Power grid black-start control method and system for vehicle-network interactive control

Abstract

The invention discloses a power grid black-start control method and system for vehicle-network interaction control, and relates to the technical field of black-start control, and the method comprises the following steps: constructing a power supply topological graph based on the first data of an electric vehicle in a target region, and generating a dynamic frequency boundary migration graph; calculating the maximum frequency response offset of each node under given disturbance based on the boundary migration graph, screening the nodes of which the deviations are within a preset threshold value, and constructing a time difference synchronization pane; based on the time difference synchronization pane, frequency behavior pattern clustering is carried out in the boundary migration graph, a plurality of transient electron donating groups are generated, and a regional transient self-excitation group is formed; identifying a frequency gradient boundary node based on a regional transient self-excitation group, and implementing cross-group regulation and control; and obtaining second data in the regulation and control process, constructing a feedback state matrix, and updating the boundary migration graph. By introducing a frequency response offset analysis and time difference synchronization pane construction mechanism, the overall stability of frequency response is improved, and the oscillation risk at the initial stage of system starting is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of black start control, and more particularly to a vehicle-grid interactive control method and system for a power grid black start. Background Art

[0002] With the widespread integration of distributed energy resources and electric vehicles (EVs), traditional power systems face significant challenges in recovering from black starts following sudden, widespread power outages (e.g., extreme weather events, main power failures, etc.). Traditional black starts typically rely on a small number of self-starting hydropower, gas, or diesel generators to gradually increase load and restore power. However, given the widespread deployment of distributed energy sources and limited centralized control capabilities, this approach struggles to meet the rapidity, resilience, and multi-source coordination requirements of modern distribution networks.

[0003] At the same time, electric vehicles, as mobile energy units with energy storage and controllable power characteristics, have the potential to participate in black start auxiliary power supply in specific scenarios. However, existing vehicle-grid interactive control strategies mostly focus on daily peak shaving, ancillary services, or V2G scheduling control, and lack a "decentralized collaborative start" mechanism for large-scale grid failures. Specifically, the uneven distribution of electric vehicles, heterogeneous access nodes, and diverse frequency response characteristics make it difficult to establish a stable synchronous control relationship during the black start process. Frequency disturbances can easily spread along the grid topology path, causing local instability or control failure.

[0004] For example, the invention patent publication number CN117578605A discloses a microgrid black start control method, apparatus, device, and storage medium for real-time calculation of a black start control strategy based on the actual operating conditions of the microgrid, thereby reducing the risk of failure during a black start, increasing the recovery speed, and enhancing the safety of the microgrid black start. The method includes: obtaining the real-time topology parameters of the microgrid and establishing an initial microgrid online simulation system based on the real-time topology parameters; assigning values ​​to the initial microgrid online simulation system based on the microgrid's operating status data to obtain a target microgrid online simulation system; performing real-time calculations using the target microgrid online simulation system and pre-set constraint boundaries to generate a black start control strategy for the microgrid; and, when a microgrid power outage is detected, switching the microgrid's mode to black start control and executing the black start control strategy.

[0005] For example, the invention patent publication number CN111221266A discloses a simulation test system and test method for a microgrid black start. A workstation generates a microgrid simulation topology model using MATLAB / SIMULINK software and sends it to a simulator for execution. The workstation then generates a microgrid black start control strategy using MATLAB / stateflow software's automatic code generation technology and sends it to a controller. The simulator uses a power amplifier to control the power level of simulated distributed power devices, performs simulation according to the microgrid black start control strategy, and collects simulation test results from the simulator to verify the correctness of the microgrid black start control strategy. This invention enables power interaction between the microgrid simulation topology model and distributed power devices, studies the impact of a power-level distributed power simulator on a microgrid black start, develops a microgrid black start control strategy using MATLAB's automatic code generation technology, illustrates the impact of distributed power devices on a microgrid black start, and verifies the correctness of the microgrid black start control strategy.

[0006] The above disclosed technical solutions have at least the following technical problems: Black start requires thousands or even tens of thousands of electric vehicles to coordinate power supply, but existing technologies are difficult to achieve real-time synchronous control of large-scale clusters. Timing differences in vehicle charging and discharging may cause frequency oscillations in the microgrid and even lead to temporary power collapse. At the same time, the existing centralized scheduling system has high computing delays.

[0007] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a grid black start control method and system with vehicle-grid interactive control. By constructing a dynamic frequency boundary migration diagram, a time difference synchronization pane and a regional transient self-excitation group, and combining frequency behavior pattern clustering with a cross-group regulation mechanism, coordinated power supply control of a group of electric vehicles during the black start process is achieved, so as to solve the problems in the prior art of poor grid black start frequency stability and easy spread of local disturbances under the conditions of large-scale electric vehicle access.

[0009] To achieve the above object, the present invention provides the following technical solutions: A grid black start control method for vehicle-grid interactive control comprises the following steps: constructing an initial power supply mapping topology based on first data of electric vehicles in a target area, and generating a dynamic frequency boundary migration diagram; calculating the maximum frequency response offset of each node under a given disturbance based on the boundary migration diagram, screening nodes with deviations within a preset threshold, and constructing a time difference synchronization pane; clustering frequency behavior patterns in the boundary migration diagram based on the time difference synchronization pane, generating a plurality of transient power supply subgroups, and forming a regional transient self-excitation group; identifying frequency gradient boundary nodes based on the regional transient self-excitation group, and implementing cross-group regulation; obtaining second data in the regulation process, constructing a feedback state matrix, and updating the boundary migration diagram.

[0010] In a preferred embodiment, the initial power supply mapping topology is constructed based on the first data of electric vehicles in the target area, specifically: based on the first data of electric vehicles in the target area, the vehicle nodes with power supply capability are screened for injectability; the screened vehicle nodes are mapped with the corresponding distribution network topology to form a set of node pairs with vehicle-grid connection relationships; based on the node pair set, the shortest power supply path from the main control node to each vehicle node is used as the basic path structure, and the path transmission weight is calculated according to the resistance-reactance parameters and topology depth in the path, a power supply connectivity diagram is constructed to form an initial power supply mapping topology.

[0011] In a preferred embodiment, the generation of a dynamic frequency boundary migration map is specifically as follows: marking the vehicle nodes with injection capability in the power supply mapping topology map as micro-source injection units, and marking the remaining nodes as perception units; selecting an initial disturbance node in the micro-source injection unit and simulating the propagation path of the frequency disturbance signal caused by the node access on the topology map; based on the path transmission weight and electrical coupling parameters in the topology map, calculating the transmission delay and frequency response amplitude of the disturbance propagation to each target node, and constructing a disturbance propagation tensor between node pairs; generating a multi-period disturbance response contour map based on the propagation tensor, extracting the node boundary set whose disturbance response amplitude crosses a preset threshold, and forming a dynamic frequency boundary migration map.

[0012] In a preferred embodiment, the maximum frequency response offset of each node under a given disturbance is calculated based on the boundary migration diagram, nodes with deviations within a preset threshold are screened, and a time difference synchronization pane is constructed. Specifically, the maximum response offset between the power injection and frequency response of each target node under a given disturbance is calculated based on the dynamic frequency boundary migration diagram and the frequency response data of the corresponding node; the maximum frequency response offset of each node is compared with a preset frequency deviation threshold, and a set of nodes whose deviations meet the threshold condition is screened out; for the screened set of nodes, the injection-response time difference between the nodes is calculated based on the delay characteristics of the disturbance propagation; the nodes whose time difference distribution falls within the preset synchronization tolerance window are classified, and a time difference synchronization pane is constructed.

[0013] In a preferred embodiment, the construction of the time difference synchronization pane also includes: setting a maximum response time window. When the response time of any electric vehicle node after the simulated disturbance injection is greater than the maximum response time window, the node is excluded from the construction range of this round of time difference synchronization pane.

[0014] In a preferred embodiment, the frequency behavior pattern clustering is performed in the boundary migration graph based on the time difference synchronization pane, specifically: each candidate node screened in the time difference synchronization pane is used as the initial clustering unit; based on the node adjacency relationship in the boundary migration graph, the frequency response similarity between each node in the initial clustering unit is calculated; the edge weights of the node pairs whose frequency response similarity is higher than the preset aggregation threshold are enhanced to form a frequency behavior coherent subgraph; according to the frequency response feature distance matrix in the coherent subgraph structure, a number of frequency behavior pattern clusters are generated based on the constrained spectral clustering method.

[0015] In a preferred embodiment, the generation of several transient power supply groups and forming a regional transient self-excitation group is specifically as follows: based on the frequency behavior pattern clustering results, the nodes in each frequency pattern cluster are uniformly mapped to the local regional subgraph in the boundary migration graph to form a candidate transient power supply group; internal topological consistency evaluation is performed on each power supply group, and candidate groups with low topological connectivity and drastic boundary frequency fluctuations are screened out to obtain the remaining power supply groups; injection frequency synchronization detection is performed on the nodes in the remaining power supply groups, the average frequency slope difference within the group is calculated, and high offset nodes are eliminated; the screened power supply groups are used as regional transient self-excitation groups, and the self-regulating electric vehicles in each self-excitation group are set as virtual master nodes.

[0016] In a preferred embodiment, the frequency gradient boundary nodes are identified based on the regional transient self-excitation group, and cross-group regulation is implemented, specifically: based on the regional transient self-excitation group and the boundary migration map, the cross-boundary nodes between two or more self-excitation groups are identified; based on the frequency response data, the relative offset of each boundary node in the frequency evolution process of the self-excitation group is calculated to form a frequency gradient map, and the cross-nodes with frequency offset greater than the threshold are screened and marked as critical control nodes; a bidirectional power reinjection strategy is constructed at the structural boundary cross-node, and cross-group regulation is performed.

[0017] In a preferred embodiment, the second data in the control process is obtained, a feedback state matrix is ​​constructed, and a boundary migration map is updated. Specifically, the second data is collected based on the real-time monitoring data of the cross-group control execution phase; the second data is compared with the baseline state of each boundary node before control, and a feedback state matrix is ​​constructed; based on the numerical distribution of the feedback index of each node in the feedback state matrix, the path weight coefficient in the boundary migration map is updated; and an updated boundary migration map is generated to achieve adaptive reconstruction of the boundary structure of cross-group collaborative control.

[0018] The technical effects and advantages of the grid black start control method and system for vehicle-grid interactive control of the present invention are as follows: 1. By constructing a dynamic frequency boundary migration diagram and clustering frequency behavior based on time-difference synchronization panes, this invention can aggregate electric vehicle nodes with similar frequency response characteristics into regional transient self-excitation groups. This structure provides local frequency-power coordination capabilities, effectively reducing reliance on centralized control during black start, enabling regional-level synchronized self-excitation operation, improving the overall stability of the frequency response, and reducing the risk of oscillation during the initial system startup.

[0019] 2. By incorporating frequency response offset analysis and a time-difference synchronization pane construction mechanism, this invention effectively filters out vehicle nodes with unstable responses or large delays, preventing nodes with abnormal frequency responses from being included in the synchronization control range. During frequency behavior clustering and boundary demarcation, the combination of similarity metrics and topological constraints reduces misclassification, helping to improve the accuracy of group control and the effectiveness of boundary control, enhancing the implementability and practical operability of commands during the black start phase. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of a grid black start control method for vehicle-grid interactive control according to the present invention; Figure 2 The diagram is a structural diagram of a grid black start control system with vehicle-grid interactive control according to the present invention. DETAILED DESCRIPTION

[0021] The following will provide a clear and complete description of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Example 1, Figure 1 The present invention provides a grid black start control method for vehicle-grid interactive control, comprising the following steps: S1, constructing an initial power supply mapping topology diagram based on first data of electric vehicles in the target area, and generating a dynamic frequency boundary migration diagram; The first data includes the access location of the electric vehicle node and state assessment parameters (including battery state of charge SOC, power margin, and grid impedance coupling coefficient); The initial power supply mapping topology is constructed based on the first data of the electric vehicles in the target area, specifically: Performing injectability screening on vehicle nodes with power supply capabilities based on first data of electric vehicles in the target area; Mapping the vehicle nodes that meet the conditions to the corresponding distribution network topology to form a set of node pairs with vehicle-grid connection relationships; Based on the node pair set, the shortest power supply path from the master node to each vehicle node is used as the basic path structure. The path transmission weight is calculated according to the resistance-reactance parameters and topological depth in the path, and a power supply connectivity graph is constructed to form an initial power supply mapping topology graph.

[0023] The injectability screening is specifically as follows: Obtaining state assessment parameters of each electric vehicle node in the target area, the parameters including battery state of charge (SOC), power margin, and grid impedance coupling coefficient; Select vehicle nodes that meet all of the following conditions as candidate nodes with power supply capabilities: The SOC is higher than the preset discharge threshold (e.g. 60%); The maximum injectable power is greater than the required power corresponding to the lowest operating voltage limit of the topology path; The current working mode is in a grid-connectable state, and its interface inverter supports black start and off-grid operation; Its geographical location has a known physical topology connection relationship with the master control node and is not in an isolated or communication failure area.

[0024] After screening, the above vehicle nodes are marked as injectable units to participate in the subsequent topology mapping and frequency control path modeling.

[0025] The path transmission weight is specifically:

[0026] in, is the path transmission weight, 、 and are the preset weight coefficients, is the resistance-to-reactance ratio of the path, is the normalized value of topological depth, is the current node depth, is the maximum depth, is the black start time, is the time constant.

[0027] The generating of the dynamic frequency boundary migration diagram is specifically as follows: The vehicle nodes with injection capability in the power supply mapping topology are marked as micro-source injection units, and the remaining nodes are marked as perception units; Select the initial disturbance node in the micro-source injection unit and simulate the propagation path of the frequency disturbance signal caused by the node access on the topology graph; Based on the path transmission weights and electrical coupling parameters in the topology graph, the propagation delay and frequency response amplitude of the disturbance propagation to each target node are calculated, and the disturbance propagation tensor between node pairs is constructed. A multi-period disturbance response contour map is generated based on the propagation tensor, and the node boundary set whose disturbance response amplitude crosses the preset threshold is extracted to form a dynamic frequency boundary migration map representing the boundary changes of the frequency disturbance evolution.

[0028] It should be noted that the dynamic frequency boundary migration diagram is used to characterize the dynamic boundary evolution characteristics of frequency disturbances propagating over time and space during a power grid black start. By simulating the frequency fluctuations caused by electric vehicles (micro-source injection units) connected to the distribution network, it quantifies the propagation of disturbances within the topology and identifies critical boundary nodes and risk areas for system frequency stability.

[0029] S2, calculates the maximum frequency response offset of each node under a given disturbance based on the boundary migration graph, filters out nodes whose deviation is within a preset threshold, and constructs a time difference synchronization pane; The method of calculating the maximum frequency response offset of each node under a given disturbance based on the boundary migration graph, screening nodes whose deviation is within a preset threshold, and constructing a time difference synchronization pane is specifically as follows: Based on the dynamic frequency boundary migration diagram and the frequency response data of the corresponding nodes, the maximum response offset between the power injection and frequency response of each target node under a given disturbance is calculated; Compare the maximum frequency response offset of each node with the preset frequency deviation threshold, and select the node set whose deviation meets the threshold condition; For the selected node set, the injection-response time difference between nodes is calculated based on the delay characteristics of disturbance propagation; Nodes whose time difference distribution falls within a preset synchronization tolerance window are classified, and at least one time difference synchronization pane is constructed as a basic unit of synchronization control.

[0030] The build time difference synchronization pane also includes: The maximum response time window is set. When the response time of any electric vehicle node after the simulated disturbance injection is greater than the maximum response time window, the node will be excluded from the construction range of the time difference synchronization pane in this round.

[0031] The maximum frequency response offset is specifically:

[0032] in, is the maximum frequency response deviation, 、 is the start and end time of the disturbance, is the delay from the disturbance source to node i, is the frequency response of node i at time t, is the node frequency-power coupling coefficient, is the active power injection of node i at time t, is the equivalent attenuation factor of the disturbance path, To reflect the nonlinear expansion of the disturbance distance and its action window width, Characterizes the spatial amplification characteristics of disturbances during propagation.

[0033] It should be noted that the node frequency-power coupling coefficient will vary due to different conditions such as the grid structure, inverter control strategy, and local impedance environment to which different electric vehicles are connected. The node located near the main feeder may have a stronger degree of frequency control and a smaller node frequency-power coupling coefficient; the node located at the far end or connected to a high impedance point is susceptible to frequency disturbance and has a larger node frequency-power coupling coefficient.

[0034] S3, based on the time difference synchronization pane, frequency behavior pattern clustering is performed in the boundary migration diagram to generate several transient electron supply groups and form a regional transient self-excitation group; The frequency behavior pattern clustering is performed in the boundary migration graph based on the time difference synchronization pane, specifically: Each candidate node screened in the time difference synchronization pane is used as the initial clustering unit, and the injection response start time of each candidate node is used as the time reference mark; Based on the node adjacency relationship in the boundary migration graph, the frequency response similarity between the nodes in the initial clustering unit is calculated, and the similarity includes the frequency change slope, response peak delay and recovery time difference; For node pairs whose frequency response similarity is higher than the preset aggregation threshold, edge weights are enhanced to form a frequency behavior coherent subgraph; According to the frequency response characteristic distance matrix in the coherent subgraph structure, several frequency behavior pattern clusters are generated based on the constrained spectral clustering method.

[0035] The generation of a plurality of transient electron supply groups to form a regional transient self-excited group is specifically as follows: Based on the frequency behavior pattern clustering results, the nodes in each frequency pattern cluster are uniformly mapped to the local region subgraph in the boundary migration graph to form a candidate transient electron donor group; The internal topological consistency of each electron donor group is evaluated to eliminate candidate groups with low topological connectivity and drastic boundary frequency fluctuations to obtain the remaining electron donor groups; Perform injection frequency synchronization detection on the remaining nodes in the donor group, calculate the average frequency slope difference within the group, and eliminate high-offset nodes; The screened electron-donating groups are used as regional transient self-excited groups, the self-regulating electric vehicles in each self-excited group are set as virtual master nodes, and their injection-response control parameters are recorded.

[0036] The self-regulating electric vehicle in each self-excitation group is set as a virtual master node, specifically: Based on the ranking of the frequency adjustment coefficient and the maximum instantaneous injectable power of each vehicle node, the node with the largest product is selected as the master node of the self-excited group. The master node is responsible for providing the frequency reference, and the remaining vehicles perform slave synchronous following control.

[0037] Furthermore, in order to prevent regional frequency drift caused by main node failure, no less than one backup main node is set in each regional transient self-excitation group. The activation condition of the backup node is that the frequency deviation of the main node exceeds the preset frequency stability threshold within a preset continuous time. Once activated, the synchronization reference frequency within the group is reset and the frequency control instructions are redistributed.

[0038] The benefits of forming a regional transient self-excitation group include: Improve the stability of synchronous control of multi-vehicle collaboration: By clustering electric vehicle nodes with similar dynamic response characteristics into a unified group through frequency behavior similarity; avoid phase interference and group desynchronization caused by response timing differences during frequency fluctuation propagation; better characterize the "dynamic controllable boundary" than simple voltage or power partitioning, and achieve more robust black start power supply synchronization.

[0039] Establish local autonomous transient power supply capabilities and improve control response speed: The generated regional transient self-excitation group can achieve local rapid self-excitation operation without the need for global scheduling response; it has a local frequency-power regulation mechanism, significantly reducing the collapse risk caused by centralized control delays.

[0040] It is highly scalable and fault-tolerant: each transient power supply group is an independently modeled subsystem with adjustable boundary constraints, which can easily operate independently in the event of system failure or communication interruption; the clustering results and boundary migration diagrams can be dynamically updated to adapt to the scenario of random access or exit of electric vehicles; if the main node in the self-excited group becomes unstable, the backup node can be quickly switched, which has the important robustness in black start.

[0041] S4, based on the regional transient self-excitation group, the frequency gradient boundary nodes are identified and cross-group control is implemented; The frequency gradient boundary nodes are identified based on the regional transient self-excitation group, and cross-group control is implemented, specifically; Based on the regional transient self-excitation group and boundary migration diagram, the cross-border nodes between two or more self-excitation groups are identified; Based on the frequency response data, the relative offset of each boundary node during the self-excited group frequency evolution process is calculated to form a frequency gradient map. The cross nodes with frequency offset greater than the threshold are screened and marked as critical control nodes. A bidirectional power reinjection strategy is constructed at the cross-nodes at the structure boundary, and cross-cluster regulation is performed.

[0042] The bidirectional power injection strategy is specifically as follows: The low-frequency group boundary nodes are used as power injection targets; The high-frequency group boundary nodes are used as power extraction sources; the maximum power injection capacity, injection delay and timing response model are established between each pair of cross nodes.

[0043] S5, obtaining the second data in the control process, constructing the feedback state matrix, and updating the boundary migration map.

[0044] The second data includes frequency drift, injection response and feedback error of each node; The second data in the control process is obtained, a feedback state matrix is ​​constructed, and a boundary transition map is updated, specifically: Collecting second data based on real-time monitoring data during the cross-group control execution phase; Comparing the second data with the baseline state of each boundary node before regulation, and constructing a feedback state matrix; Based on the numerical distribution of feedback indicators of each node in the feedback state matrix, the path weight coefficients in the boundary migration graph are updated; Generate an updated boundary migration map to achieve adaptive reconstruction of the boundary structure of cross-group collaborative regulation, and provide structural boundary support for the next round of frequency behavior clustering and regional self-excitation group division.

[0045] The path weight coefficient in the boundary migration graph is updated based on the numerical distribution of the feedback index of each node in the feedback state matrix, specifically: Enhance the connection weights between nodes in the stable response area and improve the trust in regulation; Weaken the boundary edge weights of areas where frequency residuals repeatedly appear, guiding subsequent regulation to avoid unstable paths; For nodes with significant feedback effects, the guiding frequency flow factor is introduced to dynamically adjust the control influence.

[0046] Furthermore, the guided frequency flow factor is a dynamic control coefficient introduced to solve the problem that frequency disturbances are difficult to be locally constrained and the control energy diffusion path is unstable during the black start process. It is used to fine-tune the "guiding role" or "control influence" of a specific node in the frequency control network.

[0047] Example 2, Figure 2The present invention provides a grid black start control system with vehicle-grid interactive control, including the following modules: A boundary migration map construction module is used to construct an initial power supply mapping topology map based on the first data of electric vehicles in the target area, and generate a dynamic frequency boundary migration map; Synchronization pane construction module: used to calculate the maximum frequency response offset of each node under a given disturbance based on the boundary migration graph, filter out nodes whose deviation is within the preset threshold, and construct the time difference synchronization pane; Frequency behavior clustering and transient self-excitation group generation module: used to cluster frequency behavior patterns in the boundary migration diagram based on the time difference synchronization pane, generate several transient supply electron groups, and form a regional transient self-excitation group; Cross-group boundary control module: used to identify frequency gradient boundary nodes based on regional transient self-excitation groups and implement cross-group control; Feedback regulation module: used to obtain the second data in the regulation process, build the feedback state matrix, and update the boundary migration map.

[0048] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0049] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0050] Those skilled in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0051] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0052] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0053] Finally: 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 in the scope of protection of the present invention.

Claims

1. A grid black start control method with vehicle-grid interactive control, characterized in that: The following steps are involved: Based on the first data of electric vehicles in the target area, an initial power supply mapping topology diagram is constructed, and a dynamic frequency boundary migration diagram is generated; Based on the boundary migration graph, the maximum frequency response offset of each node under a given disturbance is calculated, and nodes with deviations within a preset threshold are screened to construct a time difference synchronization pane; Based on the time difference synchronization pane, frequency behavior pattern clustering is performed in the boundary migration diagram to generate several transient electron supply groups and form regional transient self-excitation groups; Identify frequency gradient boundary nodes based on regional transient self-excitation groups and implement cross-group control; Acquire the second data in the control process, construct the feedback state matrix, and update the boundary migration map.

2. The grid black start control method for vehicle-grid interactive control according to claim 1, characterized in that: The initial power supply mapping topology is constructed based on the first data of the electric vehicles in the target area, specifically: Performing injectability screening on vehicle nodes with power supply capabilities based on first data of electric vehicles in the target area; Map the screened vehicle nodes to the corresponding distribution network topology to form a set of node pairs with vehicle-grid connection relationships; Based on the node pair set, the shortest power supply path from the master node to each vehicle node is used as the basic path structure. The path transmission weight is calculated according to the resistance-reactance parameters and topological depth in the path, and a power supply connectivity graph is constructed to form an initial power supply mapping topology graph.

3. The grid black start control method for vehicle-grid interactive control according to claim 2, characterized in that: The generating of the dynamic frequency boundary migration diagram is specifically as follows: The vehicle nodes with injection capability in the power supply mapping topology are marked as micro-source injection units, and the remaining nodes are marked as perception units; Select the initial disturbance node in the micro-source injection unit and simulate the propagation path of the frequency disturbance signal caused by the node access on the topology graph; Based on the path transmission weights and electrical coupling parameters in the topology graph, the propagation delay and frequency response amplitude of the disturbance propagation to each target node are calculated, and the disturbance propagation tensor between node pairs is constructed. A multi-period disturbance response contour map is generated based on the propagation tensor, and the node boundary set whose disturbance response amplitude crosses the preset threshold is extracted to form a dynamic frequency boundary migration map.

4. The grid black start control method for vehicle-grid interactive control according to claim 3, characterized in that: The method of calculating the maximum frequency response offset of each node under a given disturbance based on the boundary migration graph, screening nodes whose deviation is within a preset threshold, and constructing a time difference synchronization pane is specifically as follows: Based on the dynamic frequency boundary migration diagram and the frequency response data of the corresponding nodes, the maximum response offset between the power injection and frequency response of each target node under a given disturbance is calculated; Compare the maximum frequency response offset of each node with the preset frequency deviation threshold, and select the node set whose deviation meets the threshold condition; For the selected node set, the injection-response time difference between nodes is calculated based on the delay characteristics of disturbance propagation; Nodes whose time difference distribution falls within the preset synchronization tolerance window are classified to build a time difference synchronization pane.

5. The grid black start control method for vehicle-grid interactive control according to claim 4, characterized in that: The build time difference synchronization pane also includes: The maximum response time window is set. When the response time of any electric vehicle node after the simulated disturbance injection is greater than the maximum response time window, the node will be excluded from the construction range of the time difference synchronization pane in this round.

6. The grid black start control method for vehicle-grid interactive control according to claim 5, characterized in that: The frequency behavior pattern clustering is performed in the boundary migration graph based on the time difference synchronization pane, specifically: Each candidate node screened in the time difference synchronization pane is used as the initial clustering unit; Based on the node adjacency relationship in the boundary migration graph, the frequency response similarity between the nodes in the initial clustering unit is calculated; For node pairs whose frequency response similarity is higher than the preset aggregation threshold, edge weights are enhanced to form a frequency behavior coherent subgraph; According to the frequency response characteristic distance matrix in the coherent subgraph structure, several frequency behavior pattern clusters are generated based on the constrained spectral clustering method.

7. The grid black start control method for vehicle-grid interactive control according to claim 6, characterized in that: The generation of a plurality of transient electron supply groups to form a regional transient self-excited group is specifically as follows: Based on the frequency behavior pattern clustering results, the nodes in each frequency pattern cluster are uniformly mapped to the local region subgraph in the boundary migration graph to form a candidate transient electron donor group; The internal topological consistency of each electron donor group is evaluated to eliminate candidate groups with low topological connectivity and drastic boundary frequency fluctuations to obtain the remaining electron donor groups; Perform injection frequency synchronization detection on the remaining nodes in the donor group, calculate the average frequency slope difference within the group, and eliminate high-offset nodes; The screened electron supply group is used as the regional transient self-excitation group, and the self-regulating electric vehicle in each self-excitation group is set as the virtual master node.

8. The grid black start control method for vehicle-grid interactive control according to claim 7, characterized in that: The frequency gradient boundary nodes are identified based on the regional transient self-excitation group, and cross-group control is implemented, specifically; Based on the regional transient self-excitation group and boundary migration diagram, the cross-border nodes between two or more self-excitation groups are identified; Based on the frequency response data, the relative offset of each boundary node during the self-excited group frequency evolution process is calculated to form a frequency gradient map. The cross nodes with frequency offset greater than the threshold are screened and marked as critical control nodes. A bidirectional power reinjection strategy is constructed at the cross-nodes at the structure boundary, and cross-cluster regulation is performed.

9. The grid black start control method for vehicle-grid interactive control according to claim 8, characterized in that: The second data in the control process is obtained, a feedback state matrix is ​​constructed, and a boundary transition map is updated, specifically: Collecting second data based on real-time monitoring data during the cross-group control execution phase; Comparing the second data with the baseline state of each boundary node before regulation, and constructing a feedback state matrix; Based on the numerical distribution of feedback indicators of each node in the feedback state matrix, the path weight coefficients in the boundary migration graph are updated; Generate an updated boundary migration map to achieve adaptive reconstruction of the boundary structure for cross-group collaborative regulation.

10. A system using the grid black start control method for vehicle-grid interactive control according to any one of claims 1 to 9, characterized in that: Includes the following modules: A boundary migration map construction module is used to construct an initial power supply mapping topology map based on the first data of electric vehicles in the target area, and generate a dynamic frequency boundary migration map; Synchronization pane construction module: used to calculate the maximum frequency response offset of each node under a given disturbance based on the boundary migration graph, filter out nodes whose deviation is within the preset threshold, and construct the time difference synchronization pane; Frequency behavior clustering and transient self-excitation group generation module: used to cluster frequency behavior patterns in the boundary migration diagram based on the time difference synchronization pane, generate several transient supply electron groups, and form a regional transient self-excitation group; Cross-group boundary control module: used to identify frequency gradient boundary nodes based on regional transient self-excitation groups and implement cross-group control; Feedback regulation module: used to obtain the second data in the regulation process, build the feedback state matrix, and update the boundary migration map.

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