Virtual power plant-oriented energy storage power station combined operation and maintenance management system

Abstract

The invention relates to the technical field of energy storage power station combined operation and maintenance management, and provides a virtual power plant-oriented energy storage power station combined operation and maintenance management system, which comprises a dynamic sensing module for acquiring battery cluster temperature difference distribution, power converter switch transient characteristics and grid-connected point harmonic spectrum data of an energy storage power station in real time through a multi-dimensional sensor network; the collaborative decision-making module is used for dynamically generating a cross-station power interaction constraint rule based on the virtual power plant topological structure; the impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; the state balancing module is used for realizing charge state dynamic balance and power margin collaborative distribution among the multiple energy storage power stations; and the secure communication module is used for ensuring secure transmission of control instructions among the modules by adopting a layered encryption protocol. The operation stability, the power distribution flexibility and the equipment safety of the virtual power plant can be improved, and the service life of the energy storage equipment is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of joint operation and maintenance management of energy storage power stations. More specifically, the present invention relates to a joint operation and maintenance management system for energy storage power stations for virtual power plants. Background Art

[0002] With the transformation of energy structures and the rapid development of distributed energy, virtual power plants (VPPs) are gaining increasing attention as a new management model for integrating distributed energy resources. By coordinating and controlling distributed generation units (DGs), energy storage systems, and other resources, VPPs achieve efficient energy utilization and a stable supply. Energy storage power stations, as a key component of VPPs, provide flexible power regulation and energy storage capabilities, playing a key role in balancing supply and demand, improving power quality, and enhancing grid stability.

[0003] However, the operation and management of energy storage power stations face numerous challenges. On the one hand, the dynamic changes in parameters such as battery cluster temperature differences within the energy storage station, the transient characteristics of power converter switching, and the harmonic spectrum at the grid connection point place high demands on the safe operation and lifespan management of the power station. On the other hand, in the joint operation of multiple energy storage stations in a virtual power plant, how to achieve dynamic optimization of cross-station power interaction, balanced distribution of charge status, and real-time adaptation of grid impedance characteristics are currently urgent issues that need to be addressed. Furthermore, as the scale of energy storage power stations expands and the operating environment becomes more complex, communication security and equipment health status monitoring have become key factors affecting system reliability.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing energy storage power station management system has insufficient perception accuracy of dynamic parameters, making it difficult to accurately reflect the operating status of the equipment in real time; the cross-station power allocation strategy lacks flexibility and adaptability, and cannot effectively respond to changes in the virtual power plant topology and fluctuations in real-time power demand; the charge state balancing mechanism between energy storage power stations is not perfect, which can easily lead to overcharging or over-discharging of some power stations, affecting the life of the equipment; at the same time, the impact of the grid impedance characteristics on the output of the energy storage converter has not been fully optimized, and the communication security mechanism is not perfect, and there is a risk of data leakage and instruction tampering. Summary of the Invention

[0005] The present invention provides a joint operation and maintenance management system for energy storage power stations for virtual power plants, including: The dynamic sensing module collects data on the temperature difference distribution of the battery cluster, transient characteristics of the power converter switches, and harmonic spectrum of the grid connection point in real time through a multi-dimensional sensor network. A collaborative decision-making module, connected to the dynamic perception module, dynamically generates cross-site power interaction constraint rules based on the virtual power plant topology; An impedance reconstruction module, connected to the collaborative decision-making module, adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; A state balancing module, connected to the impedance reconstruction module, to achieve dynamic balancing of state of charge and coordinated allocation of power margin among multiple energy storage power stations; The secure communication module uses a layered encryption protocol to ensure the secure transmission of control instructions between modules.

[0006] Furthermore, the collaborative decision-making module performs the following steps: (1) The initial power allocation weight is calculated based on the equivalent impedance ratio of adjacent nodes, where the inverse relationship between the local measured impedance and the reference impedance value is used as the calculation basis, and an exponential decay compensation factor for the communication delay time is introduced; (2) The product of the real-time available capacity of the energy storage power station and the absolute value of the power demand deviation is input into the hyperbolic tangent function to generate a dynamic correction factor; (3) The initial power distribution weight and the dynamic correction factor are nonlinearly weighted and fused to generate the final distribution coefficient for controlling the output power of the energy storage converter.

[0007] Furthermore, the state balancing module includes: The multi-source fusion unit uses the Kalman filter algorithm to fuse the state of charge estimate reported by the battery management system with the real-time power measurement value to generate the corrected state of charge data; The dispersion control unit calculates the state of charge dispersion of all energy storage power stations in the virtual power plant and triggers the cross-station balancing mechanism when the dispersion exceeds the threshold; The dynamic response unit automatically generates the charge and discharge rate adjustment value of each power station according to the dispersion reduction target, so that the discharge rate increase value of the high state of charge power station and the charging rate increase value of the low state of charge power station form a dynamic balance.

[0008] Furthermore, the impedance reconstruction module performs the following operations: (1) Real-time monitoring of the voltage harmonic distortion rate at the grid connection point. When a distorted waveform dominated by a specific subharmonic is detected, a compensation strategy of parallel virtual resistance or series virtual reactance is selected; (2) Generate virtual impedance parameters based on the product relationship between the harmonic order and the equivalent impedance of the power grid, where higher harmonics correspond to larger impedance compensation coefficients; (3) By adjusting the harmonic injection component of the pulse width modulation wave, the output impedance frequency characteristics of the energy storage converter are changed.

[0009] Furthermore, the dynamic perception module includes: High-frequency feature unit, which captures transient voltage overshoot and current change rate of power semiconductor devices at microsecond sampling intervals; Thermal coupling unit, which establishes a dynamic correlation model between power device junction temperature fluctuation and heat sink temperature gradient; The fault warning unit generates a device aging warning signal when it detects that the voltage overshoot amplitude exceeds 30% of the rated value and the current change rate exceeds the safety threshold.

[0010] Furthermore, the collaborative decision-making module also includes: The delay compensation unit dynamically adjusts the weight update period according to the round-trip delay of the communication network and its change rate, wherein the update period is extended as the delay increases, and the emergency update mode is enabled when the delay change rate exceeds a threshold; The conflict arbitration unit, when detecting a conflict in the power demands of multiple power stations, prioritizes the power requests of power stations whose state of charge deviates more from the median value, and dynamically adjusts the priority weights based on the health status of the equipment.

[0011] Furthermore, the state balancing module implements the following protection strategies: (1) During the state of charge balancing process, when it is detected that the temperature difference of any battery cluster exceeds the safety threshold, the balancing operation is automatically suspended and the forced cooling mode is activated; (2) The power adjustment rate is safely limited. When the adjustment amount exceeds the preset value, a buffer transition process is inserted, and its ramp time is positively correlated with the adjustment amount. (3) The number of historical balancing times is accumulated. When the set threshold is reached, the battery life evaluation is triggered and the evaluation results are fed back to the power allocation weight calculation process.

[0012] Furthermore, the impedance reconstruction module further includes: The resonance suppression unit compares the amplitude and phase relationship between the energy storage system output impedance and the grid impedance in real time, activating active damping control when the two are close to matching; The phase synchronization unit adopts adaptive phase-locked technology to keep the grid-connected current phase tracking error less than 0.5 degrees within the grid frequency deviation range of 2Hz. Its tracking speed is nonlinearly positively correlated with the frequency deviation value.

[0013] Furthermore, the secure communication module implements: (1) A triple verification mechanism for control instructions, including device hardware fingerprint authentication, operation timing stamp verification, and data hash value integrity comparison; (2) Sharded encrypted storage of key parameters, splitting the running data into multiple data blocks and storing them in distributed nodes using different encryption keys; (3) Dynamic defense against replay attacks, generating real-time changing encryption tokens through XOR operations of serial number, timestamp and key.

[0014] Furthermore, the fault warning unit includes: The life prediction subunit establishes an exponential decay relationship model between the junction temperature fluctuation amplitude and duration of power devices and their remaining life, where the high temperature holding time has a cumulative amplification effect on life decay; The health assessment subunit generates a module replacement recommendation and automatically adjusts the power allocation weight of the energy storage power station when it detects that the conduction characteristics of multiple switching devices in the same power module differ by more than 15%.

[0015] According to the above-mentioned embodiments of the present invention, there are at least the following beneficial effects: the present invention can accurately grasp the various key parameters inside the energy storage power station through the high-precision real-time data collection of the dynamic perception module, and provide a reliable basis for subsequent optimization control. The collaborative decision-making module dynamically generates cross-station power interaction constraint rules based on the virtual power plant topology structure, and combines the nonlinear weighted fusion of the initial power allocation weight and the dynamic correction factor to flexibly adapt to topology changes and real-time power demand fluctuations to achieve efficient power distribution. The impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the grid, which can effectively suppress harmonic distortion, optimize grid-connected performance, and improve the stability and compatibility of the system. The state balancing module can achieve dynamic balance of charge status and collaborative distribution of power margin between multiple energy storage power stations through multi-source fusion, discreteness control and dynamic response mechanism, avoid overcharging or over-discharging of some power stations, and extend the service life of the equipment.

[0016] The present invention also has significant advantages in terms of system security and equipment health status management. The secure communication module adopts a layered encryption protocol, a triple verification mechanism for control instructions, sharded encrypted storage of key parameters, and a dynamic defense replay attack mechanism, which can effectively ensure the secure transmission of control instructions between modules, prevent data leakage and instruction tampering, and improve the overall security of the system. The fault warning unit can timely detect the aging and potential failure of power devices through high-frequency feature monitoring, thermal coupling analysis, life prediction and health assessment, and provide early warning and adjust power distribution to reduce the risk of equipment damage, further improving the operational reliability and maintenance efficiency of energy storage power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which: Figure 1 A schematic diagram of the structure of a joint operation and maintenance management system for energy storage power stations for virtual power plants provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0018] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0019] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0020] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0021] Reference below Figure 1 , Figure 1 This is a schematic diagram of the structure of a joint operation and maintenance management system for energy storage power stations oriented to virtual power plants provided by one embodiment of the present invention. Figure 1 As shown, a joint operation and maintenance management system 100 for energy storage power stations for virtual power plants includes: Dynamic sensing module 101 collects data on the temperature difference distribution of battery clusters, transient characteristics of power converter switches, and harmonic spectrum of grid connection points in real time through a multi-dimensional sensor network. A collaborative decision-making module 102 is connected to the dynamic perception module and dynamically generates cross-site power interaction constraint rules based on the virtual power plant topology; The impedance reconstruction module 103 is connected to the collaborative decision-making module and adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; The state balancing module 104 is connected to the impedance reconstruction module to achieve dynamic balancing of the state of charge and coordinated allocation of power margin among multiple energy storage power stations; The secure communication module 105 uses a layered encryption protocol to ensure secure transmission of control instructions between modules.

[0022] It should be noted that the dynamic perception module in this system is the basic part of the entire operation and maintenance management system. It collects a variety of key data of the energy storage power station in real time through a multi-dimensional sensor network. The multi-dimensional sensor network here refers to a comprehensive sensor system that can simultaneously monitor different types of physical quantities, such as the temperature difference distribution of the battery cluster, the switching transient characteristics of the power converter, and the harmonic spectrum of the grid connection point. These data are crucial for subsequent collaborative decision-making and impedance adjustment operations because they directly reflect the real-time operating status of the energy storage power station. For example, the temperature difference distribution data of the battery cluster can help determine whether the thermal management of the battery pack is normal, while the switching transient characteristics of the power converter are related to the safe operation and life assessment of the equipment.

[0023] Specifically, the high-frequency feature unit in the multi-dimensional sensor network captures transient voltage overshoots and current change rates of power semiconductor devices at microsecond sampling intervals. This means the sensors can record the voltage and current changes of power devices at the switching moment in an extremely short time, providing timely data support for fault warning. The thermal coupling unit establishes a dynamic correlation model between power device junction temperature fluctuations and heat sink temperature gradients. Specifically, by monitoring the junction temperature changes of power devices and the temperature distribution of the heat sink, it analyzes the dynamic relationship between the two to better understand the thermal operation status of the device. The fault warning unit generates a device aging warning signal when it detects a voltage overshoot exceeding 30% of the rated value and a current change rate exceeding the safety threshold. The rated value here refers to the maximum voltage or current value allowed during normal operation of the device, while the safety threshold is a critical value set according to the device's safe operating standards. If this value is exceeded, it may indicate a potential failure risk.

[0024] Preferably, the dynamic perception module can further refine its operating steps. For example, in the high-frequency feature unit, a higher-precision sampling device can be used to shorten the sampling interval to the nanosecond level to more accurately capture the transient characteristics of power semiconductor devices. In the thermal coupling unit, a more complex dynamic model can be introduced to consider the thermal conduction and heat dissipation characteristics of power devices under different load conditions, thereby more accurately predicting the temperature changes of the equipment. In addition, the fault warning unit can combine machine learning algorithms to automatically adjust the warning threshold based on historical data to improve the accuracy and reliability of the warning.

[0025] In some embodiments, the collaborative decision module performs the following steps: (1) The initial power allocation weight is calculated based on the equivalent impedance ratio of adjacent nodes, where the inverse relationship between the local measured impedance and the reference impedance value is used as the calculation basis, and an exponential decay compensation factor for the communication delay time is introduced; (2) The product of the real-time available capacity of the energy storage power station and the absolute value of the power demand deviation is input into the hyperbolic tangent function to generate a dynamic correction factor; (3) The initial power distribution weight and the dynamic correction factor are nonlinearly weighted and fused to generate the final distribution coefficient for controlling the output power of the energy storage converter.

[0026] It should be noted that the collaborative decision-making module is the core component of this system for optimizing energy storage power distribution. It dynamically generates allocation coefficients for controlling the output power of energy storage converters through a series of calculation steps. First, the module calculates initial power allocation weights based on the equivalent impedance ratios of adjacent nodes. These nodes refer to interconnected energy storage plants or power interaction points within a virtual power plant. The equivalent impedance ratios refer to the relative impedance characteristics of these nodes within the power grid. The initial weights are calculated based on the inverse relationship between the local measured impedance and the baseline impedance value. An exponentially decaying compensation factor for communication delay is introduced to account for the impact of communication delay on power allocation. Next, the module applies the product of the energy storage plant's real-time available capacity and the absolute value of the power demand deviation to a hyperbolic tangent function to generate a dynamic correction factor, which is used to adjust power allocation based on real-time operating conditions. Finally, a nonlinear weighted fusion of the initial power allocation weights and the dynamic correction factors is performed to generate the final power allocation coefficients, thereby achieving flexible and efficient power allocation.

[0027] Specifically, in the calculation of the initial power allocation weights, the local measured impedance refers to the impedance value of the connection point between the energy storage power station and the grid, obtained in real time through the sensor network, while the baseline impedance value is a standard value pre-set based on the grid design parameters. The exponential decay compensation factor for communication delay time is a coefficient that is dynamically adjusted based on the communication delay time. Its purpose is to reduce the impact of communication delay on power allocation accuracy. In the calculation of the dynamic correction factor, the real-time available capacity of the energy storage power station refers to the maximum power that the energy storage power station can currently provide, and the power demand deviation refers to the difference between the current power demand and the actual available power. The hyperbolic tangent function is a nonlinear function used to map the input value to a specific range, thereby achieving dynamic adjustment of the power allocation weights. Nonlinear weighted fusion refers to combining the initial weight and the dynamic correction factor according to a certain weight ratio to generate the final power allocation coefficient. This process can be flexibly adjusted according to the operating strategy and objectives of the virtual power plant.

[0028] Preferably, the operation steps in the collaborative decision-making module can be further refined and optimized. For example, when calculating the initial power allocation weight, more parameters can be introduced to consider the equipment characteristics of different energy storage power stations, such as battery type, capacity level, etc., to improve the accuracy of weight allocation. In the process of generating the dynamic correction factor, in addition to the hyperbolic tangent function, other nonlinear functions, such as the Sigmoid function, can also be considered to provide different adjustment curve characteristics. In addition, the weight ratio of the nonlinear weighted fusion can be dynamically adjusted according to the operating status of the virtual power plant. For example, during the peak load period of the power grid, the weight of the dynamic correction factor can be increased to respond more flexibly to real-time power demand changes.

[0029] In some embodiments, the state balancing module includes: The multi-source fusion unit uses the Kalman filter algorithm to fuse the state of charge estimate reported by the battery management system with the real-time power measurement value to generate the corrected state of charge data; The dispersion control unit calculates the state of charge dispersion of all energy storage power stations in the virtual power plant and triggers the cross-station balancing mechanism when the dispersion exceeds the threshold; The dynamic response unit automatically generates the charge and discharge rate adjustment value of each power station according to the dispersion reduction target, so that the discharge rate increase value of the high state of charge power station and the charging rate increase value of the low state of charge power station form a dynamic balance.

[0030] It should be noted that the state balancing module is a key component of this system for achieving dynamic SOC balancing and coordinated power allocation among multiple energy storage plants. This module implements its functions through three core units: the multi-source fusion unit, the dispersion control unit, and the dynamic response unit. The multi-source fusion unit uses a Kalman filter algorithm to fuse the SOC estimates reported by the battery management system with real-time power measurements to generate corrected SOC data that more accurately reflects the actual charge status of each energy storage plant. The dispersion control unit is responsible for calculating the SOC dispersion of all energy storage plants within the virtual power plant. When the dispersion exceeds a set threshold, a cross-site balancing mechanism is triggered to mitigate operational risks caused by significant SOC differences. The dynamic response unit automatically generates charge and discharge rate adjustments for each plant based on the dispersion reduction target, dynamically balancing the discharge rate increase of plants with high SOC and the charge rate increase of plants with low SOC, thereby achieving coordinated operation of the energy storage plants within the virtual power plant.

[0031] Specifically, the Kalman filter algorithm in the multi-source fusion unit is an optimal estimation algorithm based on recursive least squares. It effectively integrates measurement data from different sensors, reducing the impact of measurement noise and uncertainty on the results. In practical applications, the state of charge (SOC) estimate reported by the battery management system is typically calculated based on parameters such as battery voltage, current, and temperature, while the real-time power measurement directly reflects the current power output or input of the energy storage plant. The Kalman filter algorithm can fuse these two types of data to obtain more accurate SOC data. The dispersion control unit refers to the degree of difference in SOC between energy storage plants within the virtual power plant, typically measured by standard deviation or variance. When the dispersion exceeds a set threshold, it indicates that the SOC differences between energy storage plants are too large and balancing is required. The charge and discharge rate adjustment of the dynamic response unit is calculated based on the dispersion reduction target. Its purpose is to achieve SOC balance by adjusting the charge and discharge rates of each plant so that plants with high SOC discharge and plants with low SOC charge.

[0032] Preferably, the operating steps of the state balancing module can be further refined and optimized. For example, in the multi-source fusion unit, in addition to the Kalman filter algorithm, other advanced data fusion algorithms, such as the particle filter algorithm, can also be considered to be introduced to further improve the accuracy of the state of charge estimation. In the discreteness control unit, the discreteness threshold can be dynamically adjusted according to the actual operation requirements of the virtual power plant. For example, when the grid load is light, the discreteness threshold can be appropriately lowered to more strictly control the difference in state of charge between energy storage power stations. In the dynamic response unit, the calculation of the charge and discharge rate adjustment amount can introduce more complex control strategies, such as fuzzy control or adaptive control, to achieve a smoother charging and discharging process and reduce the impact on the grid.

[0033] In some embodiments, the impedance reconstruction module performs the following operations: (1) Real-time monitoring of the voltage harmonic distortion rate at the grid connection point. When a distorted waveform dominated by a specific subharmonic is detected, a compensation strategy of parallel virtual resistance or series virtual reactance is selected; (2) Generate virtual impedance parameters based on the product relationship between the harmonic order and the equivalent impedance of the power grid, where higher harmonics correspond to larger impedance compensation coefficients; (3) By adjusting the harmonic injection component of the pulse width modulation wave, the output impedance frequency characteristics of the energy storage converter are changed.

[0034] It should be noted that the impedance reconstruction module is a key component of this system for optimizing the interaction between the energy storage converter and the grid. This module monitors the harmonic distortion rate of the grid connection point voltage in real time and dynamically adjusts the equivalent output impedance of the energy storage converter based on the grid impedance characteristics to improve grid connection performance. Specifically, when a distorted waveform dominated by a specific subharmonic is detected, the module selects a compensation strategy using either a parallel virtual resistor or a series virtual reactance to offset the harmonic impact. Parallel virtual resistors and series virtual reactance here refer to the simulation of corresponding resistor or reactance components by controlling the output characteristics of the energy storage converter, thereby changing the grid impedance characteristics. Furthermore, virtual impedance parameters are generated based on the product of the harmonic order and the grid equivalent impedance. Higher-order harmonics are associated with larger impedance compensation coefficients, meaning the system provides stronger compensation for higher-order harmonics to better mitigate their impact on the grid. Finally, by adjusting the harmonic injection component of the pulse-width modulated waveform, the frequency characteristics of the energy storage converter's output impedance are altered, thereby dynamically matching the grid impedance characteristics.

[0035] Specifically, the harmonic distortion rate (HDR) in the impedance reconstruction module refers to the ratio of the harmonic components to the fundamental components in the grid voltage, typically expressed as a percentage. When the HDR exceeds a certain threshold, it indicates a serious harmonic problem in the grid. The module then selects an appropriate compensation strategy. For example, if a distorted waveform dominated by the fifth harmonic is detected in the grid, the system may select a parallel virtual resistor to offset the impact of the harmonic current. Virtual impedance parameters are generated based on the product of the harmonic order and the grid's equivalent impedance. This means that for higher harmonics, the system generates larger virtual impedance parameters to compensate. For example, the corresponding virtual impedance parameter for the 10th harmonic may be several times larger than that for the 5th harmonic. The harmonic injection component of the pulse-width modulation wave refers to the frequency characteristics of the energy storage converter's output impedance that are altered by adjusting the harmonic components in the pulse-width modulation signal. This adjustment can be achieved by varying parameters such as the duty cycle and frequency of the pulse-width modulation signal.

[0036] Preferably, the operating steps of the impedance reconstruction module can be further refined and optimized. For example, when selecting a compensation strategy, the ratio of parallel virtual resistance and series virtual reactance can be dynamically adjusted according to the actual operating status of the power grid and the characteristics of the distorted waveform. For complex harmonic environments, a combined compensation strategy can be adopted, using parallel virtual resistance and series virtual reactance at the same time to achieve more effective harmonic suppression. When generating virtual impedance parameters, an adaptive algorithm can be introduced to dynamically adjust the size of the virtual impedance parameters according to the real-time monitored grid impedance characteristics to achieve more accurate impedance matching. In addition, for the adjustment of the harmonic injection component of the pulse width modulated wave, more advanced modulation technologies, such as space vector pulse width modulation (SVPWM), can be used to improve the output accuracy and efficiency of the energy storage converter.

[0037] In some embodiments, the dynamic perception module includes: High-frequency feature unit, which captures transient voltage overshoot and current change rate of power semiconductor devices at microsecond sampling intervals; Thermal coupling unit, which establishes a dynamic correlation model between power device junction temperature fluctuation and heat sink temperature gradient; The fault warning unit generates a device aging warning signal when it detects that the voltage overshoot amplitude exceeds 30% of the rated value and the current change rate exceeds the safety threshold.

[0038] It should be noted that the dynamic sensing module is the core component of this system for real-time monitoring of the energy storage power station's operating status. Its function is to accurately sense key parameters of the energy storage power station through a high-frequency signature unit, a thermal coupling unit, and a fault warning unit. The high-frequency signature unit captures transient voltage overshoots and current change rates of power semiconductor devices at microsecond sampling intervals. The microsecond sampling interval here refers to the extremely high sampling frequency, which enables data acquisition in a very short time, thereby accurately capturing the transient characteristics of power devices during switching. The thermal coupling unit is used to establish a dynamic correlation model between power device junction temperature fluctuations and heat sink temperature gradients. Specifically, by monitoring power device junction temperature changes and heat sink temperature distribution, it analyzes the dynamic relationship between the two to assess the device's thermal operating status. The fault warning unit generates a device aging warning signal when it detects a voltage overshoot exceeding 30% of the rated value or a current change rate exceeding a safety threshold. The rated value refers to the maximum voltage or current value allowed during normal operation of the device, while the safety threshold is a critical value set according to the device's safety operating standards, used to determine whether the device is within the safe operating range.

[0039] Specifically, the microsecond sampling interval of the high-frequency feature unit can be adjusted according to the actual application scenario. For example, under high-precision monitoring requirements, the sampling interval can be set to 1 microsecond or even shorter to ensure that more subtle transient changes can be captured. In the thermal coupling unit, the power device junction temperature fluctuation refers to the temperature change caused by the current change during the operation of the power device, while the heat sink temperature gradient refers to the temperature difference between different positions of the heat sink. By establishing a dynamic correlation model between the two, the thermal operation state of the power device can be more accurately predicted. The safety thresholds for the voltage overshoot amplitude and current change rate in the fault warning unit are set according to the tolerance and safe operation standards of the power device. For example, for a certain type of power device with a rated voltage of 1000V, when a voltage overshoot exceeding 1300V (i.e., exceeding 30% of the rated value) is detected, the system will determine that the device may be in an aging or fault state and generate a warning signal.

[0040] Preferably, the operating steps of the dynamic perception module can be further refined and optimized. For example, in the high-frequency feature unit, a higher-precision sampling device can be used to shorten the sampling interval to the nanosecond level to more accurately capture the transient characteristics of the power semiconductor device. In the thermal coupling unit, a more complex dynamic model can be introduced to consider the thermal conduction and heat dissipation characteristics of the power device under different load conditions, so as to more accurately predict the temperature changes of the equipment. In addition, the fault warning unit can be combined with a machine learning algorithm to automatically adjust the warning threshold according to historical data to improve the accuracy and reliability of the warning. For example, by analyzing historical operating data, the system can automatically identify the voltage and current fluctuation range under normal operating conditions, and dynamically adjust the safety threshold accordingly to meet the needs of different equipment and operating environments.

[0041] In some embodiments, the collaborative decision-making module further includes: The delay compensation unit dynamically adjusts the weight update period according to the round-trip delay of the communication network and its change rate, wherein the update period is extended as the delay increases, and the emergency update mode is enabled when the delay change rate exceeds a threshold; The conflict arbitration unit, when detecting a conflict in the power demands of multiple power stations, prioritizes the power requests of power stations whose state of charge deviates more from the median value, and dynamically adjusts the priority weights based on the health status of the equipment.

[0042] It should be noted that the collaborative decision-making module in this system not only generates cross-site power interaction constraints but also expands its functionality to address communication delays and power demand conflicts. The module's newly added delay compensation unit dynamically adjusts the weight update period based on the round-trip delay of the communication network and its rate of change. Round-trip delay refers to the time it takes for a signal to travel from the transmitter to the receiver and back, while the rate of change refers to the rate at which the delay changes over time. When communication delay increases, the system extends the weight update period to avoid control instability caused by frequent updates. When the rate of change exceeds a set threshold, an emergency update mode is activated to quickly respond to drastic changes in network status. Furthermore, when a conflicting power demand is detected among multiple stations, the conflict arbitration unit prioritizes the power request from the station with the largest deviation from the median state of charge (SOC). The SOC deviation refers to the degree to which the energy storage station's current SOC deviates from its ideal median value. Priority weights are also dynamically adjusted based on the health of the equipment to ensure overall system efficiency and equipment safety.

[0043] Specifically, the round-trip delay in the delay compensation unit can be measured by the network timestamp, and the rate of change is obtained by differential calculation of the continuously measured delay data. For example, a threshold is set at 10ms / s. When the delay change rate is detected to exceed the threshold, the system will consider the network state to be unstable, thereby triggering the emergency update mode. In the conflict arbitration unit, the deviation of the state of charge from the middle value can be determined by calculating the difference between the current state of charge of each energy storage power station and the average state of charge of the virtual power plant. The dynamic adjustment of the priority weight can be set according to the health status parameters of the equipment. For example, a power station with poor equipment health status will be given a higher priority weight to give priority to meeting its power needs, thereby avoiding excessive discharge or overcharging of the equipment.

[0044] Preferably, the operating steps of the collaborative decision-making module can be further refined and optimized. For example, in the delay compensation unit, a more complex delay prediction algorithm can be introduced, such as a delay prediction model based on machine learning, to more accurately predict the changing trend of network delay, so as to adjust the weight update cycle in advance. In the conflict arbitration unit, in addition to considering the deviation of the state of charge from the median value and the health status of the equipment, other factors such as the remaining capacity of the energy storage power station and the urgency of the power demand can be introduced to more comprehensively evaluate the priority. In addition, a dynamic priority weight adjustment mechanism can be set up to dynamically adjust the weight distribution according to the real-time operating status and historical data to achieve more flexible power demand management.

[0045] In some embodiments, the state balancing module implements the following protection strategies: (1) During the state of charge balancing process, when it is detected that the temperature difference of any battery cluster exceeds the safety threshold, the balancing operation is automatically suspended and the forced cooling mode is activated; (2) The power adjustment rate is safely limited. When the adjustment amount exceeds the preset value, a buffer transition process is inserted, and its ramp time is positively correlated with the adjustment amount. (3) The number of historical balancing times is accumulated. When the set threshold is reached, the battery life evaluation is triggered and the evaluation results are fed back to the power allocation weight calculation process.

[0046] It should be noted that the state balancing module in this system not only achieves dynamic state-of-charge balancing and coordinated power margin allocation among multiple energy storage plants, but also introduces protection strategies to ensure the safe operation of the energy storage plants during the balancing process. These protection strategies include: automatically suspending balancing operations and initiating forced cooling mode when a temperature difference in any battery cluster exceeds a safety threshold. The battery cluster temperature difference here refers to the temperature difference between different battery cells within the battery pack, while the safety threshold is the maximum temperature difference set according to the battery's safe operating standards to prevent battery damage due to excessive temperature differences; implementing safety limit control on the power adjustment rate, inserting a buffer transition when the adjustment exceeds a preset value. The power adjustment rate here refers to the speed of power change during the energy storage plant's charging and discharging process. Safety limit control limits the maximum rate of power adjustment to prevent excessive power changes from impacting equipment; and accumulating historical balancing times. When a set threshold is reached, battery life assessment is triggered. The historical balancing times here refer to the cumulative number of balancing operations performed by the energy storage plant during operation. Battery life assessment evaluates the health status and remaining life of the battery based on historical balancing data. The assessment results are fed back into the power allocation weight calculation process to optimize subsequent power allocation strategies.

[0047] Specifically, battery cluster temperature differential monitoring can be achieved by placing multiple temperature sensors within the battery pack. These sensors can measure the temperature of different battery cells in real time and calculate the temperature differential. Safety thresholds can be set based on the battery's material properties, rated operating temperature range, and manufacturer recommendations. For example, for certain lithium-ion battery models, the safety threshold can be set to 10°C. Safety limit control of the power adjustment rate can be achieved by setting a maximum power change rate, for example, limiting the power adjustment rate to no more than 100kW / s. When the power adjustment exceeds a preset value, the system automatically inserts a buffer transition process, whose ramp time can be dynamically adjusted based on the adjustment amount. For example, the larger the adjustment amount, the longer the ramp time. Historical balancing times can be accumulated using an internal counter. When the accumulated number reaches a set threshold, such as 1000 times, the system triggers a battery life assessment. This assessment method may include analyzing battery charge and discharge curves, internal resistance changes, and other parameters.

[0048] Preferably, the operating steps of the state balancing module can be further refined and optimized. For example, in the temperature difference monitoring of the battery cluster, a more advanced temperature sensor network can be introduced, and distributed fiber optic sensors can be used to achieve higher accuracy and more comprehensive temperature monitoring. For the setting of the safety threshold, it can be dynamically adjusted according to the actual operating status and environmental conditions of the battery. For example, the safety threshold can be appropriately lowered in a high temperature environment to enhance the protection effect. In the safety limit control of the power adjustment rate, an adaptive control algorithm can be introduced to dynamically adjust the limit value according to the real-time operating status to achieve more flexible power adjustment. In addition, in the battery life assessment, big data analysis and machine learning algorithms can be combined to more accurately predict the remaining life of the battery based on historical balancing data and operating parameters, and the assessment results can be fed back to the power allocation weight calculation process in real time to optimize the overall operating efficiency of the system.

[0049] In some embodiments, the impedance reconstruction module further includes: The resonance suppression unit compares the amplitude and phase relationship between the energy storage system output impedance and the grid impedance in real time, activating active damping control when the two are close to matching; The phase synchronization unit adopts adaptive phase-locked technology to keep the grid-connected current phase tracking error less than 0.5 degrees within the grid frequency deviation range of 2Hz. Its tracking speed is nonlinearly positively correlated with the frequency deviation value.

[0050] It should be noted that the impedance reconstruction module in this system not only adjusts the equivalent output impedance of the energy storage converter in real time based on the grid impedance characteristics, but also further incorporates a resonance suppression unit and a phase synchronization unit to enhance system stability and grid-connected performance. The resonance suppression unit compares the amplitude and phase relationship between the energy storage system's output impedance and the grid impedance in real time, activating active damping control when the two are nearly matched. The amplitude-phase relationship here refers to the degree of matching between the impedance magnitude and phase angle, while active damping control is a technique that uses a damping mechanism to suppress system resonances. The phase synchronization unit utilizes adaptive phase-locking technology to maintain a grid-connected current phase tracking error of less than 0.5 degrees within a 2Hz grid frequency offset. Adaptive phase-locking technology automatically adjusts phase to achieve synchronization, while phase tracking error refers to the deviation between the grid current phase and the grid voltage phase. Through these functions, the impedance reconstruction module effectively suppresses resonances and ensures that the grid current phase is highly aligned with the grid voltage phase, thereby improving system stability and grid-connected performance.

[0051] Specifically, the amplitude-phase relationship in the resonance suppression unit is determined by real-time monitoring of the amplitude and phase of the energy storage system's output impedance and the grid impedance. When the amplitude and phase of the two are nearly matched, the system determines the risk of resonance and activates active damping control. Active damping control can be implemented by introducing a virtual damping resistor at the output of the energy storage converter, dissipating excess resonant energy and preventing system oscillation. The adaptive phase-locking technology in the phase synchronization unit is implemented using a phase-locked loop (PLL) algorithm. This algorithm automatically adjusts the phase of the grid-connected current based on changes in grid frequency to maintain synchronization with the grid voltage. For example, when the grid frequency deviates by 2 Hz, the PLL rapidly adjusts the phase of the grid-connected current to maintain a tracking error within 0.5 degrees. The tracking speed is nonlinearly positively correlated with the frequency deviation: the larger the frequency deviation, the faster the PLL adjustment speed, ensuring rapid system response.

[0052] Preferably, the operating steps of the impedance reconstruction module can be further refined and optimized. For example, in the resonance suppression unit, a more advanced damping control algorithm, such as fuzzy logic-based damping control, can be introduced to achieve more precise resonance suppression. In the phase synchronization unit, a higher-precision phase-locked loop algorithm, such as a digital phase-locked loop (DPLL), can be used to further improve the accuracy and response speed of phase tracking. In addition, an intelligent monitoring system can be introduced to analyze the impedance characteristics of the power grid and energy storage system in real time and dynamically adjust the parameters of the impedance reconstruction module to achieve more efficient impedance matching and system stability control. For example, by analyzing historical data through a machine learning algorithm, possible resonance risks can be predicted and the damping parameters can be adjusted in advance, thereby improving the system's active defense capabilities.

[0053] In some embodiments, the secure communication module implements: (1) A triple verification mechanism for control instructions, including device hardware fingerprint authentication, operation timing stamp verification, and data hash value integrity comparison; (2) Sharded encrypted storage of key parameters, splitting the running data into multiple data blocks and storing them in distributed nodes using different encryption keys; (3) Dynamic defense against replay attacks, generating real-time changing encryption tokens through XOR operations of serial number, timestamp and key.

[0054] It should be noted that the secure communication module is a key part of this system to ensure the secure transmission of control instructions between modules. Its functions are achieved through a triple verification mechanism for control instructions, sharded encrypted storage of key parameters, and a dynamic defense against replay attacks. The triple verification mechanism for control instructions includes device hardware fingerprint authentication, operation time stamp verification, and data hash value integrity comparison. Device hardware fingerprint authentication refers to identity authentication through the device's unique hardware identifier, operation time stamp verification refers to verifying whether the time sequence of control instructions is logical, and data hash value integrity comparison refers to verifying whether the data has been tampered with through a hash algorithm. Sharded encrypted storage of key parameters refers to splitting the running data into multiple data blocks and storing them in distributed nodes using different encryption keys to prevent data leakage. The dynamic defense against replay attacks generates a real-time changing encryption token through the exclusive OR operation of the serial number, timestamp, and key to prevent attackers from replaying old control instructions.

[0055] Specifically, device hardware fingerprint authentication can be achieved by reading the device's unique identifier (such as MAC address, serial number, etc.) to ensure that only legitimate devices can participate in communication. Operation timing stamp verification requires embedding a timestamp in the control instruction. The system will check whether the time sequence of the instructions is reasonable. For example, the timestamp of the subsequent instruction must be later than the timestamp of the previous instruction. Data hash value integrity comparison verifies whether the data has been tampered with during transmission by calculating the hash value of the data and comparing it with the original hash value. In the key parameter sharding encryption storage, the size of the data block can be set according to actual needs. For example, the data can be split into 128-byte data blocks, each data block is encrypted with a different encryption key, and stored on different distributed nodes. In the dynamic defense replay attack mechanism, the XOR operation of the serial number, timestamp, and key can generate a dynamic encryption token, which will be updated with each communication to ensure the uniqueness of each communication.

[0056] Preferably, the operating steps of the secure communication module can be further refined and optimized. For example, in the device hardware fingerprint authentication, multiple hardware features can be combined for multi-factor authentication to improve the reliability of the authentication. In the operation time stamp verification, a time window mechanism can be introduced to allow timing deviations within a certain range to cope with situations such as network delays. In the data hash value integrity comparison, a more secure hash algorithm, such as SHA-256 or SHA-3, can be used to improve the strength of data integrity verification. In addition, the dynamic defense replay attack mechanism can be combined with asymmetric encryption technology to encrypt and decrypt encrypted tokens using public and private keys to further improve security. For example, the sender uses a private key to sign the token, and the receiver uses the public key to verify it, thereby ensuring the legitimacy and uniqueness of the token.

[0057] In some embodiments, the fault warning unit includes: The life prediction subunit establishes an exponential decay relationship model between the junction temperature fluctuation amplitude and duration of power devices and their remaining life, where the high temperature holding time has a cumulative amplification effect on life decay; The health assessment subunit generates a module replacement recommendation and automatically adjusts the power allocation weight of the energy storage power station when it detects that the conduction characteristics of multiple switching devices in the same power module differ by more than 15%.

[0058] It should be noted that the fault warning unit in this system monitors and assesses the health of power devices in the energy storage power station and provides early warning signals. This functionality is implemented through the life prediction subunit and the health assessment subunit. The life prediction subunit models the exponential decay relationship between the junction temperature fluctuation amplitude and duration of power devices and their remaining life. The junction temperature fluctuation amplitude refers to the temperature variation range caused by current fluctuations during operation, while the remaining life is the expected time the device will continue to function normally under the current operating conditions. This model predicts the device's service life by analyzing the cumulative amplification effect of high-temperature maintenance time on life decay. The health assessment subunit generates a module replacement recommendation and automatically adjusts the power allocation weight of the energy storage power station if it detects conduction characteristic differences exceeding 15% between multiple switching devices in the same power module. This conduction characteristic difference refers to inconsistencies in parameters such as current and voltage when the device is in the on state, which is used to determine whether the device is aged or damaged.

[0059] Specifically, the exponential decay relationship model in the life prediction subunit is a mathematical model based on experience or experimental data, which is used to describe the relationship between the junction temperature fluctuation and the remaining life of the power device. For example, when the junction temperature fluctuation amplitude is large and the duration is long, the remaining life of the device will decay exponentially. The parameters of the model can be adjusted according to the type of device and actual operating data. The difference in conduction characteristics in the health assessment subunit can be evaluated by monitoring parameters such as the on-state voltage and on-state current of the device. For example, when the on-state voltage difference of multiple switching devices exceeds 15%, the system will determine that the module may have a failure risk and generate a module replacement recommendation. At the same time, the system will automatically adjust the power allocation weight of the energy storage power station where it is located, reduce the power allocation ratio of the module, and reduce its workload.

[0060] Preferably, the operating steps of the fault warning unit can be further refined and optimized. For example, in the life prediction subunit, more operating parameters, such as current density, switching frequency, etc., can be introduced to establish a more comprehensive life prediction model. At the same time, the machine learning algorithm can be combined to dynamically adjust the model parameters by analyzing historical operating data and fault cases to improve the accuracy of life prediction. In the health assessment subunit, more refined conduction characteristic difference thresholds can be set, such as setting thresholds according to different device types and operating environments. In addition, a fault diagnosis algorithm can be introduced to analyze the cause of the fault and provide more detailed fault information to operation and maintenance personnel to quickly locate and solve the problem.

[0061] The above-mentioned embodiments of the present invention have the following beneficial effects: the present invention can collect the temperature difference distribution of the battery cluster, the transient characteristics of the power converter switch and the harmonic spectrum data of the grid connection point of the energy storage power station in real time through the multi-dimensional sensor network of the dynamic perception module, and provide data support for the precise control of the system. The collaborative decision-making module can dynamically generate cross-station power interaction constraint rules based on the virtual power plant topology structure, and combine the initial power allocation weights and dynamic correction factors for nonlinear weighted fusion, so as to flexibly adapt to topology changes and real-time power demand fluctuations to achieve efficient power distribution. The impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the grid, which can effectively suppress harmonic distortion, optimize grid connection performance, and improve the stability and compatibility of the system. The state balancing module can achieve dynamic balance of charge state and collaborative distribution of power margin between multiple energy storage power stations through multi-source fusion, discreteness control and dynamic response mechanism, avoid overcharging or over-discharging of some power stations, and extend the service life of the equipment. The secure communication module adopts a layered encryption protocol, a triple verification mechanism for control instructions, sharded encrypted storage of key parameters, and a dynamic defense replay attack mechanism, which can effectively ensure the secure transmission of control instructions between modules, prevent data leakage and instruction tampering, and improve the overall security of the system.

[0062] In addition, the delay compensation unit and conflict arbitration unit in the collaborative decision-making module can dynamically adjust the weight update cycle to prioritize power requests from key power stations, further improving the system's response speed and the rationality of power allocation. During the state-of-charge balancing process, the state balancing module can automatically suspend abnormal operations and activate protection mechanisms, safely limit the power adjustment rate, and accumulate historical balancing times to assess battery life, thereby enhancing system safety and reliability. The resonance suppression unit and phase synchronization unit of the impedance reconstruction module can optimize the matching relationship between the energy storage system's output impedance and the grid impedance in real time, maintaining high-precision tracking of the grid-connected current phase, further improving system stability and grid-connected performance. By monitoring the transient characteristics, junction temperature fluctuations, and conduction characteristic differences of power devices, the fault warning unit can provide early warning of device aging and potential faults, generate module replacement recommendations, and adjust power allocation weights to reduce the risk of equipment damage and improve the operational reliability and maintenance efficiency of the energy storage power station.

[0063] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, server, mobile phone, or tablet.

[0064] The above descriptions merely illustrate some preferred embodiments of the present invention and the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A joint operation and maintenance management system for energy storage power stations for virtual power plants, characterized by: include: The dynamic sensing module collects data on the temperature difference distribution of the battery cluster, transient characteristics of the power converter switches, and harmonic spectrum of the grid connection point in real time through a multi-dimensional sensor network. A collaborative decision-making module, connected to the dynamic perception module, dynamically generates cross-site power interaction constraint rules based on the virtual power plant topology; An impedance reconstruction module, connected to the collaborative decision-making module, adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; A state balancing module, connected to the impedance reconstruction module, to achieve dynamic balancing of state of charge and coordinated allocation of power margin among multiple energy storage power stations; The secure communication module uses a layered encryption protocol to ensure the secure transmission of control instructions between modules.

2. The system according to claim 1, wherein: The collaborative decision-making module performs the following steps: (1) The initial power allocation weight is calculated based on the equivalent impedance ratio of adjacent nodes, where the inverse relationship between the local measured impedance and the reference impedance value is used as the calculation basis, and an exponential decay compensation factor for the communication delay time is introduced; (2) The product of the real-time available capacity of the energy storage power station and the absolute value of the power demand deviation is input into the hyperbolic tangent function to generate a dynamic correction factor; (3) The initial power distribution weight and the dynamic correction factor are nonlinearly weighted and fused to generate the final distribution coefficient for controlling the output power of the energy storage converter.

3. The system according to claim 2, characterized in that The state balancing module includes: The multi-source fusion unit uses the Kalman filter algorithm to fuse the state of charge estimate reported by the battery management system with the real-time power measurement value to generate the corrected state of charge data; The dispersion control unit calculates the state of charge dispersion of all energy storage power stations in the virtual power plant and triggers the cross-station balancing mechanism when the dispersion exceeds the threshold; The dynamic response unit automatically generates the charge and discharge rate adjustment value of each power station according to the dispersion reduction target, so that the discharge rate increase value of the high state of charge power station and the charging rate increase value of the low state of charge power station form a dynamic balance.

4. The system according to claim 3, characterized in that The impedance reconstruction module performs the following operations: (1) Real-time monitoring of the voltage harmonic distortion rate at the grid connection point. When a distorted waveform dominated by a specific subharmonic is detected, a compensation strategy of parallel virtual resistance or series virtual reactance is selected; (2) Generate virtual impedance parameters based on the product relationship between the harmonic order and the equivalent impedance of the power grid, where higher harmonics correspond to larger impedance compensation coefficients; (3) By adjusting the harmonic injection component of the pulse width modulation wave, the output impedance frequency characteristics of the energy storage converter are changed.

5. The system according to claim 1, wherein: The dynamic perception module includes: High-frequency feature unit, which captures transient voltage overshoot and current change rate of power semiconductor devices at microsecond sampling intervals; Thermal coupling unit, which establishes a dynamic correlation model between power device junction temperature fluctuation and heat sink temperature gradient; The fault warning unit generates a device aging warning signal when it detects that the voltage overshoot amplitude exceeds 30% of the rated value and the current change rate exceeds the safety threshold.

6. The system according to claim 2, wherein: The collaborative decision-making module also includes: The delay compensation unit dynamically adjusts the weight update period according to the round-trip delay of the communication network and its change rate, wherein the update period is extended as the delay increases, and the emergency update mode is enabled when the delay change rate exceeds a threshold; The conflict arbitration unit, when detecting a conflict in the power demands of multiple power stations, prioritizes the power requests of power stations whose state of charge deviates more from the median value, and dynamically adjusts the priority weights based on the health status of the equipment.

7. The system according to claim 3, wherein: The state balancing module implements the following protection strategies: (1) During the state of charge balancing process, when it is detected that the temperature difference of any battery cluster exceeds the safety threshold, the balancing operation is automatically suspended and the forced cooling mode is activated; (2) The power adjustment rate is safely limited. When the adjustment amount exceeds the preset value, a buffer transition process is inserted, and its ramp time is positively correlated with the adjustment amount. (3) The number of historical balancing times is accumulated. When the set threshold is reached, the battery life evaluation is triggered and the evaluation results are fed back to the power allocation weight calculation process.

8. The system according to claim 4, wherein: The impedance reconstruction module further includes: The resonance suppression unit compares the amplitude and phase relationship between the energy storage system output impedance and the grid impedance in real time, activating active damping control when the two are close to matching; The phase synchronization unit adopts adaptive phase-locked technology to keep the grid-connected current phase tracking error less than 0.5 degrees within the grid frequency deviation range of 2Hz. Its tracking speed is nonlinearly positively correlated with the frequency deviation value.

9. The system according to claim 1, wherein: The secure communication module implements: (1) A triple verification mechanism for control instructions, including device hardware fingerprint authentication, operation timing stamp verification, and data hash value integrity comparison; (2) Sharded encrypted storage of key parameters, splitting the running data into multiple data blocks and storing them in distributed nodes using different encryption keys; (3) Dynamic defense against replay attacks, generating real-time changing encryption tokens through XOR operations of serial number, timestamp and key.

10. The system according to claim 5, wherein: The fault warning unit includes: The life prediction subunit establishes an exponential decay relationship model between the junction temperature fluctuation amplitude and duration of power devices and their remaining life, where the high temperature holding time has a cumulative amplification effect on life decay; The health assessment subunit generates a module replacement recommendation and automatically adjusts the power allocation weight of the energy storage power station when it detects that the conduction characteristics of multiple switching devices in the same power module differ by more than 15%.

Images