instability (such as voltage collapse or frequency drift). This embodiment further improves accuracy by grouping devices according to pre-defined groups and selecting the appropriate group to generate the voltage / frequency reference.

[0096] The high-voltage group has the most stable SOC and lower power output, and its output voltage and frequency are closer to the rated values. Its high droop coefficient enables rapid adjustment of output voltage and frequency, suppressing system fluctuations. The remaining energy storage devices follow the voltage and frequency of the high-voltage group through droop control, forming a unified reference baseline.

[0097] Droop control includes voltage droop and frequency droop. Voltage droop refers to the adjustment of the energy storage device's output voltage as the state of charge (SOC) changes. The higher the SOC, the higher the output voltage. When the SOC is high, increasing the output voltage reduces the charging current, thereby preventing battery overcharging. Through voltage droop, devices with high SOCs have a higher output voltage and are discharged first, while devices with low SOCs have a lower output voltage and are charged first, achieving SOC balance. The higher the SOC, the higher the output voltage and the lower the charging current, preventing overcharging.

[0098] Frequency droop refers to the adjustment of the output frequency of the energy storage device as its power output changes. The greater the power output, the lower the frequency. When the power output is high, lowering the frequency can reduce the power output of other devices (based on the power-frequency characteristic), thereby preventing system overload. Through frequency droop, devices with high power output have a lower frequency, while those with low power output have a higher frequency, achieving power balance. Higher power output leads to lower frequency, reducing the power output of other devices and preventing overload.

[0099] The local measurement results of the medium-reliability group and the low-reliability group are monitored in real time, and the differences between them and the voltage reference and frequency reference are calculated respectively. If the differences are all less than the error range, they will be connected to the power supply system and power balance will be achieved through comprehensive droop control. For example, the initial conditions before connecting to the power supply system are:

[0100] H group (high-voltage group) status: H1: SOC = 75%, output voltage 395V, output frequency 49.95Hz; H2: SOC = 80%, output voltage 392.5V, output frequency 49.92Hz;

[0101] Group M (middle group) status: M1: SOC = 70%, initially not connected to the system;

[0102] L group (low-dependence group) status: L1: SOC = 50%, initially not connected to the system;

[0103] Droop coefficient: voltage droop coefficient 0.5V / ; frequency droop coefficient 0.01Hz / kW;

[0104] M1 needs to be gradually connected to the system, and its target voltage / frequency should be close to the reference values ​​of Group H. Assume that M1's initial connection voltage is the average voltage of Group H, that is, 393.75V; M1's initial connection frequency is the average frequency of Group H, that is, 49.935Hz. M1 adjusts its output voltage / frequency in small steps (such as 1V and 0.01Hz per second) to gradually approach the target values. When M1's output voltage / frequency is close to that of Group H (for example, if the error is <1V and 0.01Hz, it is within the error range), it is officially connected to the system.

[0105] After M1 is connected, the system states include H1, H2, and M1. M1 adjusts the frequency according to the local power output. As the load increases, the power output of M1 increases and the frequency gradually decreases.

[0106] 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 of its lower SOC, the output voltage should be lower). The target voltage corresponding to L1 is calculated based on the voltage droop coefficient of 0.5V / %. The target voltage calculation formula is as follows:

[0107]

[0108] in, is the target voltage of L1, is the rated voltage, here it is 400V; is the voltage droop coefficient; is the maximum capacitance, which is 100%; is the capacitance of L1; the target voltage is calculated to be 375V.

[0109] The target frequency corresponding to L1 is calculated based on the frequency droop coefficient of 0.01Hz / kW. The target frequency calculation formula is as follows:

[0110]

[0111] in, 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 49.501 Hz. Subsequently, based on power output, L1 adjusts its output voltage / frequency in small steps (e.g., 1 V and 0.01 Hz per second) to gradually approach the target value. When L1's output voltage / frequency approaches the target value (e.g., within the error range if the error is < 1 V and 0.01 Hz), it is officially connected to the system.

[0112] The frequency of all devices fluctuates around 49.935Hz (adjusted based on power output). Devices with high SOC (Group H) are prioritized for discharge, while devices with low SOC (Group L) are prioritized for charging or low power output. Through these steps, Groups M and L can safely and stably connect to the system, achieving black start and autonomous power supply in the event of a communication interruption. In actual testing, the droop coefficient and topology are dynamically adjusted to continuously optimize monitoring and control of the energy storage devices and generate an optimal control solution.

[0113] S4: Real-time monitoring of the state of charge and circulating current. When it is detected that the circulating current exceeds a preset circulating current threshold, the middle lean group switches several devices into a series structure through a solid-state circuit breaker to reduce the circulating current path.

[0114] In some embodiments, solid-state circuit breakers (SSCBs) are used to suppress circulating current by rapidly switching the series and parallel configurations of energy storage devices. In off-grid mode, the energy storage devices are switched to a series configuration to increase output voltage and reduce circulating current paths. In grid-connected mode, the devices are switched to a parallel configuration to increase power output capacity. Circulating current suppression involves dynamically adjusting the topology through real-time monitoring of circulating current. For example, when the circulating current exceeds a threshold, some devices are switched to a series configuration.

[0115] Prioritize the deployment of SSCBs in medium-reliability groups to achieve dynamic topology reconstruction and suppress circulation losses when off-grid. Use high-reliability and low-reliability groups to selectively deploy SSCBs based on actual needs, balancing cost and performance.

[0116] In some embodiments, the method further includes: dynamically adjusting the droop coefficient to balance the output power of each device to avoid local overload; regularly evaluating the effect of circulating current suppression and optimizing the reconstruction strategy.

[0117] In this embodiment, each energy storage device is equipped with a voltage sensor, a current sensor, and a SOC sensor, which are used to monitor the output voltage (for voltage droop control), the output current (for frequency droop control), and the remaining capacity (i.e., the state of charge, for voltage droop and SOC balance) in real time, respectively.

[0118] The high-reliability group is preferentially used for black start and critical load power supply. It is equipped with high-precision local measurement modules and fast-response solid-state circuit breakers to form an autonomous power supply core in non-communication mode, maintaining voltage / frequency stability through local droop control.

[0119] The middle support group is used for power balancing and circulating current suppression in the off-grid state, and has the ability to reconfigure the topology. By reconfiguring the topology, circulating current suppression is achieved, thereby improving energy efficiency in the off-grid state.

[0120] The low-reliability group is used for long-term energy storage and low-frequency fluctuation regulation, and is equipped with a low-cost local control module to reduce system costs. At the same time, it reduces the risk of fault propagation through group isolation.

[0121] In some embodiments, local sensors monitor SOC, voltage, current, and other parameters in real time to dynamically adjust grouping configurations. For example, when a device's SOC falls below a threshold, it is downgraded from a high-reliability group to a medium-reliability group.

[0122] The technical solution of this embodiment improves autonomous power supply capabilities during communication interruptions: critical load power restoration time is shortened to less than 10 seconds, and voltage and frequency fluctuations are controlled within ±5%. Circulation current suppression is achieved: Circulation current losses are reduced by over 50% in off-grid conditions, and system efficiency is improved by 10%-15%. Black start success rate: Under extreme operating conditions, the black start success rate is increased to over 90%. System stability: The risk of fault propagation is reduced by over 40%, and SOC balance is improved by 20%.

[0123] In this embodiment, the collaborative design of dynamic grouping, local droop control, and dynamic topology reconstruction addresses the shortcomings of existing energy storage systems in terms of communication interruption, circulating current loss, and black start capability. Local measurement and droop control enable the energy storage device to achieve voltage and frequency self-stabilization in a non-communication mode, maintaining power supply to critical loads. Dynamic topology reconstruction technology suppresses circulating current loss in an off-grid state, improving the energy storage system's energy utilization efficiency. The grouping strategy not only optimizes system resource allocation but also achieves autonomous power supply and circulating current suppression in a non-communication mode through the differentiated design of high-reliability, medium-reliability, and low-reliability groups, significantly improving the reliability and efficiency of the energy storage system under extreme operating conditions.

[0124] Example 4: This example makes further improvements based on the above content.

[0125] The method also 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, and establishing a digital twin model; if a fault risk is detected, obtaining the grouping status and charge state distribution data, re-determining the grouping scheme based on the digital twin model, simulating the performance indicators under different grouping strategies, and generating an evaluation result; generating a topology reconstruction path based on the evaluation result, and predicting potential fault points and the probability of fault occurrence.

[0126] In some embodiments, the evaluation results include the state of charge (SOC) balance, power support capability and circulation suppression effect of each grouping scheme; the performance indicators include power distribution, circulation path, voltage fluctuation and frequency fluctuation.

[0127] In some embodiments, the historical fault database includes all fault conditions that occurred over a period of time, along with the corresponding energy storage devices, the time of occurrence, and the treatment plan. By building a virtual mirror of the physical system, the digital twin enables real-time status monitoring, fault prediction, and optimized decision-making. The core functions of the digital twin model are defined to optimize the topology reconfiguration of the energy storage system, perform fault prediction, and analyze SOC balance, thereby increasing the service life of the energy storage devices. For energy storage systems, a closed-loop process of "real-time status synchronization - fault risk rehearsal - reconfiguration strategy generation" is required. This enables efficient management and fault rehearsal of the energy storage system, significantly improving system reliability and O&M efficiency.

[0128] In some embodiments, when the system detects load fluctuations or fault risks, the digital twin model generates multiple grouping schemes (such as adjusting the H / M group boundary and adding an L group isolation device) based on the current SOC distribution and health. It simulates power distribution, circulating current paths, and voltage / frequency fluctuations under different grouping strategies, evaluating each scheme's SOC balance, power support capability, and circulating current suppression effectiveness. For potential circulating current paths or overload risks identified during pre-tests, the digital twin model generates topology reconstruction paths (such as switch switching sequences and parallel device reorganization schemes). Simulation verifies the feasibility of the reconstructed paths, selecting the paths with minimal voltage fluctuations and optimal circulating current suppression.

[0129] Combining historical fault data with real-time status, the digital twin model predicts potential failure points (e.g., devices with SOH below a threshold or switches on frequently circulating current paths). Before topology reconfiguration, the digital twin model simulates fault isolation solutions (e.g., disconnecting parallel branches of faulty devices or adjusting group boundaries) and assesses the stability of the system after isolation. If the predicted fault risk exceeds a threshold, isolation is prioritized to prevent the fault from spreading to other groups.

[0130] In this embodiment, a dynamic grouping strategy for energy storage devices is deeply integrated with digital twin technology. By constructing a digital twin model of the energy storage system's entire lifecycle, a joint simulation verification of the grouping strategy and topology reconfiguration is achieved. The digital twin model synchronizes the physical system's grouping status, SOC distribution, and historical fault data in real time. Before reconfiguration, the digital twin model previews voltage / frequency stability, circulating current suppression, and fault propagation risk under different grouping strategies. It dynamically optimizes grouping schemes and reconfiguration paths, enhancing the system's adaptability under complex operating conditions.

[0131] In this embodiment, through digital twin rehearsal, circulation or overload problems caused by blind grouping are avoided, and the rationality of the grouping strategy is improved. For example, when the load suddenly increases, the digital twin model can quickly verify the feasibility of temporarily merging some M group devices into 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 attenuation of lithium battery capacity, the model can gradually lower the SOC lower limit threshold of H group to avoid power support failure due to insufficient capacity.

[0132] This embodiment uses digital twin technology to deeply couple dynamic grouping with topology reconstruction, achieving an upgrade from passive response to active rehearsal. This improves system robustness while reducing operation and maintenance costs and equipment loss, providing key technical support for the intelligent management of large-scale distributed energy storage systems.

[0133] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A monitoring and control method for an energy storage device in a microgrid, characterized in that: include: S1: Obtain renewable energy fluctuation rate and load forecast error rate based on the basic data set and load forecast model, and set the deviation threshold and regulation margin based on the mapping relationship; S2: Determine the charge level based on the real-time state of charge of each energy storage device and calculate the single cycle capacity decay rate; Determine the optimal charge and discharge rate for each energy storage device based on the single-cycle capacity decay rate and charge level, and generate an initial power usage strategy based on the deviation threshold and adjustment margin; Step S2 also includes: if the single cycle capacity decay rate is higher than the decay threshold, marking it as a risk point; setting a monitoring period and recording the total number of risk points within the monitoring period; obtaining a stable value of each energy storage device within the monitoring period based on the change in charge level; and determining the optimal charge and discharge rate of each energy storage device based on the total number of risk points and the stable value. The stability value is obtained by adjusting the preset initial value according to the change of the charge level, and is used to reflect the stability of the charge state change of the energy storage device over a period of time; the charge level change includes positive changes and negative changes, and positive changes are added to the initial value; negative changes are subtracted from the initial value; The basic step length and basic duration are pre-set, and a stable value is obtained based on the charge level change, basic step length, and basic duration. The basic step length refers to the minimum unit value of each score adjustment; the basic duration refers to the standard duration after the level change. If the basic duration is not exceeded, points are added according to the basic step length. If the standard duration is exceeded, points are added proportionally based on the corresponding relationship between the basic duration and the basic step length. Determining the optimal charge and discharge rate for each energy storage device based on the total number of risk points and the stability value includes: presetting a risk point number threshold and a stability value threshold, comparing them respectively; if the total number of risk points is greater than the risk point number threshold, first setting the charge and discharge rate based on the risk point status, and then adjusting it based on the stability value; S3: The energy storage devices are grouped into high-reliability, medium-reliability, and low-reliability groups based on the analysis of the initial power consumption strategy results. When a communication interruption is detected, the high-reliability group is used for preliminary local droop control to form a voltage reference and frequency reference. The local measurement results of the medium-reliability and low-reliability groups are monitored in real time, and the differences between them and the voltage reference and frequency reference are calculated respectively. If the differences are all within the error range, they will be connected to the power supply system to achieve power balance through comprehensive droop control.

2. The monitoring and control method for an energy storage device in a microgrid according to claim 1, wherein: The method further comprises: S4: Real-time monitoring of the state of charge and circulating current. When it is detected that the circulating current exceeds the preset circulating current threshold, the middle lean group switches several devices into 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, the historical fault database and real-time status monitoring data are integrated to establish a digital twin model. If a fault risk is detected, the grouping status and state of charge distribution data are obtained, and the grouping scheme is re-determined based on the digital twin model. The performance indicators under different grouping strategies are simulated and evaluation results are generated. Based on the evaluation results, a topology reconstruction path is generated, and potential fault points and the probability of fault occurrence are predicted.

3. The monitoring and control method for an energy storage device in a microgrid according to claim 1, wherein: The basic data set includes energy storage basic data, power data and environmental data; The mapping relationship is based on a pre-set fluctuation threshold range, error threshold range, deviation reference value, and adjustment reference value. The adjustment reference value is adjusted accordingly according to the proportional relationship between the renewable energy fluctuation rate and the fluctuation threshold range to determine the adjustment margin; the deviation reference value is adjusted accordingly according to the proportional relationship between the load forecast error rate and the error threshold range to determine the deviation threshold; The regulation margin refers to the dynamic adjustment capability range reserved by the energy storage device for fluctuations in renewable energy power generation under the charging and discharging strategy; The deviation threshold refers to the upper limit of the allowable instantaneous power deviation.

4. The method for monitoring and controlling an energy storage device in a microgrid according to claim 1, wherein: The charge level includes a core layer, a buffer layer and a risk layer; the core layer refers to a charge state between [65%, 100%], the buffer layer refers to a charge state between [60%, 65%], and the risk layer refers to a charge state less than 60%.

5. The monitoring and control method for an energy storage device in a microgrid according to claim 1, wherein: The voltage reference is a voltage stability value that the microgrid expects to maintain, and the frequency reference is a frequency stability value that the microgrid expects to maintain; The droop control includes voltage droop and frequency droop. The voltage droop refers to the adjustment of the output voltage of the energy storage device as the state of charge changes. The higher the state of charge, the higher the output voltage. The frequency droop refers to the adjustment of the output frequency of the energy storage device as the power output changes. The greater the power output, the lower the frequency.

6. The method for monitoring and controlling an energy storage device in a microgrid according to claim 1, wherein: Step S3 also includes: initializing the droop coefficient and initializing the topology reconstruction strategy; Initializing the droop coefficient includes: determining microgrid system parameters, setting a droop coefficient range, and setting corresponding droop coefficients in groups; The initialization topology reconstruction strategy includes: real-time monitoring of communication status, disconnecting the middle and low-reliability groups from the busbar when communication is interrupted, retaining only the high-reliability group for power supply, and forming a voltage reference and a frequency reference based on the high-reliability group through local droop control.

7. The method for monitoring and controlling an energy storage device in a microgrid according to claim 2, wherein: The evaluation results include the charge state balance, power support capability and circulation suppression effect of each grouping scheme; the performance indicators include power distribution, circulation path, voltage fluctuation and frequency fluctuation.

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