Methods and systems for suppressing voltage fluctuations in distribution networks through the synergy of distributed photovoltaic and energy storage

Abstract

This invention discloses a method and system for suppressing voltage fluctuations in distribution networks using a combination of distributed photovoltaic (PV) and energy storage, relating to the field of voltage fluctuation suppression technology. The method includes the following steps: constructing an inter-node disturbance propagation reachability matrix based on real-time distribution network operation data to identify a set of voltage disturbance paths; constructing an irreversible disturbance structure graph based on the voltage disturbance path set to identify unidirectional amplification chains; calculating the disturbance excitation probability and generating an active disturbance propagation domain based on historical disturbance data of the source nodes of the unidirectional amplification chains; constructing an energy storage intervention buffer within the active disturbance propagation domain; and evaluating the reverse blocking capability of energy storage within the energy storage intervention buffer by simulating disturbance propagation to generate activation conditions for the collaborative strategy. This invention reduces redundant energy storage configuration and power consumption through disturbance path simulation evaluation, thereby improving the system's suppression efficiency and adaptability.

Description

Technical Field

[0001] This invention relates to the field of voltage fluctuation suppression technology, and more specifically, to a method and system for suppressing voltage fluctuations in distribution networks using a combination of distributed photovoltaic and energy storage. Background Technology

[0002] As the proportion of distributed photovoltaic (PV) power grids connected to the grid continues to increase, voltage disturbances in grid operation exhibit complex spatiotemporal propagation characteristics. PV power output fluctuations are not only affected by environmental factors, but the resulting voltage disturbances also propagate along the distribution network topology, exhibiting a clear path irreversibility characteristic: that is, disturbances generated by some nodes can only spread downstream in a specific direction and are difficult to cancel out in the reverse direction through the same path; once a disturbance is amplified on a certain link, traditional single-point regulation or simple distributed control methods are difficult to effectively pull back or suppress voltage fluctuations, and may even lead to the accumulation and exacerbation of disturbances.

[0003] While existing technologies such as multi-level coordination, distributed scheduling, or frequency stability analysis-based regulation methods can alleviate voltage fluctuations to some extent, none of them have established a refined model for the irreversible path structure characteristics of voltage disturbance propagation, nor do they possess dynamic control strategy designs based on chain vulnerability and node regulation capabilities. Therefore, in scenarios with high-proportion photovoltaic grid integration, traditional regulation strategies struggle to achieve high-precision suppression and rapid response, and the voltage fluctuation problem remains prominent.

[0004] The above-disclosed technical solutions have at least the following technical problems: the existing collaborative regulation mechanism does not explicitly model the irreversible structural characteristics of the voltage disturbance propagation path, which leads to the inability of the regulation process to converge effectively, and may even exacerbate the cumulative effect of the disturbance in the network.

[0005] To address the above problems, this invention proposes a solution. Summary of the Invention

[0006] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide a method and system for suppressing voltage fluctuations in distribution networks through the coordinated use of distributed photovoltaic and energy storage. By identifying voltage disturbance paths and unidirectional amplification chains, an active disturbance propagation domain is generated. Within this domain, a minimum closed control chain and an energy storage buffer are constructed in reverse. The reverse blocking capability of energy storage is evaluated, and the control strategy is corrected in real time, thereby achieving coordinated regulation of photovoltaic and energy storage. This solves the problems of regulation delay and incomplete suppression caused by irreversible voltage disturbances in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] On the one hand, the distribution network voltage fluctuation suppression method of distributed photovoltaic and energy storage collaboration includes the following steps: based on real-time operation data of the distribution network, constructing a disturbance propagation reachability matrix between nodes and identifying a set of voltage disturbance paths; constructing a disturbance irreversibility structure graph based on the set of voltage disturbance paths and identifying unidirectional amplification chains; calculating the disturbance excitation probability and generating an active disturbance propagation domain based on historical disturbance data of the source nodes of the unidirectional amplification chain; constructing an energy storage intervention buffer within the active disturbance propagation domain; and evaluating the reverse blocking capability of energy storage by simulating disturbance propagation within the energy storage intervention buffer and generating activation conditions for collaborative strategies.

[0009] In a preferred embodiment, the method for constructing an inter-node disturbance propagation reachability matrix and identifying a voltage disturbance path set based on real-time distribution network operation data is as follows: Real-time distribution network operation data is acquired, and a disturbance-response sample set is generated through distributed active detection; equivalent path impedance is calculated based on topology data to construct a model prior sensitivity matrix; regularized time-series regression is performed on the disturbance-response sample set to generate a data-driven sensitivity matrix; topology-aware fusion is performed on the model prior sensitivity matrix and the data-driven sensitivity matrix to form a directional disturbance propagation reachability matrix; threshold pruning is performed on the reachability matrix to search for all path sets that satisfy directional consistency constraints; paths containing self-loops or directional reversal elements are removed from the path set to obtain the final voltage disturbance path set.

[0010] In a preferred embodiment, the topology-aware fusion of the model prior sensitivity matrix and the data-driven sensitivity matrix specifically involves: determining node connectivity, electrical distance, and disturbance propagation direction tendency based on the distribution network topology; evaluating the credibility weights of the model prior sensitivity matrix and the data-driven sensitivity matrix respectively, and extracting temporal causal information between nodes; jointly optimizing the two types of sensitivity matrices using credibility weights, temporal causal information, and topological adjacency information as constraints to obtain a fusion matrix; and projecting and filtering the fusion matrix according to electrical distance, disturbance propagation direction tendency, and directional threshold to output a directional disturbance propagation reachability matrix.

[0011] In a preferred embodiment, the step of constructing an irreversible perturbation structure graph based on the voltage perturbation path set and identifying unidirectional amplification chains specifically involves: calculating the directional index of each edge in the voltage perturbation path set based on the relationship between the element values ​​and reverse elements of the topology-aware reachability matrix, and identifying unidirectional perturbation edges according to preset judgment conditions; constructing an irreversible structure graph reflecting the unidirectional propagation characteristics of perturbation by removing non-unidirectional perturbation edges from the voltage perturbation path set; calculating the forward path amplification and reverse path amplification for each directed path in the irreversible structure graph, and obtaining the quantization ratio of irreversibility; filtering unidirectional amplification chains according to the quantization ratio, sorting them in conjunction with the path vulnerability index, outputting a set of unidirectional amplification chains, and determining the head-of-chain driving source node and tail-of-chain diffusion node of each chain.

[0012] In a preferred embodiment, the step of calculating the disturbance excitation probability and generating the active disturbance propagation domain based on the historical disturbance data of the unidirectional amplification chain source node specifically involves:

[0013] Second data of the driving source node at the head of the unidirectional amplification chain is obtained. The second data includes: the short-term output fluctuation rate index of the distributed photovoltaic, the standard deviation of photovoltaic output, and the topological sensitivity position measurement of the driving node in the irreversible structure graph. Based on the second data, the single-point excitation probability of the driving source node at the head of the chain is calculated, and the single-point excitation probability is coupled with the structural amplification of the chain to generate the overall disturbance excitation probability of the chain. Chains with an overall disturbance excitation probability exceeding a preset activation threshold are selected and included in the disturbance active propagation domain.

[0014] In a preferred embodiment, the construction of the energy storage intervention buffer includes the step of generating a minimum closed inverse control chain, specifically: using the tail node of the unidirectional amplification chain within the active perturbation propagation domain as the control anchor point, a candidate set of energy storage units that meet the electrical distance requirements is selected according to preset performance constraints; along the unidirectional amplification chain from the tail to the head, the energy storage candidate units are arranged in the chain segment order to form an inverse control relay sequence; with covering all amplification segments of the chain as a necessary constraint and minimizing the total control resources as the optimization objective, a minimum closed inverse control chain and a corresponding chain segment-energy storage mapping list are generated.

[0015] In a preferred embodiment, the construction of the energy storage intervention buffer specifically involves: based on the minimum closed inverse control chain and its segment-energy storage mapping list, setting necessary control chain coverage thresholds including structural coverage, dynamic response coverage, and energy coverage; comparing the actual coverage indicators with the coverage thresholds chain by chain to identify substandard amplification segments; expanding the substandard segments upstream from the tail node of the chain according to a preset priority order to form an energy storage intervention buffer; pre-setting backup energy storage units, droop coefficients, power limits, and state maintenance strategies within the buffer; and outputting a buffer configuration list and an activation priority table.

[0016] In a preferred embodiment, the step of evaluating the reverse blocking capability of energy storage within the energy storage intervention buffer by simulating disturbance propagation and generating cooperative strategy activation conditions specifically involves: constructing a disturbance propagation simulation model based on the buffer configuration list and activation priority, which includes node voltage dynamic equations, energy storage power-voltage coupling model, and reverse relay control logic; sequentially injecting equivalent photovoltaic power output fluctuation signals along the disturbance active propagation domain chain, and triggering the reverse control response of energy storage within the buffer in real time; calculating the reverse blocking capability index obtained by weighted normalization of voltage recovery rate, response time margin, and energy utilization efficiency, and comparing it with a preset criterion to generate cooperative strategy activation conditions.

[0017] On the other hand, the distributed photovoltaic and energy storage collaborative distribution network voltage fluctuation suppression system includes the following modules: a disturbance reachability modeling module, used to construct an inter-node disturbance propagation reachability matrix based on real-time distribution network operation data and identify voltage disturbance path sets; an irreversible disturbance structure identification module, used to construct a disturbance irreversible structure diagram based on the voltage disturbance path set and identify unidirectional amplification chains; an active propagation domain generation module, used to calculate the disturbance excitation probability and generate an active disturbance propagation domain based on historical disturbance data of the source nodes of the unidirectional amplification chain; a buffer configuration module, used to construct an energy storage intervention buffer within the active disturbance propagation domain; and an energy storage reverse blocking capability assessment module, used to assess the reverse blocking capability of energy storage by simulating disturbance propagation within the energy storage intervention buffer and generate collaborative strategy activation conditions.

[0018] The technical effects and advantages of the distributed photovoltaic and energy storage synergistic distribution network voltage fluctuation suppression method and system of this invention are as follows:

[0019] 1. This invention combines a physical prior model with a data-driven sensitivity matrix through "topology-aware fusion" to form a directional disturbance propagation reachability matrix. By combining threshold pruning and depth-first traversal, it can accurately eliminate weak propagation paths and reverse loops, ultimately obtaining the true set of voltage disturbance paths. Furthermore, by utilizing the ratio of unidirectional disturbance edges to irreversibility, it can quickly identify unidirectional amplification chains and their driving source nodes, achieving high-resolution source tracing and probabilistic prediction of chain-like voltage fluctuations, significantly improving the accuracy of power grid operation status perception and early warning of disturbance risks.

[0020] 2. Based on identifying the active propagation domain of disturbances, this invention generates a minimum closed inverse control chain according to the chain topology and introduces a triple threshold of structural coverage, dynamic response coverage, and energy coverage to construct an energy storage intervention buffer, enabling energy storage to possess continuous, rapid, and segmented reverse voltage regulation capabilities during the disturbance growth phase. The reverse blocking capability index is evaluated through disturbance path simulation, and combined with a dynamic disturbance structural evolution feedback mechanism, real-time correction and priority adjustment of the control strategy are achieved. This ensures suppression effectiveness while reducing energy storage redundancy and power consumption, thereby improving the system's suppression efficiency and adaptability. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the distribution network voltage fluctuation suppression method of the present invention, which combines distributed photovoltaic and energy storage.

[0022] Figure 2 This is a schematic diagram of the distribution network voltage fluctuation suppression system of the present invention, which combines distributed photovoltaic and energy storage. Detailed Implementation

[0023] The technical solutions of 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1, Figure 1 This invention presents a method for suppressing voltage fluctuations in distribution networks through the synergy of distributed photovoltaic and energy storage, comprising the following steps:

[0025] S1, based on real-time operation data of the distribution network, constructs an inter-node disturbance propagation reachability matrix and identifies a set of voltage disturbance paths;

[0026] In this embodiment, the real-time operating data includes the topology data of the distribution network and the voltage sensitivity index between each node measured based on the disturbance injection method.

[0027] The method of constructing an inter-node disturbance propagation reachability matrix based on real-time operation data of the distribution network and identifying a set of voltage disturbance paths is as follows:

[0028] Acquire real-time operational data of the power distribution network and generate a disturbance-response sample set through distributed active detection;

[0029] The equivalent path impedance is calculated based on topological data, and the model's prior sensitivity matrix is ​​constructed.

[0030] Regularized time-series regression is performed on the disturbance-response sample set to generate a data-driven sensitivity matrix;

[0031] Topological sensing fusion of the model prior sensitivity matrix and the data-driven sensitivity matrix is ​​performed to form a directional perturbation propagation reachability matrix.

[0032] The reachability matrix is ​​pruned by a threshold, weak propagation paths with weights below a preset threshold are removed, and a depth-first traversal is used to search for all paths in the matrix that satisfy the directional consistency constraint.

[0033] By removing paths that contain self-loops or reverse-direction elements from the path set, the final voltage disturbance path set is obtained.

[0034] The topology-aware fusion of the model prior sensitivity matrix and the data-driven sensitivity matrix is ​​specifically as follows:

[0035] Based on the topology data of the distribution network, the connectivity between nodes and the electrical distance between lines are determined, forming topological adjacency information that reflects the structural connectivity and transmission attenuation characteristics, and the tendency of disturbance propagation direction between nodes is determined according to the phase angle of line impedance.

[0036] The confidence weights of the model prior sensitivity matrix and the data-driven sensitivity matrix between each node pair are evaluated respectively, and the temporal causal information between nodes is obtained through historical running data to reflect the temporal sequence of disturbance propagation.

[0037] In the fusion process, the credibility weight, temporal causal information and topological adjacency information are used as constraints. The model prior sensitivity matrix and the data-driven sensitivity matrix are jointly optimized and solved to obtain the fusion matrix, which makes the fusion result numerically close to both data observation and physical prior, and structurally conforms to the reachability constraints of the electrical topology.

[0038] The fusion matrix is ​​projected and filtered according to the line electrical distance, the tendency of disturbance propagation direction, and the preset directional threshold. Edges that do not meet the unidirectional propagation characteristics are removed, and the weights of the retained edges are normalized to obtain the directional disturbance propagation reachability matrix.

[0039] The specific calculation formula for topology-aware fusion is as follows:

[0040]

[0041]

[0042] in, Let node j be the topology-aware fusion weight of node i. Let J be the sensitivity of the power disturbance at node j to the voltage amplitude at node i. For the impedance between nodes The directional function is determined by the phase angle sign; it takes a value of 1 if the disturbance propagation direction matches the power flow direction, and 0 otherwise. For line-based The propagation attenuation factor The attenuation coefficient is... The voltage regulation capability deficiency factor of node i (the weaker the regulation capability, the larger the value). Let represent the voltage change at node i before and after the disturbance. This refers to the reactive power disturbance injected at node j.

[0043] In this embodiment, the distributed active detection specifically involves applying small-amplitude, orderly injection perturbations (±ΔP, ±ΔQ) to a selected photovoltaic inverter / energy storage inverter at different short time intervals, and simultaneously recording the time-series voltage responses at multiple points to form a perturbation-response test dataset.

[0044] S2, based on the set of voltage disturbance paths, constructs an irreversible disturbance structure graph and identifies unidirectional amplification chains, specifically:

[0045] For each directed edge (u,v) in the voltage disturbance path set, the directionality coefficient is calculated based on the relationship between the element values ​​and the reverse elements of the topology-aware reachability matrix, and edges with a direction value greater than a preset direction threshold are identified as unidirectional disturbance edges.

[0046] Based on the set of voltage disturbance paths, edges that do not meet the directionality criteria are removed to obtain a directed subgraph containing only unidirectional disturbance edges, which serves as the disturbance irreversible structure graph.

[0047] The directed path amplification and reverse amplification are calculated based on the perturbation irreversible structure graph, and the irreversibility ratio is obtained.

[0048] Paths that meet the criteria are selected as candidate unidirectional amplification chains. The final set of unidirectional amplification chains is output by sorting the chains by their fragility. The head-driven source node and the tail-spreading node of each chain are determined.

[0049] In this embodiment, the path that meets the conditions is specifically:

[0050] The path whose amplification volume is greater than the preset amplification threshold and whose irreversibility ratio is greater than the preset ratio threshold.

[0051] The specific formula for calculating the directional indicator is as follows:

[0052]

[0053] The specific calculation formulas for the amplification and reverse amplification are as follows:

[0054]

[0055]

[0056] The irreversibility ratio is calculated using the following formula:

[0057]

[0058] in, It is the directional coefficient. These are the element values ​​of the topology-aware reachability matrix. The reversed element value, The constant should be very small to avoid a denominator of 0. To increase the volume, For topology-aware fusion weights, To generate a large amount of data in the reverse direction, This represents the irreversibility ratio.

[0059] S3, calculate the disturbance excitation probability and generate the disturbance active propagation domain based on the historical disturbance data of the source node of the unidirectional amplification chain;

[0060] The historical disturbance data includes distributed photovoltaic power output fluctuation characteristics, topology sensitivity location, and voltage regulation capability deficiency index.

[0061] In this embodiment, the step of calculating the disturbance excitation probability and generating the disturbance active propagation domain based on the historical disturbance data of the unidirectional amplification chain source node specifically involves:

[0062] Obtain a unidirectional amplification chain The first set of data includes historical disturbance data at the source node of the chain-driven power generation system, and the second set of data includes: short-term output fluctuation rate index of distributed photovoltaic power generation. Standard deviation of photovoltaic output And the topology sensitivity location metric of the driving node in the irreversible graph. ;

[0063] Based on historical perturbation data, the single-point excitation probability of the chain head driving source node is calculated, and the single-point excitation probability is coupled with the structural amplification of the chain to calculate the overall perturbation excitation probability of the chain.

[0064] All chains that satisfy the preset activation threshold constraint are classified into the perturbation active propagation domain.

[0065] The activation threshold is adaptively determined based on historical samples.

[0066] In this embodiment, the single-point activation probability is specifically:

[0067]

[0068] The overall perturbation excitation probability is specifically calculated using the following formula:

[0069]

[0070] in, For single-point activation probability, , , These are the weighting coefficients (obtained from training based on historical samples). This is an indicator of the short-term output fluctuation rate of distributed photovoltaic power. For the standard deviation of photovoltaic output, To measure the topological sensitivity of nodes in an irreversible graph. This is a mapping function that maps linear combinations to probability values. This represents the overall probability of disturbance activation. To increase the volume, , This is the normalization function used for scaling transformation.

[0071] S4, construct an energy storage intervention buffer zone within the active perturbation propagation domain;

[0072] In this embodiment, the construction of the energy storage intervention buffer includes the step of generating the minimum closed inverse control chain, specifically:

[0073] Within the active disturbance propagation domain, with the tail node of each unidirectional amplification chain as the anchor point, and based on the constraints of reverse voltage regulation accessibility, controllable margin, communication delay and state availability, a candidate set of energy storage units within the allowable electrical distance is selected.

[0074] Based on the candidate set of energy storage units, a reverse control relay sequence is established from the tail of the chain to the head of the chain according to the chain segment order, so that adjacent relay nodes have continuous response capability within the time window and meet the local voltage pullback requirements.

[0075] With the inverse controllability of all amplified segments of the coverage chain as a necessary constraint and the sum of the number of energy storage and the number of communication links as the optimization objective, the minimum energy storage set that can close the chain is determined, the corresponding minimum closed inverse control chain is generated, and the chain segment-energy storage mapping list, the response time window of each relay node and the preset voltage-reactive / active adjustment parameters are output simultaneously as inputs for subsequent path suppression coverage assessment and buffer construction.

[0076] The selection of candidate energy storage units within the permissible electrical distance range specifically involves:

[0077] Taking the tail node of the unidirectional amplification chain as the center, the electrical distance from the tail node to other nodes is calculated according to the power distribution network topology and line impedance, and energy storage units that exceed the preset maximum allowable distance are eliminated.

[0078] Calculate the reverse voltage regulation sensitivity of the remaining energy storage units at the chain tail node, and remove energy storage units with sensitivity lower than the lower limit of reverse adjustment to ensure that they have the ability to effectively pull back the chain tail voltage.

[0079] Assess the controllable margin of each energy storage unit, that is, the proportion of active / reactive power regulation capacity that can be immediately dispatched under the current operating conditions to its rated capacity, and eliminate units that are below the minimum margin threshold.

[0080] Based on the distribution network communication topology and delay statistics, determine whether the single-trip delay of the control command of each energy storage unit is within the allowable range of the link segment disturbance growth time constant, and eliminate units that exceed the limit;

[0081] The status availability tag of the energy storage unit is retrieved, and units in maintenance, off-grid, or power-limited mode are removed. The energy storage units that meet all constraints are retained to form a candidate set of energy storage units for the tail node, and a reverse adjustment priority tag is attached to each candidate unit.

[0082] The construction of the energy storage intervention buffer specifically involves:

[0083] Based on the minimum closed inverse control chain and its chain segment-energy storage mapping list, a necessary control chain coverage threshold including structural coverage, dynamic response coverage and energy coverage is set. The structural coverage is used to determine whether each amplified segment of the chain is reverse controlled by at least one energy storage relay node. The dynamic response coverage is used to determine whether each relay node can complete continuous relay within a specified time window. The energy coverage is used to determine whether the power of energy storage and the available power meet the suppression requirements during the expected disturbance duration.

[0084] The actual coverage index is compared with the coverage threshold chain by chain. For the amplified segments that do not meet the standard, the amplification is expanded from the tail of the chain to the upstream in order of priority to form an energy storage intervention buffer. In the buffer, the backup energy storage unit, droop coefficient, power limit and state maintenance strategy are preset, and the buffer configuration list and activation priority table are output.

[0085] S5, within the energy storage intervention buffer zone, assesses the reverse blocking capability of energy storage by simulating disturbance propagation, and generates activation conditions for the cooperative strategy, specifically:

[0086] Using the buffer configuration list and activation priority table of the energy storage intervention buffer as simulation input, a multi-timescale disturbance propagation simulation platform is constructed, which includes node voltage dynamic equations, energy storage power-voltage coupling model and reverse relay control logic.

[0087] According to the chain topology of the active perturbation propagation domain, equivalent photovoltaic power output fluctuation signals are injected sequentially from the head of the chain to the tail of the chain, and the reverse control response of the energy storage in the buffer is triggered in real time in the simulation. The voltage pull-back time, perturbation attenuation coefficient and power consumption curve of the chain segment are recorded.

[0088] The reverse blocking capability index of each energy storage relay segment is calculated. The reverse blocking capability index is obtained by weighted normalization of the chain segment voltage recovery rate, response time margin and energy utilization efficiency, and is compared chain by chain with the set blocking capability criterion.

[0089] Based on the comparison results, activation conditions for the collaborative strategy are generated. These activation conditions include a trigger disturbance power threshold, a lower limit for link segment blocking capability, and a threshold for the remaining proportion of energy storage. These activation conditions are then used as direct inputs to the subsequent online control system to determine whether to activate the energy storage collaborative reverse blocking strategy during actual grid operation.

[0090] In this embodiment, the reverse blocking capability index is specifically calculated using the following formula:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] in, This is an index of reverse blocking capability. For voltage recovery rate, To allow for a response time margin, For energy utilization efficiency, , , These are the weighting factors (obtained from historical data). This represents the maximum voltage drop at the tail node under uncontrolled conditions. The instantaneous voltage drop value under control conditions. In the evaluation time domain The residual fall at the end The allowed suppression time for the chain segment, The time it takes for the tail voltage to return to a preset reference threshold. This represents the difference in voltage drop area between the link segment and the controlled voltage drop area under both uncontrolled and controlled conditions. Energy scale conversion factor ( ,in, The rated power of the system, (for voltage reference value) For energy storage The actual energy consumed internally. This is the normalization function.

[0097] In this embodiment, the distribution network voltage fluctuation suppression method of the present invention, which combines distributed photovoltaic and energy storage, further includes the following steps:

[0098] Construct a dynamic disturbance structure evolution feedback mechanism to correct the cooperative control strategy path in real time, specifically as follows:

[0099] In actual operation, voltage fluctuations and propagation trends in the network are monitored, and the reachability matrix and irreversible path structure are updated in real time.

[0100] If the current regulatory behavior fails to suppress the path or a new irreversible path is triggered, the strategy is retrospectively corrected to activate the region, prioritize the intervention nodes, and adjust the control chain structure, forming a structurally dynamic adaptive closed loop.

[0101] The structural evolution feedback mechanism includes the following processing logic:

[0102] The reachability matrix is ​​updated in real time, and the irreversibility of newly added perturbation paths is assessed.

[0103] Reassign energy storage regulation responsibility nodes to unclosed paths in the control strategy;

[0104] Update the collaborative priority weight vector for high-frequency activation paths;

[0105] Output control warning signs to areas where a closed-loop suppression has not been formed after the strategy is implemented.

[0106] Example 2: The distributed photovoltaic and energy storage synergistic distribution network voltage fluctuation suppression system of the present invention includes the following modules:

[0107] Disturbance reachability modeling module: Used to construct the inter-node disturbance propagation reachability matrix and identify voltage disturbance path sets based on real-time operation data of the distribution network;

[0108] Irreversible Disturbance Structure Identification Module: Used to construct an irreversible disturbance structure diagram based on the set of voltage disturbance paths and identify unidirectional amplification chains;

[0109] Active propagation domain generation module: used to calculate the disturbance excitation probability and generate the active propagation domain of the disturbance based on the historical disturbance data of the source node of the unidirectional amplification chain;

[0110] Buffer configuration module: used to construct an energy storage intervention buffer within the active perturbation propagation domain;

[0111] Energy Storage Reverse Interception Capability Assessment Module: Used to assess the reverse interception capability of energy storage within the energy storage intervention buffer by simulating disturbance propagation, and to generate activation conditions for collaborative strategies.

[0112] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0113] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0114] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0116] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0117] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for suppressing voltage fluctuations in distribution networks through the synergy of distributed photovoltaic and energy storage, characterized in that, Includes the following steps: Based on real-time operation data of the distribution network, a disturbance propagation reachability matrix between nodes is constructed to identify the voltage disturbance path set. Specifically, real-time operation data of the distribution network is obtained, and a disturbance-response sample set is generated through distributed active detection. The equivalent path impedance is calculated based on topological data, and a model prior sensitivity matrix is ​​constructed. Regularized time-series regression is performed on the disturbance-response sample set to generate a data-driven sensitivity matrix. The model prior sensitivity matrix and the data-driven sensitivity matrix are fused with topology awareness to form a directional disturbance propagation reachability matrix. The reachability matrix is ​​pruned by thresholding, and all paths that satisfy the directional consistency constraint are searched. Paths containing self-loops or directional reversal elements are removed from the path set to obtain the final voltage disturbance path set. Construct an irreversible perturbation structure graph based on the set of voltage perturbation paths to identify unidirectional amplification chains; Based on the historical disturbance data of the source node of the unidirectional amplification chain, the disturbance excitation probability is calculated and the disturbance active propagation domain is generated. Construct an energy storage intervention buffer zone within the active perturbation propagation domain; Within the energy storage intervention buffer zone, the reverse blocking capability of energy storage is evaluated by simulating disturbance propagation, and the activation conditions for the collaborative strategy are generated. Specifically, based on the buffer zone configuration list and activation priority, a disturbance propagation simulation model is constructed, which includes the node voltage dynamic equation, the energy storage power-voltage coupling model, and the reverse relay control logic. Equivalent photovoltaic power output fluctuation signals are injected sequentially along the disturbance active propagation domain chain, and the reverse control response of energy storage within the buffer zone is triggered in real time. The reverse blocking capability index, obtained by weighted normalization of voltage recovery rate, response time margin, and energy utilization efficiency, is calculated and compared with the preset criteria to generate the activation conditions for the collaborative strategy.

2. The method for suppressing voltage fluctuations in a distribution network using distributed photovoltaic and energy storage synergy as described in claim 1, characterized in that, The topology-aware fusion of the model prior sensitivity matrix and the data-driven sensitivity matrix is ​​specifically as follows: Determine node connectivity, electrical distance, and disturbance propagation direction based on the distribution network topology; The confidence weights of the model prior sensitivity matrix and the data-driven sensitivity matrix are evaluated separately, and the temporal causal information between nodes is extracted. By using confidence weights, temporal causal information, and topological adjacency information as constraints, the two types of sensitivity matrices are jointly optimized to obtain the fusion matrix; Based on electrical distance, disturbance propagation direction tendency, and directional threshold, the fusion matrix is ​​directionally projected and filtered to output a directional disturbance propagation reachability matrix.

3. The method for suppressing voltage fluctuations in a distribution network using distributed photovoltaic and energy storage synergy as described in claim 2, characterized in that, The construction of the irreversible perturbation structure graph based on the voltage perturbation path set, and the identification of unidirectional amplification chains, specifically involves: Based on the relationship between the element values ​​and the reverse elements of the topology-aware reachability matrix, the directional index of each edge in the voltage disturbance path set is calculated, and unidirectional disturbance edges are identified according to preset judgment conditions. By removing non-unidirectional perturbation edges from the voltage perturbation path set, an irreversible structure graph reflecting the unidirectional propagation characteristics of perturbation is constructed. For each directed path in the irreversible structure graph, the amplification of the forward path and the amplification of the reverse path are calculated separately, and the quantification ratio of irreversibility is obtained. The unidirectional amplification chains are selected based on the quantification ratio, sorted by the path vulnerability index, and a set of unidirectional amplification chains is output. The head-of-chain driving source node and the tail-of-chain diffusion node of each chain are determined.

4. The method for suppressing voltage fluctuations in a distribution network using distributed photovoltaic and energy storage synergy as described in claim 3, characterized in that, The specific steps for calculating the disturbance excitation probability and generating the active disturbance propagation domain based on the historical disturbance data of the unidirectional amplification chain source node are as follows: The second data of the driving source node at the head of the chain in the unidirectional amplification chain is obtained. The second data includes: the short-term output fluctuation rate index of distributed photovoltaic, the standard deviation of photovoltaic output, and the topological sensitivity position measurement of the driving node in the irreversible structure graph. Based on the second data, the single-point excitation probability of the chain head driving source node is calculated, and the single-point excitation probability is coupled with the structural amplification of the chain to generate the overall perturbation excitation probability of the chain. Chains whose overall perturbation excitation probability exceeds a preset activation threshold are selected and classified into the perturbation active propagation domain.

5. The method for suppressing voltage fluctuations in a distribution network using distributed photovoltaic and energy storage synergy as described in claim 4, characterized in that, The construction of the energy storage intervention buffer includes the step of generating the minimum closed inverse control chain, specifically: Using the tail node of the unidirectional amplification chain in the active perturbation propagation domain as the control anchor point, a candidate set of energy storage units that meet the electrical distance is selected according to the preset performance constraints. Along the unidirectional amplification chain from the tail to the head, the energy storage candidate units are arranged in the order of chain segments to form a reverse control relay sequence; With the complete amplification segment of the control chain as a necessary constraint and the minimization of total control resources as the optimization objective, a minimum closed inverse control chain and the corresponding chain segment-energy storage mapping list are generated.

6. The method for suppressing voltage fluctuations in a distribution network using distributed photovoltaic and energy storage synergy as described in claim 5, characterized in that, The construction of the energy storage intervention buffer specifically involves: Based on the minimum closed inverse control chain and its segment-energy storage mapping list, the necessary control chain coverage thresholds, including structural coverage, dynamic response coverage and energy coverage, are set. The actual coverage index is compared chain by chain with the coverage threshold to identify the amplified segments that do not meet the standard; Starting from the tail node and moving upstream, substandard sections are expanded outwards in a preset priority order to form an energy storage intervention buffer zone. The system pre-configures backup energy storage units, droop coefficients, power limits, and state maintenance strategies within the buffer zone, and outputs a buffer configuration list and activation priority table.

7. A system using the distributed photovoltaic and energy storage synergistic distribution network voltage fluctuation suppression method as described in any one of claims 1-6, characterized in that, Includes the following modules: Disturbance reachability modeling module: Used to construct the inter-node disturbance propagation reachability matrix and identify voltage disturbance path sets based on real-time operation data of the distribution network; Irreversible Disturbance Structure Identification Module: Used to construct an irreversible disturbance structure diagram based on the set of voltage disturbance paths and identify unidirectional amplification chains; Active propagation domain generation module: used to calculate the disturbance excitation probability and generate the active propagation domain of the disturbance based on the historical disturbance data of the source node of the unidirectional amplification chain; Buffer configuration module: used to construct an energy storage intervention buffer within the active perturbation propagation domain; Energy Storage Reverse Interception Capability Assessment Module: Used to assess the reverse interception capability of energy storage within the energy storage intervention buffer by simulating disturbance propagation, and to generate activation conditions for collaborative strategies.

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