A power grid black start control method and system for vehicle-to-grid interaction control

Abstract

The application 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 comprises the following steps: constructing a power supply topology graph and generating a dynamic frequency boundary migration graph based on first data of electric vehicles in a target region; calculating maximum frequency response offsets of each node under a given disturbance based on the boundary migration graph, screening nodes with deviations within a preset threshold, and constructing a time difference synchronization window; clustering frequency behavior patterns in the boundary migration graph based on the time difference synchronization window, generating a plurality of transient power supply subgroups, and forming regional transient self-excitation groups; identifying frequency gradient boundary nodes based on the regional transient self-excitation groups, and implementing cross-group regulation and control; acquiring second data in the regulation and control process, constructing a feedback state matrix, and updating the boundary migration graph. The application introduces frequency response offset analysis and a time difference synchronization window construction mechanism, improves the overall stability of frequency response, and reduces the oscillation risk in the initial stage of system start.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of black start control, and more particularly, to a power grid black start control method and system for vehicle-to-grid interaction control. BACKGROUND

[0002] With the large-scale access of distributed energy and electric vehicles (EVs), the traditional power system faces significant challenges in the black start recovery process after a sudden large-area power outage (such as extreme weather, main power failure, etc.). Traditional black start usually relies on a few self-starting-capable hydroelectric, gas or diesel generators to gradually pull in the load and gradually restore power supply capability. However, in the context of widespread deployment of distributed energy supply and limited centralized control capability, this method is difficult to meet the requirements of modern distribution networks for speed, flexibility and multi-source coordination.

[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 certain scenarios. However, existing vehicle-to-grid interaction control strategies focus more on daily peak shaving, auxiliary services or V2G scheduling control, and lack a "decentralized coordinated start" mechanism after a large-scale power grid failure. Specifically, electric vehicles are unevenly distributed, connected to heterogeneous nodes, and have diverse frequency response characteristics, making it difficult to establish stable synchronization control relationships during black start and causing frequency disturbances to spread along the power grid topology, leading to local instability or control failure.

[0004] For example, the invention patent with the announcement number CN117578605A announces a micro-grid black start control method, device, equipment and storage medium, which is used for real-time calculation of black start control strategy according to the actual operation of the micro-grid, reduces the risk of failure of the micro-grid black start, improves the recovery speed of the micro-grid, and improves the safety of the micro-grid black start. The method comprises: obtaining real-time topological parameters of the micro-grid, and establishing an initial micro-grid online simulation system based on the real-time topological parameters; the initial micro-grid online simulation system is valued based on the operation state data of the micro-grid to obtain a target micro-grid online simulation system; the target micro-grid online simulation system and the preset constraint boundary are used for real-time calculation to generate a black start control strategy of the micro-grid; when the micro-grid is detected to be powered off, the mode of the micro-grid is switched to black start control, and the black start control strategy is executed.

[0005] For example, the invention patent announcement No. CN111221266A, a kind of simulation test system and test method suitable for micro-grid black start, workstation generates micro-grid simulation topology model by MATLAB / SIMULINK software and sends to simulation machine to run, workstation generates micro-grid black start control strategy by the automatic code generation technology of MATLAB / stateflow software and sends to controller, simulation machine realizes the power level control to analog distributed power equipment by power amplifier, according to micro-grid black start control strategy carries out simulation, controller collects the simulation test result of simulation machine, verifies the correctness of micro-grid black start control strategy.The present application can realize the power interaction between micro-grid simulation topology model and distributed power equipment, study the influence of power level distributed power simulator on micro-grid black start, develop micro-grid black start control strategy combined with MATLAB automatic code generation technology, illustrate the influence of distributed power equipment on micro-grid black start and verify the correctness of micro-grid black start control strategy.

[0006] In the above disclosed technical solutions, there are at least the following technical problems: black start needs thousands or even tens of thousands of electric vehicles to cooperate power supply, but the existing technology is difficult to realize real-time synchronous control of large-scale clusters,

[0007] The timing difference of vehicle charging and discharging may cause micro-grid frequency oscillation, and even cause temporary power collapse, and the existing centralized scheduling system has high calculation delay.

[0008] In view of the above problems, the present application provides a solution. SUMMARY

[0009] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a power grid black start control method and system for vehicle-to-grid interaction control, by constructing a dynamic frequency boundary migration graph, a time difference synchronization window and a regional transient self-excitation group, and combining frequency behavior pattern clustering and cross-group regulation mechanism, the cooperative power supply control of electric vehicle group in the process of black start is realized, to solve the problem of poor frequency stability of power grid black start and easy diffusion of local disturbance under the condition of large-scale electric vehicle access in the prior art.

[0010] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0011] The method comprises the following steps: based on the first data of the electric vehicles in the target area, an initial power supply mapping topological graph is constructed, and a dynamic frequency boundary migration graph is generated; based on the boundary migration graph, the maximum frequency response offset of each node under a given disturbance is calculated, nodes with a deviation within a preset threshold are screened, and a time difference synchronization window is constructed; based on the time difference synchronization window, frequency behavior mode clustering is performed in the boundary migration graph, a plurality of transient power supply groups are generated, and a regional transient self-excitation group is formed; based on the regional transient self-excitation group, a frequency gradient boundary node is identified, and cross-group regulation is implemented; the second data in the regulation process is obtained, a feedback state matrix is constructed, and the boundary migration graph is updated.

[0012] In a preferred embodiment, based on the first data of the electric vehicles in the target area, the initial power supply mapping topological graph is constructed, specifically: based on the first data of the electric vehicles in the target area, the injectability of the vehicle nodes with power supply capability is screened; the screened vehicle nodes are mapped with the corresponding power distribution network topological structure to form a node pair set of vehicle network connection relationship; based on the node pair set, the shortest power supply path from the master node to each vehicle node is taken as the basic path structure, and the path transmission weight is calculated according to the resistance-reactance parameters and topological depth in the path, to construct a power supply connectivity graph and form an initial power supply mapping topological graph.

[0013] In a preferred embodiment, the dynamic frequency boundary migration graph is generated, specifically: the vehicle nodes with injection capability in the power supply mapping topological graph are marked as micro-source injection units, and the remaining nodes are marked as sensing units; an initial disturbance node is selected in the micro-source injection unit, and the propagation path of the frequency disturbance signal caused by the node access in the topological graph is simulated; based on the path transmission weight and the electrical coupling parameters in the topological graph, the transfer delay and the frequency response amplitude of the disturbance propagation to each target node are calculated, to construct a disturbance propagation tensor between the node pairs; a multi-period disturbance response profile is generated according to the propagation tensor, a node boundary set with a disturbance response amplitude across a preset threshold is extracted, and a dynamic frequency boundary migration graph is formed.

[0014] In a preferred embodiment, based on the boundary migration graph, the maximum frequency response offset of each node under a given disturbance is calculated, nodes with a deviation within a preset threshold are screened, and a time difference synchronization window is constructed, specifically: based on the dynamic frequency boundary migration graph and the frequency response data of the corresponding nodes, the maximum response offset between power injection and frequency response of each target node under a given disturbance is calculated; the maximum frequency response offset of each node is compared with a preset frequency deviation threshold, and a node set with a deviation satisfying the threshold condition is screened out; based on the time delay characteristics of disturbance propagation, the injection-response time difference between the screened node set is calculated; nodes with a time difference distribution falling within a preset synchronization tolerance window are classified, and a time difference synchronization window is constructed.

[0015] In a preferred embodiment, the construction time difference synchronization pane further comprises: setting a maximum response time window, when the response time of any electric vehicle node after the simulation of the disturbance injection is greater than the maximum response time window, the node is excluded from the construction range of the time difference synchronization pane in this round.

[0016] In a preferred embodiment, the time difference synchronization pane is used to cluster frequency behavior patterns in the boundary migration graph, specifically: the candidate nodes screened in the time difference synchronization pane are used as initial clustering units; the frequency response similarity between the nodes in the initial clustering units is calculated based on the node adjacency relationship in the boundary migration graph; the edge weight of the node pairs with a frequency response similarity higher than a preset aggregation threshold is enhanced to form a frequency behavior coherent subgraph; and a number of frequency behavior pattern clusters are generated based on the constrained spectral clustering method according to the frequency response feature distance matrix in the coherent subgraph structure.

[0017] In a preferred embodiment, a number of transient power supply subgroups are generated and constitute regional transient self-excitation groups, specifically: based on the clustering results of the frequency behavior patterns, the nodes in each frequency pattern cluster are uniformly mapped to the local area subgraph in the boundary migration graph to form candidate transient power supply subgroups; the internal topological consistency of each power supply subgroup is evaluated to screen out candidate groups with low topological connectivity and intense boundary frequency fluctuations to obtain remaining power supply subgroups; injection frequency synchronization detection is performed on the nodes in the remaining power supply subgroups, the average frequency slope difference in the subgroup is calculated, and high deviation nodes are removed; the screened power supply subgroups 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.

[0018] In a preferred embodiment, the regional transient self-excitation groups are used to identify frequency gradient boundary nodes and implement cross-group regulation, specifically: based on the regional transient self-excitation groups and the boundary migration graph, the intersection boundary nodes between two or more self-excitation groups are identified; the relative deviation of each boundary node in the frequency evolution process of the self-excitation group is calculated based on the frequency response data to form a frequency gradient map, and the intersection nodes with a frequency deviation greater than a threshold value are screened and marked as critical regulation nodes; a bidirectional power injection strategy is constructed at the structural boundary intersection nodes, and cross-group regulation is performed.

[0019] In a preferred embodiment, the second data in the regulation process is obtained, a feedback state matrix is constructed, and the boundary migration graph is updated, specifically: the second data is collected based on the real-time monitoring data in the cross-group regulation execution stage; the second data is compared with the baseline state of each boundary node before regulation, and a feedback state matrix is constructed; the path weight coefficient in the boundary migration graph is updated based on the numerical distribution of the feedback indicators of the nodes in the feedback state matrix; and the updated boundary migration graph is generated to realize adaptive reconstruction of the cross-group collaborative regulation boundary structure.

[0020] The technical effects and advantages of the power grid black start control method and system for vehicle network interaction control of the present application are as follows:

[0021] 1. The present application can aggregate electric vehicle nodes with similar frequency response characteristics into regional transient self-excitation groups by constructing a dynamic frequency boundary migration graph and a frequency behavior clustering based on a time difference synchronization window. This structure has local frequency-power coordination capability, can effectively reduce the dependence on centralized control during black start, realize regional level synchronous self-excitation operation, help improve the overall stability of frequency response, and reduce the risk of oscillation in the initial stage of system start.

[0022] 2. The present application can effectively filter unstable or delayed vehicle nodes by introducing frequency response offset analysis and time difference synchronization window construction mechanism, avoiding the inclusion of nodes with abnormal frequency response into the synchronization control range. In the process of frequency behavior clustering and boundary division, the combination of similarity index and topological constraint can reduce misclassification, help improve the accuracy of group regulation and the execution effect of boundary control, and enhance the instruction landing and actual operability in the black start stage. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The present application is a flowchart of a power grid black start control method for vehicle network interaction control.

[0024] Figure 2 The present application is a structural diagram of a power grid black start control system for vehicle network interaction control. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0026] Embodiment 1, Figure 1 The present application is a power grid black start control method for vehicle network interaction control, comprising the following steps:

[0027] S1, based on the first data of the electric vehicle in the target region, constructing an initial power supply mapping topological graph and generating a dynamic frequency boundary migration graph;

[0028] The first data includes the access position of the electric vehicle node, state evaluation parameters (including battery state of charge SOC, power margin, and power grid impedance coupling coefficient);

[0029] The first data of the electric vehicle in the target area is used to construct an initial power supply mapping topology, specifically:

[0030] The first data of the electric vehicle in the target area is used to screen the injectable nodes of the vehicle nodes with power supply capability;

[0031] The vehicle nodes that meet the conditions are mapped with the corresponding power distribution network topology to form a node pair set of vehicle network connection relationship;

[0032] 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, and the path transmission weight is calculated according to the resistance-reactance parameters in the path and the topology depth, to construct a power supply connectivity graph and form an initial power supply mapping topology.

[0033] The injectable screening is specifically:

[0034] The state evaluation parameters of each electric vehicle node in the target area are obtained, including the battery state of charge SOC, the power margin, and the grid impedance coupling coefficient;

[0035] The vehicle nodes that meet all the following conditions are selected as candidate nodes with power supply capability:

[0036] The SOC is higher than the preset dischargeable threshold (such as 60%);

[0037] The maximum injectable power is greater than the demand power corresponding to the lower limit of the lowest operating voltage of the topology path;

[0038] The current working mode is in a grid-connected state, and the interface inverter supports black start and off-grid operation;

[0039] The geographic location of the vehicle node has a known physical topology connection relationship with the master node, and is not in an isolated or communication failure area.

[0040] After screening, the above vehicle nodes are marked as injectable units and participate in subsequent topology mapping and frequency control path modeling.

[0041] The path transmission weight is specifically:

[0042]

[0043] Wherein, is the path transmission weight, , and are preset weight coefficients, is the resistance-reactance ratio of the path, is the topology depth normalization value, is the current node depth, for the maximum depth, for the time of black start, for the time constant.

[0044] The generating dynamic frequency boundary migration map, specifically:

[0045] Mark the vehicle nodes with injection capability in the power supply mapping topological map as micro-source injection units, and mark the remaining nodes as sensing units;

[0046] Select an 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 topological map;

[0047] Based on the path transmission weight and electrical coupling parameters in the topological map, calculate the transfer delay and frequency response amplitude of the disturbance propagation to each target node, and construct the disturbance propagation tensor between the node pairs;

[0048] Generate a multi-period disturbance response profile based on the propagation tensor, extract a node boundary set whose disturbance response amplitude crosses a preset threshold, and form a dynamic frequency boundary migration map representing the evolution of the frequency disturbance boundary change.

[0049] It should be noted that the dynamic frequency boundary migration map is used to depict the dynamic boundary evolution characteristics of the frequency disturbance propagation with time and space in the process of power grid black start. It simulates the frequency fluctuation caused by the access of electric vehicles (micro-source injection units) to the distribution network, quantifies the propagation law of the disturbance in the topological structure, and then identifies the key boundary nodes and risk areas of system frequency stability.

[0050] S2, based on the boundary migration map, calculate the maximum frequency response deviation of each node under a given disturbance, select the nodes whose deviation is within a preset threshold, and construct a time difference synchronization window;

[0051] The maximum frequency response deviation of each node under a given disturbance is calculated based on the boundary migration map, and the nodes whose deviation is within a preset threshold are selected to construct a time difference synchronization window, specifically:

[0052] Based on the dynamic frequency boundary migration map and the frequency response data of the corresponding nodes, calculate the maximum response deviation between the power injection and the frequency response of each target node under a given disturbance;

[0053] Compare the maximum frequency response deviation of each node with a preset frequency deviation threshold, and select a node set whose deviation satisfies the threshold condition;

[0054] Based on the time delay characteristics of disturbance propagation, calculate the injection-response time difference between the selected node set;

[0055] The nodes with time difference distribution falling within a preset synchronization tolerance window are classified to construct at least one time difference synchronization window as a basic unit of synchronization control.

[0056] The constructing the time difference synchronization window further includes:

[0057] A maximum response time window is set, and when the response time of any electric vehicle node after the simulation disturbance injection is greater than the maximum response time window, the node is excluded from the construction range of the time difference synchronization window in this round.

[0058] The maximum frequency response offset is specifically:

[0059]

[0060] Wherein, is the maximum frequency response offset, , 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, is a nonlinear expansion reflecting the nearness of the disturbance and the width of the action window, characterizes the spatial amplification characteristics of the disturbance in the propagation process.

[0061] It should be noted that the node frequency-power coupling coefficient will change due to different grid structures connected by different electric vehicles, inverter control strategies, local impedance environment and other conditions. The node located near the main feeder may have a stronger frequency control, and the node frequency-power coupling coefficient is smaller. The frequency of the node located at the far end or connected to a high impedance point is easily disturbed, and the node frequency-power coupling coefficient is larger.

[0062] S3, based on the time difference synchronization window, frequency behavior pattern clustering is performed in the boundary migration graph to generate a plurality of transient power supply subgroups and form regional transient self-excitation groups;

[0063] The frequency behavior pattern clustering based on the time difference synchronization window in the boundary migration graph is specifically:

[0064] Each candidate node screened in the time difference synchronization window is taken as an initial clustering unit, and the injection response start time of each candidate node is taken as a time reference mark;

[0065] Based on the node adjacency relationship in the boundary migration graph, the frequency response similarity between nodes in the initial clustering unit is calculated, including the frequency change slope, response peak time delay and recovery time difference;

[0066] The node pairs with a frequency response similarity higher than a preset aggregation threshold are enhanced in edge weight to form a frequency behavior coherent subgraph.

[0067] According to the frequency response feature distance matrix in the coherent subgraph structure, a plurality of frequency behavior mode clusters are generated based on the constrained spectral clustering method.

[0068] The method further comprises generating a plurality of transient power supply groups and forming a regional transient self-excitation group, specifically:

[0069] Based on the frequency behavior mode clustering result, the nodes in each frequency mode cluster are uniformly mapped to the local area subgraph in the boundary migration graph to form a candidate transient power supply group.

[0070] The internal topological consistency of each power supply group is evaluated, and the candidate groups with low topological connectivity and high boundary frequency fluctuation are filtered out to obtain the remaining power supply groups.

[0071] The injection frequency synchronization detection is performed on the nodes in the remaining power supply groups, the average frequency slope difference in the group is calculated, and the high offset nodes are removed.

[0072] The filtered power supply groups are used as the regional transient self-excitation group, each self-regulating electric vehicle in the self-excitation group is set as a virtual master node, and the injection-response control parameters are recorded.

[0073] The method further comprises setting each self-regulating electric vehicle in the self-excitation group as a virtual master node, specifically:

[0074] Based on the frequency regulation coefficient and the maximum instantaneous injectable power of each vehicle node, the node with the maximum product is selected as the master node of the self-excitation group, and the remaining vehicles perform slave synchronous follow-up control.

[0075] Further, to prevent the regional frequency drift caused by the failure of the master node, at least one backup master node is set in each regional transient self-excitation group, and the activation condition of the backup node is that the frequency offset of the master node exceeds the preset frequency stability threshold within a preset continuous time. Once activated, the synchronous reference frequency in the group is reset and the frequency regulation instruction is redistributed.

[0076] The advantages of the regional transient self-excitation group include:

[0077] Stability of synchronous control of multi-vehicle coordination is improved: through frequency behavior similarity clustering, electric vehicle nodes with similar dynamic response characteristics are aggregated into a unified group; phase interference and group out-of-step phenomena caused by response time difference are avoided in frequency fluctuation propagation; compared with simple voltage or power partitioning, a more stable black start power supply synchronization is achieved.

[0078] Local autonomous transient power supply capability is established, and control response speed is improved: the generated regional transient self-excitation group can realize local fast self-excitation operation without global dispatching response; the local frequency-power regulation mechanism significantly reduces the collapse risk caused by centralized control delay.

[0079] High scalability and fault tolerance are achieved: each transient power supply group is an independent subsystem with adjustable boundary constraints, which can operate independently in the case of system failure or communication interruption; the clustering results and boundary migration graph can be dynamically updated to adapt to the scenario of random access or exit of electric vehicles; after the main node in the self-excitation group loses stability, the backup node can be quickly switched, which has important robustness in black start.

[0080] S4, based on the regional transient self-excitation group, the frequency gradient boundary node is identified, and cross-group regulation is implemented;

[0081] The frequency gradient boundary node is identified based on the regional transient self-excitation group, and cross-group regulation is implemented, specifically:

[0082] Based on the regional transient self-excitation group and the boundary migration graph, the intersection boundary nodes between two or more self-excitation groups are identified;

[0083] 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 intersection nodes with a frequency offset greater than a threshold are selected and marked as critical regulation nodes;

[0084] A bidirectional power injection strategy is constructed at the structural boundary intersection nodes, and cross-group regulation is performed.

[0085] The bidirectional power injection strategy is specifically:

[0086] The low-frequency group boundary node is used as the power injection target;

[0087] The high-frequency group boundary node is used as the power extraction source; the maximum power injection capacity, injection delay, and time sequence response model are established between each pair of intersection nodes.

[0088] S5, the second data in the regulation process is obtained, a feedback state matrix is constructed, and the boundary migration graph is updated.

[0089] The second data includes the frequency drift, injection response, and feedback error of each node;

[0090] The second data in the regulation process is acquired, a feedback state matrix is constructed, and a boundary migration graph is updated, specifically:

[0091] Second data is collected based on real-time monitoring data in the cross-group regulation execution stage;

[0092] The second data is compared with the reference state of each boundary node before regulation, and a feedback state matrix is constructed;

[0093] Based on the numerical distribution of the feedback indicators of each node in the feedback state matrix, the path weight coefficient in the boundary migration graph is updated;

[0094] An updated boundary migration graph is generated, realizing adaptive reconstruction of the boundary structure of cross-group collaborative regulation, and providing structural boundary support for the next round of frequency behavior clustering and regional self-activated group division.

[0095] The path weight coefficient in the boundary migration graph is updated based on the numerical distribution of the feedback indicators of each node in the feedback state matrix, specifically:

[0096] The connection weight between the nodes in the stable response area is enhanced, and the regulation trust degree is improved;

[0097] The boundary edge weight of the area where the frequency residual repeatedly appears is weakened, guiding the subsequent regulation to avoid unstable paths;

[0098] For the nodes with significant feedback effect, a guide frequency flow direction factor is introduced to dynamically adjust the control influence degree.

[0099] Further, the guide frequency flow direction factor is a dynamic control coefficient introduced to solve the problem of difficult local constraint of frequency disturbance and unstable regulation energy diffusion path in the black start process, and is used to finely adjust the "guiding effect" or "control influence degree" of a specific node in the frequency regulation network.

[0100] Embodiment 2, Figure 2 A power grid black start control system for vehicle-network interaction control is given, comprising the following modules:

[0101] The boundary migration graph construction module is used to construct an initial power supply mapping topological graph based on the first data of the electric vehicles in the target area, and generate a dynamic frequency boundary migration graph;

[0102] The synchronization pane construction module is used to calculate the maximum frequency response deviation of each node under a given disturbance based on the boundary migration graph, filter the nodes with a deviation within a preset threshold, and construct a time difference synchronization pane;

[0103] The frequency behavior clustering and transient self-excitation group generation module is configured to perform frequency behavior pattern clustering in the boundary migration graph based on the time difference synchronization window, generate a plurality of transient power supply groups, and form the regional transient self-excitation group;

[0104] The cross-group boundary regulation module is configured to identify frequency gradient boundary nodes based on the regional transient self-excitation group, and implement cross-group regulation.

[0105] The feedback adjustment module is configured to obtain second data in the regulation process, construct a feedback state matrix, and update the boundary migration graph.

[0106] The above formulas are all dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the most recent real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0107] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially.

[0108] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0109] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0110] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0111] Finally, the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A power grid black start control method of vehicle-to-grid interaction control, characterized in that, The method comprises the following steps: Based on the first data of the electric vehicles in the target area, an initial power supply mapping topological graph is constructed, and a dynamic frequency boundary migration graph is generated, which is used to represent the amplitude and delay characteristics of the frequency fluctuation caused by the electric vehicles accessing the power distribution network propagating among nodes; Based on the boundary migration graph, the maximum frequency response deviation of each node under a given disturbance is calculated, nodes with a deviation within a preset threshold are screened, and a time difference synchronization window is constructed; The time difference synchronization window refers to a node set with a response time falling within a synchronization tolerance window based on the disturbance response time delay and the node frequency response difference, which is used to form a basic unit for synchronization control; Based on the time difference synchronization window, frequency behavior mode clustering is performed in the boundary migration graph, a plurality of transient power supply subgroups are generated, and a regional transient self-excitation group is formed, the transient power supply subgroup refers to a node set with similar frequency response characteristics and good topological connectivity in the black start process, and the regional transient self-excitation group is a local self-regulating group composed of one or more transient power supply subgroups, which is used to realize fast local frequency self-excitation and synchronization control; Based on the regional transient self-excitation group, frequency gradient boundary nodes are identified, and cross-group regulation is implemented; Second data in the regulation process is obtained, a feedback state matrix is constructed, and the boundary migration graph is updated. 2.The power grid black start control method of vehicle-to-grid interaction control according to claim 1, wherein, Based on the first data of the electric vehicles in the target area, an initial power supply mapping topological graph is constructed, and a dynamic frequency boundary migration graph is generated, which is used to represent the amplitude and delay characteristics of the frequency fluctuation caused by the electric vehicles accessing the power distribution network propagating among nodes; Based on the first data of the electric vehicles in the target area, an initial power supply mapping topological graph is constructed, and a dynamic frequency boundary migration graph is generated, which is used to represent the amplitude and delay characteristics of the frequency fluctuation caused by the electric vehicles accessing the power distribution network propagating among nodes; The specific steps are as follows: The vehicle nodes with injection capability in the power supply mapping topological graph are marked as micro-source injection units, and the remaining nodes are marked as sensing units; 3.The power grid black start control method of claim 2, wherein, An initial disturbance node is selected in the micro-source injection unit, and the propagation path of the frequency disturbance signal caused by the node access in the topological graph is simulated; Based on the path transmission weight and electrical coupling parameters in the topological graph, the transfer delay and frequency response amplitude of the disturbance propagation to each target node are calculated, and a disturbance propagation tensor between the node pairs is constructed; According to the propagation tensor, a multi-period disturbance response profile is generated, a node boundary set with a disturbance response amplitude exceeding a preset threshold is extracted, and a dynamic frequency boundary migration graph is formed. The specific steps are as follows: Based on the dynamic frequency boundary migration graph and the frequency response data of the corresponding nodes, the maximum response deviation between power injection and frequency response of each target node under a given disturbance is calculated; 4. The power grid black start control method of claim 3, wherein, The maximum frequency response deviation of each node is compared with a preset frequency deviation threshold, and a node set with a deviation satisfying the threshold condition is screened out; ​ ​ For the selected node set, the injection-response time difference between nodes is calculated based on the time delay characteristics of disturbance propagation; Nodes with time difference distribution within the preset synchronization tolerance window are classified to construct a time difference synchronization window.

5. The power grid black start control method of claim 4, wherein, The construction of the time difference synchronization window also includes: Set the maximum response time window. If 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 the time difference synchronization window in this round.

6. The power grid black start control method of claim 5, wherein, Based on the time difference synchronization window, the frequency behavior pattern clustering is performed in the boundary migration graph, specifically: Each candidate node in the time difference synchronization window is selected as an initial clustering unit. Based on the node adjacency relationship in the boundary migration graph, the frequency response similarity between nodes in the initial clustering unit is calculated. For node pairs with a frequency response similarity higher than a preset aggregation threshold, the edge weight is enhanced to form a frequency behavior coherent subgraph. According to the frequency response feature distance matrix in the coherent subgraph structure, a plurality of frequency behavior pattern clusters are generated based on the constrained spectral clustering method.

7. The power grid black start control method of vehicle-to-grid interaction control according to claim 6, characterized in that, The generation of a plurality of transient power supply subgroups and the formation of a regional transient self-excitation group are as follows: Based on the frequency behavior pattern clustering result, the nodes in each frequency pattern cluster are uniformly mapped to the local area subgraph in the boundary migration graph to form a candidate transient power supply subgroup. The internal topological consistency of each power supply subgroup is evaluated, and the candidate groups with low topological connectivity and intense boundary frequency fluctuations are filtered out to obtain the remaining power supply subgroups. The injection frequency synchronization detection is performed on the nodes in the remaining power supply subgroups, the average frequency slope difference in the group is calculated, and high deviation nodes are removed. The filtered power supply subgroup is used as the regional transient self-excitation group, and the self-regulating electric vehicles in each self-excitation group are set as virtual master nodes. 8.The method of claim 7, wherein, Based on the regional transient self-excitation group, the frequency gradient boundary nodes are identified, and the cross-group regulation is implemented, specifically: Based on the regional transient self-excitation group and the boundary migration graph, the intersection boundary nodes between two or more self-excitation groups are identified. Based on the frequency response data, the relative deviation of each boundary node in the frequency evolution process of the self-excitation group is calculated to form a frequency gradient map, and the intersection nodes with a frequency deviation greater than a threshold value are selected and marked as critical regulation nodes. A bidirectional power injection strategy is constructed for the structural boundary intersection nodes, and cross-group regulation is performed. 9.The power grid black-start control method of claim 8, wherein, The second data in the regulation process is obtained, a feedback state matrix is constructed, and the boundary migration graph is updated, specifically: Based on the real-time monitoring data in the cross-group regulation execution stage, the second data is collected. The second data is compared with the baseline state of each boundary node before regulation, and a feedback state matrix is constructed. Based on the numerical distribution of the feedback indicators of each node in the feedback state matrix, the path weight coefficient in the boundary migration graph is updated. An updated boundary migration graph is generated to realize the adaptive reconstruction of the cross-group collaborative regulation boundary structure.

10. A system for using the power grid black start control method of vehicle-to-grid interaction control according to any one of claims 1-9, characterized in that, The following modules are included: A boundary migration graph construction module is used to construct an initial power supply mapping topological graph based on the first data of electric vehicles in a target region and generate a dynamic frequency boundary migration graph. The synchronization pane construction module is configured to calculate the maximum frequency response deviation of each node under a given disturbance based on the boundary migration graph, screen the nodes with the deviation within a preset threshold, and construct a time difference synchronization pane; The frequency behavior clustering and transient self-excitation group generation module is configured to perform frequency behavior mode clustering in the boundary migration graph based on the time difference synchronization pane, generate a plurality of transient power supply groups, and form a regional transient self-excitation group; The cross-group boundary regulation module is configured to identify frequency gradient boundary nodes based on the regional transient self-excitation group, and implement cross-group regulation; The feedback adjustment module is configured to obtain second data in the regulation process, construct a feedback state matrix, and update the boundary migration graph.

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