Distributed energy storage cooperative control system and device for intelligent power distribution network

Abstract

The invention relates to the technical field of power distribution network analysis, in particular to a distributed energy storage cooperative control system and device for an intelligent power distribution network, in the distributed energy storage cooperative control system and device for the intelligent power distribution network, inverter phase angles and bus voltage fluctuation characteristics are collected in real time through a dynamic virtual impedance parameter library, and a virtual impedance parameter library reflecting a disturbance propagation path is constructed; a lower-layer region dynamic division unit of the double-layer decision control module dynamically divides cooperative control sub-regions based on a virtual impedance deviation value and a preset clustering interval, and improves local disturbance suppression precision in combination with power grid topology constraints, and an upper-layer weight distribution unit generates a dynamic power distribution weight coefficient according to an inverse proportion relation of an equivalent impedance ratio; the overload risk caused by traditional fixed weight distribution and impedance characteristic mismatch is solved; when the energy storage unit is off-network, the power disturbance buffer factor calculation module adjusts the upper limit of output of the remaining units according to the original weight proportion and the equivalent impedance ratio in an equal-ratio scaling mode, and output adjustment and the impedance attenuation direction are maintained to be synchronous.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network analysis, and in particular to a distributed energy storage collaborative control system and device for a smart distribution network. Background Art

[0002] As the core carrier for accessing a high proportion of renewable energy, the operating mode of the smart distribution network is transforming from traditional one-way, radial power supply to a diversified, interactive one. With the large-scale penetration of fluctuating power sources such as distributed photovoltaic and wind power, the distribution network faces challenges such as bidirectional power flow, frequent node voltage overshoots, and weakened inertial support capabilities. Especially in extreme weather or sudden load changes, local disturbances can easily propagate rapidly through power electronic equipment, triggering cascading voltage collapse or frequency instability. Existing distributed energy storage collaborative control technologies mostly adopt a centralized architecture, relying on a central controller to collect network-wide information and issue commands. However, due to communication delays and bandwidth limitations, it is difficult to respond to millisecond-level disturbance events in a timely manner.

[0003] In addition, traditional virtual impedance control strategies are usually based on fixed parameters or offline simulation settings, and cannot adapt to the time-varying impedance characteristics caused by dynamic reconstruction of the power grid topology (such as microgrid on-grid switching, random energy storage switching), resulting in deterioration of power distribution and disturbance suppression. For example, when a certain energy storage unit exits due to a fault, the centralized control needs to recalculate the power instruction of the entire network, which has high computational complexity and is prone to secondary oscillations due to the lag in weight redistribution. Some literature proposes an adaptive virtual impedance adjustment method based on local measurement, but does not consider the spatial correlation of the disturbance propagation path, resulting in coupling mismatch of impedance parameters of adjacent nodes, exacerbating the circulation problem. At the regional coordination level, existing technologies mostly use static clustering algorithms to divide the control sub-areas, ignoring the spatiotemporal attenuation characteristics of the impedance parameters during the disturbance propagation process, resulting in a mismatch between the coordination area and the actual disturbance influence range, making it difficult to accurately block the spread of the disturbance.

[0004] At the same time, the power allocation weight is usually in a fixed proportion to the energy storage capacity or SOC (state of charge), and is not associated with the dynamic changes of the equivalent impedance, resulting in insufficient output of the energy storage unit on the high-impedance path and overload of the low-impedance path, exacerbating power oscillations.

[0005] Therefore, there is an urgent need for a distributed control technology that can track the disturbance propagation path in real time, dynamically divide the cooperative area, and achieve adaptive matching of weights and impedance characteristics, so as to improve the dynamic stability and anti-disturbance capability of the smart distribution network under complex working conditions. Summary of the Invention

[0006] The present invention aims to provide a distributed energy storage collaborative control system and device for smart distribution networks to address the problems raised in the aforementioned background technology. Specific technical issues 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 upper limit to address the problem of insufficient power oscillation suppression caused by dynamic coupling of multiple regions.

[0007] To achieve the above objectives, one of the objectives of the present invention is to provide a distributed energy storage collaborative control system for a smart distribution network, 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 of each node inverter and bus voltage fluctuation characteristics 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 the grid disturbance response sensitivity index. It generates virtual impedance parameters through linear combination and normalization processing to build a dynamic parameter library reflecting the disturbance propagation path. Furthermore, it establishes a parameter update channel based on the grid topology connection relationship. When a disturbance event is detected, it constructs a propagation path model with the disturbance source as the starting point according to the spatiotemporal attenuation law of the virtual impedance parameters. The impedance attenuation direction is clearly defined as the path extension direction, realizing 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 the time-space attenuation model, providing real-time data support for dynamic collaborative control.

[0010] The lower-level dynamic area division unit in the two-layer decision-making and control module calculates the deviation value of the virtual impedance parameter, groups the energy storage units according to the preset clustering interval threshold, and eliminates out-of-range node groups in combination with the grid topology space constraints (such as the maximum coupling radius) to form physically adjacent collaborative control sub-areas with similar impedance characteristics. When a load impact event is detected, the dynamic compression mechanism of the clustering interval threshold is triggered to narrow the allowable deviation range, ensure that the impedance deviation in the sub-area 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-making control module hierarchically sorts and weights the virtual impedance parameters of the energy storage units within the coordinated control sub-area along the disturbance propagation path to generate an equivalent impedance ratio. The weight coefficient is dynamically assigned based on the inverse proportional relationship between the equivalent impedance ratio and power demand. By monitoring the rate of change of the energy storage unit's state of charge, the slope parameter of the inverse proportional function is dynamically adjusted to ensure that the weight coefficient adaptively matches the disturbance intensity, avoiding the risk of overcharging or over-discharging.

[0012] The two-layer decision-making control module blocks the spread of disturbances by dynamically dividing areas, decouples power distribution from disturbance propagation paths based on impedance level weighting, and adjusts the state of charge in a coordinated manner to enhance the dynamic stability of the system.

[0013] After detecting that an energy storage unit is offline, the power disturbance buffer factor calculation module constructs an impedance-output mapping function based on its original weight ratio and current equivalent impedance ratio, adjusting the output upper limit of the remaining units according to the geometric scaling principle. It also associates the impedance attenuation direction data from the dynamic virtual impedance parameter library to ensure that the output adjustment is synchronized with the impedance characteristic changes. At the same time, it maintains an inverse proportional relationship between the weight coefficient and the equivalent impedance ratio, ensuring that the total output capacity of the sub-region is always dynamically matched to the disturbance intensity.

[0014] The power disturbance buffer factor calculation module realizes the smooth transfer of power shortage in the off-grid scenario through impedance and output mapping function mapping. It combines the impedance attenuation direction data to maintain the consistency of output adjustment and disturbance propagation path to prevent secondary oscillation.

[0015] A second object of the present invention is to provide a device for a distributed energy storage coordinated control system for a smart distribution network, 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:

[0016] High-frequency synchronous data acquisition units are deployed at each distribution network node to capture the instantaneous changes in the inverter output phase angle and 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 that performs phase correlation matrix construction, 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 dynamic division of collaborative control sub-areas and power allocation weight decision-making.

[0018] The communication interface module supports real-time data exchange between distribution network nodes, regional control centers, and energy storage unit clusters, enabling the simultaneous transmission of virtual impedance parameter updates, state of charge feedback, and control instructions.

[0019] 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. It is used to receive coordinated control instructions and perform power compensation operations.

[0020] The device of the distributed energy storage collaborative control system for smart distribution networks uses edge computing and cloud collaborative architecture to store the dynamic virtual impedance parameter library and grid topology data in distributed memory, and combines it with the real-time computing capability of the processor to achieve dynamic matching of the energy storage unit output and grid impedance characteristics and suppress secondary overload risks.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] A dynamic virtual impedance parameter library accurately captures disturbance propagation paths, and a two-tier decision-making mechanism enables dynamic regional division and adaptive weight allocation, significantly improving response speed and power oscillation suppression capabilities. Load shocks trigger clustering threshold compression, enhancing local disturbance suppression accuracy. Weight coefficients are adjusted in conjunction with the state of charge to avoid overcharge and discharge risks. Output caps are scaled proportionally in off-grid scenarios to ensure system robustness. Ultimately, this enables rapid coordinated control of distributed energy storage in multiple disturbance scenarios, effectively improving the dynamic stability and renewable energy absorption capacity of smart distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the overall module of the present invention;

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

[0025] In the figure: 100, dynamic virtual impedance parameter library construction module; 200, two-layer decision control module; 201, lower layer area dynamic division unit; 202, upper layer weight allocation unit; 300, power disturbance buffer factor calculation module. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] Next, see Figure 1 One of the purposes of this embodiment is to provide a distributed energy storage collaborative control system for a smart distribution network, 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, the phase angle change gradient within a set time window is extracted, and a multidimensional matrix representing the phase correlation characteristics between nodes is generated through sliding window difference calculation as the phase correlation matrix. For the bus voltage fluctuation characteristics, the wavelet transform is used to analyze the time-frequency distribution characteristics of the fluctuation waveform, calculate the energy concentration of each node in different frequency bands, and generate the grid disturbance response sensitivity index.

[0029] Performing a linear combination operation on the phase correlation matrix and the grid disturbance response sensitivity index, and generating virtual impedance parameters through normalization processing;

[0030] A parameter update channel is established based on the topological connection relationship of the power grid. When a disturbance event is detected, a propagation path model with the disturbance source as the starting point is constructed according to the spatiotemporal variation law of the virtual impedance parameters. The propagation path model extends in the attenuation direction of the virtual impedance parameters. By integrating the parameter change rate in the time dimension and the topological connection relationship 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 spatiotemporal-correlated impedance data foundation for the division of collaborative control sub-areas.

[0031] The dynamic virtual impedance parameter library construction module 100 collects the phase angle of each node inverter and the bus voltage fluctuation characteristics 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 the grid disturbance response sensitivity index, and generates virtual impedance parameters through linear combination and normalization processing; establishes a parameter update channel based on the grid topology connection relationship, and when a disturbance event is triggered, constructs a propagation path model with the disturbance source as the starting point according to the spatiotemporal attenuation law of the virtual impedance parameters, and clarifies the impedance attenuation direction as the path extension direction; solves the problem that traditional methods cannot dynamically track the disturbance propagation path, quantifies the disturbance sensitivity by fusing phase and voltage characteristics, and accurately locates the disturbance propagation path by combining the spatiotemporal attenuation model, providing real-time data support for dynamic collaborative control.

[0032] See also Figure 2 The lower-layer dynamic region division 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, where the energy storage unit is an independent energy storage device consisting 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, and energy storage units with deviation values ​​in the same interval are automatically clustered into cooperative control sub-regions. The time series similarity of the virtual impedance parameters is analyzed through a sliding time window. In combination with the spatial constraints of the power grid topology, node groups whose physical distance exceeds the maximum coupling radius are dynamically excluded, ultimately forming a cluster of energy storage units with consistent impedance response characteristics.

[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 units in the coordinated control sub-area 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 cluster grouping, so that the virtual impedance parameter deviation of all energy storage units in the coordinated control sub-area is always compressed within the preset disturbance suppression tolerance range, thereby maintaining the impedance response consistency of the coordinated control sub-area under disturbance events and avoiding the risk of overload or control failure due to impedance mismatch.

[0034] The lower-level area dynamic division unit 201 calculates the deviation value of the virtual impedance parameter, combines the preset clustering interval threshold and the grid topology space constraint conditions (such as the maximum coupling radius), and dynamically divides the cooperative control sub-area. It triggers the dynamic compression mechanism of the clustering threshold in the event of a load shock to narrow the allowable deviation range; solves the problem of mismatch between static area division and the actual disturbance impact range, and improves the accuracy of local disturbance suppression through a grouping strategy with physical proximity and similar impedance characteristics.

[0035] The upper-level weight allocation unit 202 in the two-layer decision-making control module 200 performs topological sorting and weighting on the virtual impedance parameters of all energy storage units within each coordinated control sub-region. Units with deeper hierarchies along the disturbance propagation path are assigned higher weight coefficients, and the equivalent impedance ratio of the sub-region is generated through weighted accumulation. Subsequently, based on the nonlinear relationship between the equivalent impedance ratio and the power allocation demand, an inversely proportional weight allocation function is constructed. Sub-regions with larger equivalent impedance ratios receive smaller power allocation weight coefficients, thereby reducing the output burden of energy storage units on high-impedance paths.

[0036] By continuously monitoring the state-of-charge change rate of each energy storage unit in the coordinated control sub-area after power allocation, the dynamic fluctuations of the grid disturbance intensity are inverted in real time. Based on the relationship between the state-of-charge change rate and the 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 changes adaptively with the disturbance intensity. This correction mechanism ensures that the power allocation weight coefficient is synchronized with the actual propagation rate and energy impact amplitude of the grid disturbance event through closed-loop feedback, avoiding the risk of secondary overload caused by the mismatch between the energy storage unit output and the grid impedance characteristics due to the lag in weight allocation.

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

[0038] When detecting that an energy storage unit has exited operation in the collaborative control sub-region, the power disturbance buffer factor calculation module 300 retrieves the historical data of the original power allocation weight ratio of the off-grid energy storage unit in the collaborative control sub-region before the exit from the dynamic virtual impedance parameter library, and obtains the updated equivalent impedance ratio of the current sub-region; based on the geometric scaling principle, 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 failure) is redistributed according to the proportional relationship of the equivalent impedance ratio of the remaining energy storage units (i.e., the energy storage units that continue to operate in the current collaborative control sub-region). Specifically, by constructing an impedance and output mapping function, the new output upper limit value of the remaining energy storage units satisfies the conservation relationship of the product of 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 associated in real time to ensure that the output upper limit adjustment of the remaining energy storage units is synchronized with the impedance characteristic changes of the disturbance propagation path, and finally the output upper limit compensation of each remaining energy storage unit is generated, so that the total output capacity of the coordinated control sub-area 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 unchanged.

[0040] When the 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 the current equivalent impedance ratio, adjusts the output upper limit of the remaining units according to the geometric scaling principle, and synchronously adjusts the output characteristics in conjunction with the impedance attenuation direction data to maintain the inverse proportional relationship between the weight coefficient and the equivalent impedance ratio; solves the secondary oscillation problem caused by the imbalance of power shortage distribution in the off-grid scenario, and ensures the dynamic adaptation of the total output capacity and the disturbance intensity by matching the output adjustment with the consistency of the propagation path.

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

[0042] High-frequency synchronous data acquisition units are installed at each distribution network node to capture the instantaneous change of 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. Based on the phase correlation matrix construction, grid disturbance response sensitivity analysis and dynamic parameter library generation algorithm, it performs normalization processing and disturbance propagation path modeling. It also integrates an adaptive clustering algorithm and an inverse proportional weight allocation function for dynamic division of coordinated control sub-areas and power allocation weight decision-making.

[0044] The communication interface module supports real-time data exchange between multiple nodes, connecting 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 instructions;

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

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

[0047] The above shows and describes 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 above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A distributed energy storage collaborative control system for a smart distribution network, characterized in that: The system comprises 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 reflecting the grid disturbance propagation path; The two-layer decision control module (200) comprises a lower-layer dynamic area division unit (201) and an upper-layer weight allocation unit (202), wherein: The lower layer region dynamic division unit (201) calculates the deviation value of the virtual impedance parameter in the dynamic virtual impedance parameter library, and divides the energy storage units whose deviation values ​​of the virtual impedance parameter are within a preset clustering interval into cooperative control sub-regions; The upper layer weight allocation unit (202) generates a power allocation weight coefficient inversely proportional to the equivalent impedance ratio based on the equivalent impedance ratio of the cooperative control sub-area; When the power disturbance buffer factor calculation module (300) detects that an energy storage unit has exited operation in the cooperative control sub-area, the energy storage unit is treated as an off-grid energy storage unit, and the output upper limit of the remaining energy storage units is adjusted according to the geometric scaling principle based on the proportion of the off-grid energy storage unit in the original weight distribution and the equivalent impedance ratio of the current cooperative control sub-area.

2. The distributed energy storage collaborative control system for smart distribution network according to claim 1, characterized in that: The dynamic virtual impedance parameter library construction module (100) extracts the phase angle variation 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, performs a linear combination operation on the phase correlation matrix and the power grid disturbance response sensitivity index, and generates a virtual impedance parameter through normalization processing.

3. The distributed energy storage coordinated control system for smart distribution network according to claim 1, characterized in that: The dynamic virtual impedance parameter library construction module (100) establishes a parameter update channel based on the topological connection relationship of the power grid. When a disturbance event is detected, a propagation path model with the disturbance source as the starting point is constructed according to the spatiotemporal variation law of the virtual impedance parameter, and a dynamic virtual impedance parameter library reflecting the propagation path of the power grid disturbance is established, wherein the propagation path model extends in the attenuation direction of the virtual impedance parameter.

4. The distributed energy storage coordinated control system for smart distribution network according to claim 1, characterized in that: The lower layer area dynamic division unit (201) calculates the deviation value of the virtual impedance parameter in the dynamic virtual impedance parameter library and groups them according to a preset clustering interval threshold to form a collaborative control sub-area, while excluding the node group whose physical distance exceeds the maximum coupling radius in combination with the spatial constraint conditions of the power grid topology.

5. The distributed energy storage coordinated control system for smart distribution network 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 value of the virtual impedance parameter of the energy storage unit in the coordinated control sub-area meets the preset disturbance suppression tolerance range.

6. The distributed energy storage coordinated control system for smart distribution network according to claim 1, characterized in that: The upper layer weight distribution unit (202) performs hierarchical sorting and weighting of the virtual impedance parameters of the energy storage units in the cooperative control sub-area along the disturbance propagation path direction to generate an equivalent impedance ratio, and generates a power distribution weight coefficient based on the inverse proportional relationship between the equivalent impedance ratio and the power distribution demand.

7. The distributed energy storage coordinated control system for smart distribution network according to claim 6, characterized in that: The upper layer weight distribution unit (202) dynamically adjusts the slope parameter of the inverse proportional function by monitoring the charge state change rate of the energy storage unit after power distribution, so as to keep the power distribution weight coefficient dynamically matched with the power grid disturbance intensity.

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

9. The distributed energy storage coordinated control system for smart distribution network 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 power grid disturbance intensity by maintaining the inverse proportional relationship between the power distribution weight coefficient and the equivalent impedance ratio.

10. A device using the distributed energy storage coordinated control system for a smart distribution network according to any one of claims 1 to 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, among which: The high-frequency synchronous data acquisition unit is deployed at each distribution network node to capture the instantaneous change of the inverter output phase angle and the bus voltage fluctuation waveform in real time and transmit it to the data processing core; The data processing core has a built-in virtual impedance parameter calculation engine that performs phase correlation matrix construction, grid disturbance response sensitivity index analysis, and dynamic virtual impedance parameter library generation algorithm. It also integrates an adaptive clustering algorithm and an inverse proportional weight allocation function for dynamic division of collaborative control sub-areas 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 achieve virtual impedance parameter updates, state of charge feedback, and synchronous transmission of control instructions; 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 collaborative control instructions and perform power compensation operations; The device for a distributed energy storage collaborative control system for a smart distribution network uses edge computing and a cloud collaborative architecture to store a dynamic virtual impedance parameter library and grid topology data in a distributed memory, and combines it with the real-time computing capabilities of the processor to achieve dynamic matching of the energy storage unit output with the grid impedance characteristics and suppress the risk of secondary overload.

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