A distributed energy storage collaborative control system and device for a smart power distribution network

Abstract

The application relates to the technical field of power distribution network analysis, in particular to a distributed energy storage collaborative control system and device for an intelligent power distribution network.In the application, a virtual impedance parameter library reflecting a disturbance propagation path is constructed by collecting the inverter phase angle and bus voltage fluctuation characteristics in real time through a dynamic virtual impedance parameter library; a lower-layer regional dynamic division unit of a double-layer decision control module dynamically divides a collaborative control sub-region based on a virtual impedance deviation value and a preset clustering interval, and the local disturbance suppression precision is improved in combination with a power grid topology constraint; a upper-layer weight distribution unit generates a dynamic power distribution weight coefficient according to an inverse proportional relationship of an equivalent impedance ratio, so that the overload risk caused by the mismatch between traditional fixed weight distribution and impedance characteristics is solved; and a power disturbance buffer factor calculation module adjusts the upper limit of the output of the remaining units according to the original weight proportion and the equivalent impedance ratio in an equal ratio scaling mode when the energy storage unit is disconnected from the grid, so that the output adjustment and the impedance attenuation direction are kept synchronous.

Description

Technical Field

[0001] This invention relates to the field of power distribution network analysis technology, specifically to a distributed energy storage collaborative control system and device for smart power distribution networks. Background Technology

[0002] As the core carrier for high-proportion renewable energy integration, smart distribution networks are transforming their operation mode from traditional unidirectional radial power supply to multi-faceted interactive power supply. With the large-scale penetration of fluctuating power sources such as distributed photovoltaic and wind power, distribution networks face challenges such as bidirectional power flow, frequent voltage exceeding limits at nodes, and weakened inertial support capabilities. Especially in extreme weather or load surge scenarios, local disturbances can easily propagate rapidly through power electronic devices, triggering cascading voltage collapses or frequency instability. Existing distributed energy storage collaborative control technologies mostly adopt a centralized architecture, relying on a central controller to collect information from the entire network and issue commands. However, due to communication latency and bandwidth limitations, it is difficult to respond to millisecond-level disturbance events in a timely manner.

[0003] Furthermore, traditional virtual impedance control strategies are typically based on fixed parameters or offline simulation settings, which cannot adapt to the time-varying impedance characteristics caused by dynamic grid topology reconfiguration (such as microgrid on / off-grid switching and random energy storage switching), leading to deterioration in power allocation and disturbance suppression effects. For example, when an energy storage unit fails and exits the grid, centralized control needs to recalculate the entire network power command, resulting in high computational complexity and a high risk of secondary oscillations due to weight redistribution lag. Some literature proposes adaptive virtual impedance adjustment methods based on local measurements, but these do not consider the spatial correlation of disturbance propagation paths, leading to impedance parameter coupling mismatch between adjacent nodes and exacerbating circulating current problems. At the regional coordination level, existing technologies mostly use static clustering algorithms to divide control sub-regions, ignoring the spatiotemporal attenuation characteristics of impedance parameters during disturbance propagation. This results in a mismatch between the coordinated region and the actual disturbance impact range, making it difficult to accurately block disturbance propagation.

[0004] Meanwhile, the power allocation weight is usually a fixed proportion to the energy storage capacity or SOC (state of charge) and is not related to the dynamic changes of the equivalent impedance, resulting in insufficient output of energy storage units on high impedance paths and overload on low impedance paths, which exacerbates power oscillations.

[0005] Therefore, there is an urgent need for a distributed control technology that can track disturbance propagation paths in real time, dynamically divide collaborative regions, and achieve adaptive matching of weights and impedance characteristics, so as to improve the dynamic stability and disturbance resistance of smart distribution networks under complex operating conditions. Summary of the Invention

[0006] The purpose of this invention is to provide a distributed energy storage collaborative control system and device for smart distribution networks, to solve the problems mentioned in the background art. Specific technical problems include how to dynamically divide energy storage collaborative control sub-regions to address the response lag caused by real-time changes in the propagation path of grid disturbances; and how to adaptively adjust the power allocation weight coefficient and output limit to address the insufficient suppression of power oscillations caused by dynamic coupling across multiple regions.

[0007] To achieve the above objectives, one objective of this invention is a distributed energy storage collaborative control system for smart distribution networks, comprising a dynamic virtual impedance parameter library construction module, a two-layer decision control module, and a power disturbance buffer factor calculation module, wherein:

[0008] The dynamic virtual impedance parameter library construction module collects the phase angle and bus voltage fluctuation characteristics of each node inverter in real time, extracts the phase angle change gradient within a set time window to generate a phase correlation matrix, and uses wavelet transform to analyze the voltage fluctuation waveform to generate a grid disturbance response sensitivity index. Virtual impedance parameters are generated through linear combination and normalization processing to construct a dynamic parameter library reflecting the disturbance propagation path. Furthermore, a parameter update channel is established based on the grid topology connection relationship. When a disturbance event is detected, a propagation path model starting from the disturbance source is constructed based on the spatiotemporal attenuation law of the virtual impedance parameters, and the impedance attenuation direction is defined as the path extension direction to achieve real-time tracking of the disturbance propagation path.

[0009] The dynamic virtual impedance parameter library construction module quantifies disturbance sensitivity by integrating phase and voltage characteristics, and accurately locates the disturbance propagation path by combining a spatiotemporal attenuation model, providing real-time data support for dynamic collaborative control.

[0010] In the dual-layer decision control module, the lower-layer region dynamic division unit calculates the deviation value of the virtual impedance parameter, groups the energy storage units according to the preset clustering interval threshold, and excludes out-of-range node groups in combination with grid topology space constraints (such as maximum coupling radius) to form a collaborative control sub-region with physical proximity and similar impedance characteristics. When a load impact event is detected, the dynamic compression mechanism of the clustering interval threshold is triggered to reduce the allowable deviation range, ensure that the impedance deviation in the sub-region meets the preset disturbance suppression tolerance, and improve the accuracy of local disturbance suppression.

[0011] The upper-level weight allocation unit in the two-layer decision control module performs hierarchical sorting and weighting of the virtual impedance parameters of the energy storage units in the collaborative control sub-region along the disturbance propagation path to generate an equivalent impedance ratio. Based on the inverse proportional relationship between the equivalent impedance ratio and power demand, the weight coefficients are dynamically allocated. By monitoring the rate of change of the state of charge of the energy storage units, the slope parameter of the inverse proportional function is dynamically adjusted so that the weight coefficients are adaptively matched with the disturbance intensity, avoiding the risk of overcharging or over-discharging.

[0012] The dual-layer decision control module blocks disturbance propagation by dynamically dividing regions, decouples power allocation from disturbance propagation paths based on impedance hierarchy weighting, and enhances system dynamic stability through state-of-charge linkage adjustment.

[0013] After the power disturbance buffer factor calculation module detects that the energy storage unit has gone offline, it constructs an impedance-output mapping function based on its original weight ratio and current equivalent impedance ratio, and adjusts the upper limit of the output of the remaining units according to the principle of proportional scaling; it associates the impedance attenuation direction data of the dynamic virtual impedance parameter library to ensure that the output adjustment is synchronized with the change of impedance characteristics; at the same time, it maintains the inverse proportional relationship between the weight coefficient and the equivalent impedance ratio so that the total output capacity of the sub-region is always dynamically matched with the disturbance intensity.

[0014] The power disturbance buffer factor calculation module realizes the smooth transfer of power deficit in the off-grid scenario through impedance and output mapping function. Combined with impedance attenuation direction data, it maintains the consistency between output adjustment and disturbance propagation path to prevent secondary oscillation.

[0015] The second objective of this invention is to provide a device for a distributed energy storage collaborative control system in a smart distribution network, characterized in that it comprises a high-frequency synchronous data acquisition unit, a data processing core, a communication interface module, and an energy storage control unit, wherein:

[0016] High-frequency synchronous data acquisition units are deployed at each distribution network node to capture the instantaneous change in the inverter output phase angle and the bus voltage fluctuation waveform in real time, and transmit them to the data processing core.

[0017] The data processing core has a built-in virtual impedance parameter calculation engine, which performs phase correlation matrix construction, power grid disturbance response sensitivity index analysis and dynamic virtual impedance parameter library generation algorithms. It also integrates an adaptive clustering algorithm and an inverse proportional weight allocation function for the dynamic division of sub-regions and power allocation weight decision-making in coordination control.

[0018] The communication interface module supports real-time data interaction between distribution network nodes, regional control centers and energy storage unit clusters, and is used to realize the synchronous transmission of virtual impedance parameter updates, state of charge feedback and control commands.

[0019] The energy storage control unit is embedded in each energy storage device and includes a battery pack status monitoring module, an output limit adjustment module and a fault response logic unit, which are used to receive coordinated control commands and perform power compensation operations.

[0020] The distributed energy storage collaborative control system for smart distribution networks uses an edge computing and cloud collaborative architecture to store dynamic virtual impedance parameter libraries and grid topology data in a distributed memory. Combined with the real-time computing power of the processor, it is used to achieve dynamic matching between the output of the energy storage unit and the impedance characteristics of the grid, as well as to suppress the risk of secondary overload.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] By accurately capturing disturbance propagation paths through a dynamic virtual impedance parameter library and combining it with a two-layer decision-making mechanism to achieve dynamic regional division and adaptive weight allocation, the response speed and power oscillation suppression capabilities are significantly improved. Load impact-triggered clustering threshold compression enhances the accuracy of local disturbance suppression; weight coefficients are adjusted in conjunction with the state of charge to avoid overcharging / discharging risks; and the output upper limit is scaled proportionally in off-grid scenarios to ensure system robustness. Ultimately, rapid and coordinated control of distributed energy storage is achieved under multi-disturbance scenarios, effectively improving the dynamic stability of the smart distribution network and the renewable energy absorption capacity. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall modules of the present invention;

[0024] Figure 2 This is a schematic diagram of the overall module unit of the present invention.

[0025] In the diagram: 100, Dynamic Virtual Impedance Parameter Library Construction Module; 200, Two-Layer Decision Control Module; 201, Lower-Layer Dynamic Region Division Unit; 202, Upper-Layer Weight Allocation Unit; 300, Power Disturbance Buffer Factor Calculation Module. Detailed Implementation

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Next, please refer to Figure 1 One of the objectives of this embodiment is to provide a distributed energy storage collaborative control system for smart distribution networks, including a dynamic virtual impedance parameter library construction module 100, a two-layer decision control module 200, and a power disturbance buffer factor calculation module 300.

[0028] The dynamic virtual impedance parameter library construction module 100 deploys a high-frequency synchronous acquisition unit at each distribution network node to capture the instantaneous change in the inverter output phase angle and the bus voltage fluctuation waveform in real time. For the phase angle data, it extracts the phase angle change gradient within a set time window and generates a multi-dimensional matrix characterizing the phase correlation characteristics between nodes through sliding window differential calculation, which serves as the phase correlation matrix. For the bus voltage fluctuation characteristics, it uses wavelet transform to analyze the time-frequency distribution characteristics of the fluctuation waveform, calculates the energy concentration of each node in different frequency bands, and generates a grid disturbance response sensitivity index.

[0029] The phase correlation matrix and the power grid disturbance response sensitivity index are linearly combined and then normalized to generate virtual impedance parameters.

[0030] Based on the power grid topology, a parameter update channel is established. When a disturbance event is detected, a propagation path model starting from the disturbance source is constructed according to the spatiotemporal variation law of the virtual impedance parameter. The propagation path model extends along the attenuation direction of the virtual impedance parameter. By integrating the parameter change rate in the time dimension with the topology in the spatial dimension, a dynamic virtual impedance parameter library reflecting the propagation path of the power grid disturbance is established. The impedance characteristics of the disturbance propagation path are mapped in real time, providing a spatiotemporally correlated impedance data basis for the division of collaborative control sub-regions.

[0031] The dynamic virtual impedance parameter library construction module 100 collects the phase angle and bus voltage fluctuation characteristics of inverters at each node in real time, extracts the phase angle change gradient to generate a phase correlation matrix, and combines wavelet transform to analyze the voltage fluctuation waveform to generate a grid disturbance response sensitivity index. Virtual impedance parameters are generated through linear combination and normalization. A parameter update channel is established based on the grid topology connection relationship. When a disturbance event is triggered, a propagation path model starting from the disturbance source is constructed based on the spatiotemporal attenuation law of the virtual impedance parameters, clearly defining the impedance attenuation direction as the path extension direction. This solves the problem that traditional methods cannot dynamically track the disturbance propagation path. By fusing phase and voltage characteristics to quantify disturbance sensitivity and combining the spatiotemporal attenuation model, the disturbance propagation path is accurately located, providing real-time data support for dynamic coordinated control.

[0032] Please see Figure 2 The lower-level dynamic region partitioning unit 201 in the two-layer decision control module 200 traverses the virtual impedance parameters of all energy storage units and calculates the deviation between the virtual impedance parameters of each energy storage unit and the current average impedance value of the power grid. The energy storage unit is an independent energy storage device composed of a battery pack, a bidirectional converter, and a control unit. An adaptive clustering algorithm is used to group the deviation values ​​according to a preset clustering interval threshold. Energy storage units with deviation values ​​in the same interval are automatically clustered into a collaborative control sub-region. The time series similarity of the virtual impedance parameters is analyzed by a sliding time window. Combined with the spatial constraints of the power grid topology, node groups whose physical distance exceeds the maximum coupling radius are dynamically excluded, and finally, an energy storage unit cluster with consistent impedance response characteristics is formed.

[0033] When a load surge event is detected, dynamic compression of the clustering interval threshold is triggered in real time. By narrowing the preset clustering interval threshold range, the virtual impedance deviation value of the energy storage unit in the collaborative control sub-region is forcibly adjusted. This mechanism is based on the real-time intensity and propagation characteristics of the grid disturbance event and dynamically constrains the boundary conditions of the clustering group. This ensures that the virtual impedance parameter deviation of all energy storage units in the collaborative control sub-region is always compressed within the preset disturbance suppression tolerance range, thereby maintaining the impedance response consistency of the collaborative control sub-region under disturbance events and avoiding the risk of overload or control failure due to impedance mismatch.

[0034] The lower-level dynamic partitioning unit 201 dynamically partitions the collaborative control sub-regions by calculating the deviation value of the virtual impedance parameters and combining the preset clustering interval threshold and power grid topology space constraints (such as the maximum coupling radius). It also triggers a dynamic compression mechanism for the clustering threshold during load impact events to reduce the allowable deviation range. This solves the problem of mismatch between static region partitioning and the actual disturbance impact range. By using a grouping strategy based on physical proximity and similar impedance characteristics, it improves the accuracy of local disturbance suppression.

[0035] The upper-level weight allocation unit 202 in the two-layer decision control module 200 performs topological sorting and weighting on the virtual impedance parameters of all energy storage units in each collaborative control sub-region. Units with deeper levels along the disturbance propagation path are assigned higher weighting coefficients. The equivalent impedance ratio of the sub-region is generated by weighted accumulation. Subsequently, based on the nonlinear relationship between the equivalent impedance ratio and the power allocation demand, an inverse proportional weight allocation function is constructed so that the sub-region with a larger equivalent impedance ratio receives a smaller power allocation weight coefficient, thereby reducing the power output burden of energy storage units on high impedance paths.

[0036] By continuously monitoring the rate of change of state of charge (SOC) of each energy storage unit within the collaborative control sub-region after power allocation, the dynamic fluctuations of grid disturbance intensity are inverted in real time. Based on the relationship between the SOC and a preset threshold, the slope parameter of the inverse proportional weight allocation function is dynamically adjusted so that the inverse proportional relationship between the equivalent impedance ratio and the power allocation weight coefficient adapts to the disturbance intensity. This correction mechanism ensures through closed-loop feedback that the power allocation weight coefficient is synchronously matched with the actual propagation rate and energy impact amplitude of grid disturbance events, avoiding the risk of secondary overload caused by the mismatch between the output of energy storage units and the impedance characteristics of the grid due to the lag in weight allocation.

[0037] The upper-level weight allocation unit 202 dynamically allocates power weight coefficients based on the inverse proportional relationship of the equivalent impedance ratio. It adjusts the slope parameter of the inverse proportional function by monitoring the rate of change of the state of charge of the energy storage unit. This solves the problem of mismatch between fixed weight allocation and dynamic impedance characteristics, decouples power allocation from disturbance propagation path, and avoids the risk of overcharging or over-discharging through linkage adjustment of the state of charge.

[0038] When the power disturbance buffer factor calculation module 300 detects that an energy storage unit has exited operation within the cooperative control sub-region, it retrieves historical data of the original power allocation weight ratio of the off-grid energy storage unit before its exit from the dynamic virtual impedance parameter library, and obtains the updated equivalent impedance ratio of the current sub-region. Based on the principle of proportional scaling, the original weight ratio of the off-grid energy storage unit (i.e., the energy storage unit that has exited operation due to overload or fault) is redistributed according to the equivalent impedance ratio of the remaining energy storage units (i.e., the energy storage units that continue to operate within the current cooperative control sub-region). Specifically, by constructing an impedance-output mapping function, the new output upper limit of the remaining energy storage units satisfies the product conservation relationship between the original weight ratio of the off-grid energy storage unit and the current equivalent impedance ratio.

[0039] During this process, the impedance attenuation direction data of the dynamic virtual impedance parameter library is linked in real time to ensure that the adjustment of the upper limit of the output of the remaining energy storage units is synchronized with the change of impedance characteristics of the disturbance propagation path. Finally, the upper limit compensation of the output of each remaining energy storage unit is generated, so that the total output capacity of the collaborative control sub-region is dynamically matched with the grid disturbance intensity, while maintaining the inverse proportional relationship between the power allocation weight coefficient and the equivalent impedance ratio.

[0040] When an energy storage unit is disconnected from the grid, the power disturbance buffer factor calculation module 300 constructs an impedance-output mapping function based on its original weight ratio and current equivalent impedance ratio. The module adjusts the upper limit of the output of the remaining units according to the principle of proportional scaling and correlates the impedance attenuation direction data to adjust the output characteristics synchronously, maintaining the inverse proportional relationship between the weight coefficient and the equivalent impedance ratio. This solves the problem of secondary oscillation caused by the imbalance of power deficit distribution in the disconnection scenario. By matching the output adjustment with the propagation path consistency, it ensures that the total output capacity and the disturbance intensity are dynamically adapted.

[0041] The second objective of this embodiment is to provide a device for a distributed energy storage collaborative control system for smart distribution networks. This device is deployed at distribution network nodes and regional control centers, and includes a high-frequency synchronous data acquisition unit, a data processing core, a communication interface module, and an energy storage control unit; wherein:

[0042] The high-frequency synchronous data acquisition unit is installed at each distribution network node to capture the instantaneous change in the inverter output phase angle and the bus voltage fluctuation waveform in real time, and transmit them to the data processing core.

[0043] The data processing core has a built-in virtual impedance parameter calculation engine, which performs normalization processing and disturbance propagation path modeling based on phase correlation matrix construction, power grid disturbance response sensitivity analysis and dynamic parameter library generation algorithms. It also integrates an adaptive clustering algorithm and an inverse proportional weight allocation function for the dynamic division of sub-regions and power allocation weight decision-making in coordination control.

[0044] The communication interface module supports real-time data interaction between multiple nodes and connects to the energy storage unit cluster through wired or wireless communication networks to ensure the synchronous transmission of virtual impedance parameter updates, state of charge feedback, and control commands.

[0045] The energy storage control unit is embedded in each energy storage device and includes battery pack status monitoring, output limit adjustment and fault response logic. It is used to receive coordinated control commands and perform power compensation operations.

[0046] A device for a distributed energy storage collaborative control system for smart distribution networks uses an edge computing and cloud collaborative architecture to store a dynamic virtual impedance parameter library and grid topology data in a distributed memory. Combined with the real-time computing power of the processor, it can achieve dynamic matching between the output of the energy storage unit and the impedance characteristics of the grid under disturbance events and suppress the risk of secondary overload.

[0047] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A distributed energy storage collaborative control system for smart distribution networks, characterized in that, It includes a dynamic virtual impedance parameter library construction module (100), a two-layer decision control module (200), and a power disturbance buffer factor calculation module (300), wherein: The dynamic virtual impedance parameter library construction module (100) is used to collect the inverter output phase angle and bus voltage fluctuation characteristics of each distribution network node in real time, and establish a dynamic virtual impedance parameter library that reflects the propagation path of power grid disturbances. The two-layer decision control module (200) includes a lower-layer region dynamic partitioning unit (201) and an upper-layer weight allocation unit (202), wherein: The lower region dynamic partitioning unit (201) calculates the deviation value of the virtual impedance parameter in the dynamic virtual impedance parameter library, and divides the energy storage unit whose virtual impedance parameter deviation value is within the preset clustering interval into a collaborative control sub-region. The upper-level weight allocation unit (202) generates a power allocation weight coefficient that is inversely proportional to the equivalent impedance ratio based on the equivalent impedance ratio of the cooperative control sub-region. When the power disturbance buffer factor calculation module (300) detects that an energy storage unit in the cooperative control sub-region has exited operation, it treats the energy storage unit as an off-grid energy storage unit and adjusts the upper limit of the output of the remaining energy storage units according to the proportion of the off-grid energy storage unit in the original weight allocation and the equivalent impedance ratio of the current cooperative control sub-region, according to the principle of proportional scaling.

2. The distributed energy storage collaborative control system for smart distribution networks according to claim 1, characterized in that, The dynamic virtual impedance parameter library construction module (100) extracts the phase angle change gradient within a set time window and generates a phase correlation matrix, and generates a power grid disturbance response sensitivity index by analyzing the bus voltage fluctuation waveform through wavelet transform. The phase correlation matrix and the power grid disturbance response sensitivity index are linearly combined and calculated, and virtual impedance parameters are generated through normalization.

3. The distributed energy storage collaborative control system for smart distribution networks according to claim 1, characterized in that, The dynamic virtual impedance parameter library construction module (100) establishes a parameter update channel based on the power grid topology connection relationship. When a disturbance event is detected, it constructs a propagation path model starting from the disturbance source according to the spatiotemporal change law of the virtual impedance parameter, and establishes a dynamic virtual impedance parameter library that reflects the propagation path of the power grid disturbance. The propagation path model extends in the attenuation direction of the virtual impedance parameter.

4. The distributed energy storage collaborative control system for smart distribution networks according to claim 1, characterized in that, The lower-level region dynamic partitioning unit (201) calculates the deviation value of the virtual impedance parameters in the dynamic virtual impedance parameter library and groups them according to the preset clustering interval threshold to form a collaborative control sub-region. At the same time, it excludes node groups whose physical distance exceeds the maximum coupling radius by combining the spatial constraints of the power grid topology.

5. The distributed energy storage collaborative control system for smart distribution networks according to claim 4, characterized in that, The dynamic compression mechanism of the clustering interval threshold is triggered when a load impact event is detected, ensuring that the deviation of the virtual impedance parameter of the energy storage unit in the collaborative control sub-region meets the preset disturbance suppression tolerance range.

6. The distributed energy storage collaborative control system for smart distribution networks according to claim 1, characterized in that, The upper-level weight allocation unit (202) performs hierarchical sorting and weighting of the virtual impedance parameters of the energy storage units in the collaborative control sub-region along the disturbance propagation path direction to generate the equivalent impedance ratio, and generates the power allocation weight coefficient based on the inverse proportional relationship between the equivalent impedance ratio and the power allocation requirement.

7. The distributed energy storage collaborative control system for smart distribution networks according to claim 6, characterized in that, The upper-level weight allocation unit (202) monitors the rate of change of the state of charge of the energy storage unit after power allocation and dynamically adjusts the slope parameter of the inverse proportional function so that the power allocation weight coefficient keeps dynamically matched with the grid disturbance intensity.

8. The distributed energy storage collaborative control system for smart distribution networks according to claim 1, characterized in that, The power disturbance buffer factor calculation module (300) adjusts the upper limit of the output of the remaining energy storage units by constructing an impedance-output mapping function based on the original weight ratio of the off-grid energy storage units and the equivalent impedance ratio of the current collaborative control sub-region, and realizes the synchronization of the adjustment amount and the change of impedance characteristics by associating the impedance attenuation direction data of the dynamic virtual impedance parameter library.

9. The distributed energy storage collaborative control system for smart distribution networks according to claim 8, characterized in that, The power disturbance buffer factor calculation module (300) ensures dynamic matching between the total output capacity of the coordinated control sub-region and the grid disturbance intensity by maintaining the inverse proportional relationship between the power allocation weight coefficient and the equivalent impedance ratio.

10. An apparatus using a distributed energy storage collaborative control system for smart distribution networks comprising any one of claims 1-9, characterized in that, It includes a high-frequency synchronous data acquisition unit, a data processing core, a communication interface module, and an energy storage control unit, wherein: The high-frequency synchronous data acquisition unit is deployed at each distribution network node to capture the instantaneous change in the inverter output phase angle and the bus voltage fluctuation waveform in real time, and transmit them to the data processing core. The data processing core has a built-in virtual impedance parameter calculation engine, which executes algorithms for phase correlation matrix construction, power grid disturbance response sensitivity index analysis and dynamic virtual impedance parameter library generation. It also integrates an adaptive clustering algorithm and an inverse proportional weight allocation function for collaborative control of dynamic division of sub-regions and power allocation weight decision-making. The communication interface module supports real-time data interaction between distribution network nodes, regional control centers and energy storage unit clusters, and is used to realize the synchronous transmission of virtual impedance parameter updates, state of charge feedback and control commands. The energy storage control unit is embedded in each energy storage device and includes a battery pack status monitoring module, an output upper limit adjustment module and a fault response logic unit, which is used to receive cooperative control commands and perform power compensation operations. The device for the distributed energy storage collaborative control system for smart distribution networks uses an edge computing and cloud collaborative architecture to store the dynamic virtual impedance parameter library and grid topology data in a distributed memory. Combined with the real-time computing power of the processor, it is used to achieve dynamic matching between the output of the energy storage unit and the impedance characteristics of the grid, as well as to suppress the risk of secondary overload.

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