A frequency modulation method and device of a vehicle-network interaction system, a terminal device, and a storage medium

Abstract

The application discloses a frequency modulation method and device of a vehicle-to-grid interaction system, terminal equipment and a storage medium, and belongs to the field of power system automation and intelligent dispatching. The method comprises the following steps: acquiring impedance phase angle contribution sensitivity and electrical distance of each charging pile; acquiring a control strategy database; in the case that a high-frequency modulation instruction set is received and it is determined that there is a resonance risk after the high-frequency modulation instruction set is executed, intercepting the high-frequency modulation instruction set, and dividing all charging piles into several charging pile subgroups; obtaining a to-be-matched vector based on instruction type coding and a reference impedance value; taking an index primary key with the minimum Euclidean distance between the to-be-matched vector in the control strategy database as a target index primary key, and controlling inverters corresponding to each charging pile subgroup based on parameters corresponding to the target index primary key. Therefore, by implementing the application, the problem of low operation stability of the frequency modulation method of the vehicle-to-grid interaction system in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of power system automation and intelligent dispatching, and in particular to a frequency regulation method, apparatus, terminal equipment and storage medium for a vehicle-grid interaction system. Background Technology

[0002] In a vehicle-to-grid (V2G) interactive system, when the active power supply and demand of the distribution network are unbalanced, the grid operating frequency will deviate from the rated operating frequency of 50Hz, thereby generating a high-frequency frequency regulation demand for the grid. It is necessary to switch the operating state of the bidirectional grid-connected inverter in the V2G interactive system and call on the energy storage resources of a large number of grid-connected electric vehicles to quickly participate in power regulation and achieve rapid frequency correction.

[0003] Currently, the mainstream frequency regulation method for vehicle-to-grid (V2G) interactive systems is as follows: after obtaining the frequency regulation demand, the dispatching platform directly issues a globally unified mode switching command to a large number of bidirectional grid-connected inverters, causing these bidirectional grid-connected inverters to collectively and synchronously switch between "rectification and inversion" states within the same millisecond-level time window. However, due to the concentrated and synchronous switching of operating states by a large number of bidirectional grid-connected inverters, this frequency regulation method causes a drastic change in the equivalent impedance of the V2G interactive system within a very short time. This drastic change in system impedance will continuously excite the wide-frequency domain oscillation mode of the distribution network, significantly increasing the risk of wide-frequency resonance in the system. Therefore, the frequency regulation method of the existing V2G interactive system suffers from low operational stability. Summary of the Invention

[0004] This invention provides a frequency modulation method, apparatus, terminal equipment, and storage medium for a vehicle-to-everything (V2X) interactive system, which can solve the problem of low operational stability in the existing frequency modulation methods for V2X interactive systems.

[0005] The frequency modulation method for the vehicle-to-everything (V2X) interactive system provided by this invention includes: The system acquires the impedance phase angle contribution sensitivity and electrical distance of each charging pile in the vehicle-to-grid interaction system; it also acquires a preset control strategy database, which includes: the index key of each control strategy, the instruction delay time of each subgroup corresponding to each index key, and the power ramp-up rate of each subgroup. If a high-frequency modulation command set is received and it is determined that there is a risk of resonance after executing the high-frequency modulation command set, the high-frequency modulation command set is intercepted, and all charging piles are divided into several charging pile subgroups based on the electrical distance of each charging pile. Obtain the code of each instruction type in the high-frequency modulation instruction set, and based on the instruction type code and the reference impedance value of each grid-connected port in the vehicle-to-grid interaction system at each frequency, obtain the vector to be matched; The index key with the smallest Euclidean distance to the vector to be matched in the control strategy database is used as the target index key. Based on the instruction delay time of each subgroup and the power ramp-up rate of each subgroup corresponding to the target index key, the inverter corresponding to each charging pile subgroup is controlled so that each charging pile subgroup participates in power regulation at the corresponding time node according to the corresponding subgroup instruction delay time and the corresponding subgroup power ramp-up rate.

[0006] Furthermore, the control policy database is determined in the following ways: Acquire several sets of historical scheduling instruction scenarios; each set of historical scheduling instruction scenarios includes: a historical instruction type code and the historical reference impedance value of each inverter at each frequency; For each historical dispatch instruction scenario, the historical scenario vector corresponding to the historical dispatch instruction scenario is determined based on the corresponding historical instruction type code and the historical reference impedance value of each grid-connected port at each frequency. A two-dimensional path search space is constructed, and each node in the space is defined as a candidate delay strategy. The number of paths explored in parallel in a single iteration is used as the ant size of the ant colony algorithm, and the pheromone is updated based on the quality of the candidate delay strategy. The set of candidate delay strategy schemes is iteratively optimized through the ant colony algorithm until the algorithm converges, resulting in a target delay strategy scheme set, which serves as the target value of the instruction delay time for each subgroup in each historical scheduling instruction scenario. For each historical dispatch instruction scenario, the target power ramp rate of each subgroup corresponding to the historical dispatch instruction scenario is determined based on the target value of the instruction delay time of each subgroup and the preset average inverter power. A control strategy database is constructed using historical scene vectors as the primary index key in the control strategy, the target values ​​of instruction delay time for each subgroup corresponding to the historical scene vectors as the instruction delay time for each subgroup in the control strategy, and the target power ramp rate for each subgroup corresponding to the historical scene vectors as the power ramp rate for each subgroup in the control strategy.

[0007] Furthermore, for each historical scheduling instruction scenario, based on the target value of the instruction delay time for each subgroup and the preset average inverter power, the target power ramp rate for each subgroup corresponding to the historical scheduling instruction scenario is determined, including: For each subgroup in each historical scheduling instruction scenario, the target value of the subgroup instruction delay time corresponding to the subgroup is taken as the first delay time; Based on the preset ratio between the average inverter power and the first delay time, the candidate target power ramp rate of the subgroup is determined. When the candidate target power ramp rate is greater than the preset ramp rate limit, the preset ramp rate limit is used as the subgroup target power ramp rate; when the candidate target power ramp rate is less than or equal to the preset ramp rate limit, the candidate target power ramp rate is used as the subgroup target power ramp rate.

[0008] Furthermore, the presence of resonance risk after executing the high-frequency modulation instruction set is determined using the following methods: At each frequency, the reference admittance matrix is ​​obtained based on the reference admittance value corresponding to each grid-connected port in the vehicle-to-grid interaction system at the current frequency. For each high-frequency frequency modulation instruction in the high-frequency frequency modulation instruction set, the grid-connected ports corresponding to the current high-frequency frequency modulation instruction are taken as the affected grid-connected port set; the inverters corresponding to the affected grid-connected port set are taken as the affected inverter cluster; based on the penetration rate and the preset port equivalent impedance of the affected inverter cluster, the impedance change value of the affected grid-connected port at each time point at each frequency is determined. For each grid-connected port in the vehicle-to-grid interactive system, at each time point and at each frequency, the impedance change value of each high-frequency modulation command at the current frequency is converted into admittance and then accumulated to obtain the admittance accumulation value of each grid-connected port. For each grid-connected port in the vehicle-to-grid interactive system, at each frequency corresponding to each time point, the corresponding reference admittance value and the accumulated admittance value are superimposed and the reciprocal is taken to obtain the total equivalent impedance of each grid-connected port at each frequency corresponding to each time point. Based on the total equivalent impedance of each grid-connected port at each frequency at each time point, determine the combined impedance matrix at each frequency at each time point. At each frequency corresponding to each time point, the combined impedance matrix at the current frequency is decomposed into eigenvalues ​​to obtain several feature points on the complex plane. The feature point and the initial resonant pole at the current frequency are denoted as the point to be evaluated. Calculate the phase margin and impedance amplification factor for each point to be evaluated; If the phase margin value of the point to be evaluated is less than the preset phase angle safety threshold, or the impedance amplitude amplification factor value is greater than the preset amplitude safety threshold, then a resonance risk is determined to exist.

[0009] Furthermore, based on the penetration rate corresponding to the affected inverter cluster and the preset port equivalent impedance, the impedance change value of the affected grid-connected port at each time point corresponding to each frequency is determined, including: At each frequency, the target equivalent impedance value of the affected inverter cluster at each frequency is determined based on the penetration rate and the preset port equivalent impedance of the affected inverter cluster. At each frequency, starting from the reference impedance value corresponding to the affected inverter cluster at the current frequency, and ending with the target equivalent impedance value corresponding to the affected inverter cluster at the current frequency, the reference impedance value is gradually approximated to the target equivalent impedance value according to the step response law, so as to obtain the impedance change value of the affected grid-connected port corresponding to the affected inverter cluster at each time point at the current frequency.

[0010] Furthermore, based on the electrical distance of each charging station, all charging stations are divided into several subgroups, including: For each charging station, the electrical distance of the charging station is normalized to obtain the normalized electrical distance; Based on the normalized electrical distance, an unsupervised clustering algorithm is used to divide the charging piles into several subgroups.

[0011] Another embodiment of the present invention provides a frequency modulation device for a vehicle-to-grid interaction system, comprising: a data acquisition module, a charging pile subgroup determination module, a matching vector determination module, and a control module; The data acquisition module is used to acquire the impedance phase angle contribution sensitivity and electrical distance of each charging pile in the vehicle-to-grid interaction system; and to acquire the preset control strategy database. The control strategy database includes: the index key of each control strategy, the instruction delay time of each subgroup corresponding to each index key, and the power ramp-up rate of each subgroup. The charging pile subgroup determination module is used to intercept the high-frequency modulation command set when it receives the high-frequency modulation command set and determines that there is a risk of resonance after executing the high-frequency modulation command set. Based on the electrical distance of each charging pile, it divides all charging piles into several charging pile subgroups. The module for determining the vector to be matched is used to obtain the code of each instruction type in the high-frequency frequency modulation instruction set, and to obtain the vector to be matched based on the instruction type code and the reference impedance value of each grid-connected port in the vehicle-to-grid interaction system at each frequency. The control module is used to take the index primary key with the smallest Euclidean distance between the control strategy database and the vector to be matched as the target index primary key. Based on the instruction delay time of each subgroup corresponding to the target index primary key and the power ramp-up rate of each subgroup, the module controls the inverter corresponding to each charging pile subgroup so that each charging pile subgroup participates in power regulation at the corresponding time node according to the corresponding subgroup instruction delay time and the corresponding subgroup power ramp-up rate.

[0012] Furthermore, the charging pile subgroup determination module includes: a normalized electrical distance determination subunit and a division subunit; The normalized electrical distance determination subunit is used to normalize the electrical distance of each charging pile to obtain the normalized electrical distance. Sub-units are used to divide charging piles into several subgroups based on normalized electrical distance using an unsupervised clustering algorithm.

[0013] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the frequency modulation method of the vehicle-to-grid interactive system provided by the present invention.

[0014] Another embodiment of the present invention also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the frequency modulation method of the vehicle-to-grid interactive system provided by the present invention.

[0015] The following benefits can be obtained by implementing the present invention: This invention provides a frequency modulation method, apparatus, terminal device, and storage medium for a vehicle-to-grid (V2G) interactive system. The method includes: acquiring the impedance phase angle contribution sensitivity and electrical distance of each charging pile in the V2G interactive system; acquiring a preset control strategy database; the control strategy database includes: an index key for each control strategy, the instruction delay time of each subgroup corresponding to each index key, and the power ramp-up rate of each subgroup; upon receiving a high-frequency modulation instruction set and determining that there is a resonance risk after executing the high-frequency modulation instruction set, intercepting the high-frequency modulation instruction set, and dividing all charging piles into several charging stations based on the electrical distance of each charging pile. The system obtains the code for each instruction type in the high-frequency modulation instruction set. Based on the instruction type code and the reference impedance value of each grid-connected port in the vehicle-to-grid interaction system at each frequency, it obtains the vector to be matched. The system uses the index key with the smallest Euclidean distance from the vector to be matched in the control strategy database as the target index key. Based on the instruction delay time of each subgroup corresponding to the target index key and the power ramp-up rate of each subgroup, the system controls the inverter corresponding to each charging pile subgroup so that each charging pile subgroup participates in power regulation at the corresponding time node according to the corresponding subgroup instruction delay time and the corresponding subgroup power ramp-up rate.

[0016] Compared to existing technologies that directly issue high-frequency modulation commands, this invention, upon receiving a high-frequency modulation command set, first determines the resonance risk based on the reference impedance value, reference admittance value, and initial resonant pole, and intercepts the execution of the command set if resonance risk exists. Based on this, and considering impedance phase contribution sensitivity and electrical distance, the charging piles are divided into a preset number of subgroups. Subsequently, the index primary key corresponding to the vector to be matched is retrieved from the control strategy database, and a corresponding command delay time and power ramp-up rate are matched for each subgroup, thereby controlling the inverters of each subgroup. Because differentiated command delay times and power ramp-up rates are matched for each charging pile subgroup, the inverters do not perform switching simultaneously, and power changes are controlled by the corresponding power ramp-up rate, avoiding drastic impedance changes caused by synchronous switching, thus preventing wideband resonance and improving the operational stability of the frequency modulation method in existing vehicle-to-grid interaction systems. Attached Figure Description

[0017] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a frequency modulation method for a vehicle-to-grid interactive system according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the frequency modulation device of a vehicle-to-grid interactive system provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0021] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0024] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0025] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0026] See Figure 1 To address the issue of low operational stability in existing frequency modulation methods for vehicle-to-grid (V2G) interactive systems, an embodiment of the present invention provides a frequency modulation method for a V2G interactive system, comprising: S101: Obtain the impedance phase angle contribution sensitivity and electrical distance of each charging pile in the vehicle-to-grid interaction system; obtain the preset control strategy database; the control strategy database includes: the index key of each control strategy, the instruction delay time of each subgroup corresponding to each index key, and the power ramp-up rate of each subgroup. Furthermore, the control policy database is determined in the following ways: Acquire several sets of historical scheduling instruction scenarios; each set of historical scheduling instruction scenarios includes: a historical instruction type code and the historical reference impedance value of each inverter at each frequency; For each historical dispatch instruction scenario, the historical scenario vector corresponding to the historical dispatch instruction scenario is determined based on the corresponding historical instruction type code and the historical reference impedance value of each grid-connected port at each frequency. A two-dimensional path search space is constructed, and each node in the space is defined as a candidate delay strategy. The number of paths explored in parallel in a single iteration is used as the ant size of the ant colony algorithm, and the pheromone is updated based on the quality of the candidate delay strategy. The set of candidate delay strategy schemes is iteratively optimized through the ant colony algorithm until the algorithm converges, resulting in a target delay strategy scheme set, which serves as the target value of the instruction delay time for each subgroup in each historical scheduling instruction scenario. For each historical dispatch instruction scenario, the target power ramp rate of each subgroup corresponding to the historical dispatch instruction scenario is determined based on the target value of the instruction delay time of each subgroup and the preset average inverter power. A control strategy database is constructed using historical scene vectors as the primary index key in the control strategy, the target values ​​of instruction delay time for each subgroup corresponding to the historical scene vectors as the instruction delay time for each subgroup in the control strategy, and the target power ramp rate for each subgroup corresponding to the historical scene vectors as the power ramp rate for each subgroup in the control strategy.

[0027] Furthermore, for each historical scheduling instruction scenario, based on the target value of the instruction delay time for each subgroup and the preset average inverter power, the target power ramp rate for each subgroup corresponding to the historical scheduling instruction scenario is determined, including: For each subgroup in each historical scheduling instruction scenario, the target value of the subgroup instruction delay time corresponding to the subgroup is taken as the first delay time; Based on the preset ratio between the average inverter power and the first delay time, the candidate target power ramp rate of the subgroup is determined. When the candidate target power ramp rate is greater than the preset ramp rate limit, the preset ramp rate limit is used as the subgroup target power ramp rate; when the candidate target power ramp rate is less than or equal to the preset ramp rate limit, the candidate target power ramp rate is used as the subgroup target power ramp rate.

[0028] It should be noted that the historical instruction type code is used to uniquely identify the inverter mode switching type of each subgroup corresponding to the historical dispatch instruction scenario. The inverter mode switching types include two types: rectification to inversion and inversion to rectification. Rectification to inversion refers to the inverter corresponding to the charging pile switching from the rectifier operation mode of charging the grid to the inverter operation mode of feeding power to the grid. This is mainly used in scenarios where the grid has insufficient active power. Inversion to rectification refers to the inverter corresponding to the charging pile switching from the inverter operation mode of feeding power to the grid to the rectifier operation mode of absorbing power from the grid. This is mainly used in scenarios where the grid has excess active power. The historical reference impedance value refers to the steady-state equivalent impedance value of each grid-connected inverter of the vehicle-grid interaction system in the historical dispatch instruction scenario, under all preset key frequency points of the distribution network, before the mode switching is executed. The steady-state equivalent impedance value is a broadband impedance parameter under the steady-state operation condition of the grid, which can fully characterize the inherent broadband impedance characteristics of the current distribution network.

[0029] Preferably, for each historical dispatch instruction scenario, based on the corresponding historical instruction type code and the historical reference impedance value of each grid-connected port at each frequency, the historical scenario vector corresponding to the historical dispatch instruction scenario is determined, including: For each historical dispatch instruction scenario, firstly, a dimensionally complete impedance feature dataset is constructed using the historical reference impedance value of each grid-connected port at each frequency; then, principal component analysis (PCA) is performed on the impedance feature dataset to reduce dimensionality, remove data redundancy and invalid noise, select and extract the top d principal components with the highest contribution rates, and reconstruct a low-dimensional, highly representative d-dimensional impedance feature vector, where d takes the value of 2 to 5, and the historical instruction type code is formatted. Specifically, the historical instruction type encoding is standardized into a fixed-length standard integer string format to eliminate the problem of inconsistent encoding formats. Next, each floating-point component of the d-dimensional impedance feature vector is uniformly preserved with a fixed decimal precision (e.g., three decimal places), discarding any extra decimal places. Then, string concatenation is performed according to a fixed order rule: the first part is fixedly concatenated with the formatted historical instruction type encoding string, and the subsequent parts are strictly concatenated with the formatted impedance feature vector components of the 1st dimension, 2nd dimension, up to the dth dimension, according to the principal component sorting order extracted by principal component analysis. The concatenation order and data format are fixed throughout the process, ultimately generating a historical scene vector with a uniform format, fixed length, and unique sorting.

[0030] It should be noted that the first dimension of the two-dimensional path search space is the index of the historical scene vector, and each coordinate in the first dimension corresponds to a historical scene vector. The second dimension is the control decision dimension, with the asynchronous delay time of the instructions of each charging pile subgroup as a variable search variable. Each coordinate in the second dimension corresponds to a certain instruction delay time of each subgroup. For example, different coordinates in the first dimension can correspond to historical scene vectors A, B, and C, respectively. Among them, historical scene vector A corresponds to: the inverter mode of subgroup 1 is rectifier-to-inverter, the inverter mode of subgroup 2 is rectifier-to-inverter, and the inverter mode of subgroup 3 is... The mode is inverter-to-rectification, and the d-dimensional impedance feature vector constructed from the historical reference impedance values ​​of each inverter at each frequency is 'a'. Historical scenario vector B corresponds to: the inverter mode in subgroup 1 is inverter-to-rectification, the inverter mode in subgroup 2 is inverter-to-rectification, and the inverter mode in subgroup 3 is rectification-to-inverting. The d-dimensional impedance feature vector constructed from the historical reference impedance values ​​of each inverter at each frequency is 'b'. Historical scenario vector C corresponds to: the inverter mode in subgroup 1 is inverter-to-rectification, the inverter mode in subgroup 2 is rectification-to-inverting, and the inverter mode in subgroup 3 is inverter-to-rectification. The rectification, and the d-dimensional impedance eigenvector constructed from the historical reference impedance values ​​of each inverter at each frequency, is c; exemplarily, the different coordinates of the second dimension correspond to: the command delay time x of each subgroup, the command delay time y of each subgroup, and the command delay time z of each subgroup, wherein the command delay time x of each subgroup includes: the command delay time of subgroup 1 is 10ms, the command delay time of subgroup 2 is 20ms, and the command delay time of subgroup 3 is 30ms; the command delay time y of each subgroup includes: the command delay time of subgroup 1 is 20ms, the command delay time of subgroup 2 is 30ms, and the command delay time of subgroup 3 is 40ms. The instruction delay time z for each subgroup includes: 30ms for subgroup 1, 40ms for subgroup 2, and 50ms for subgroup 3. In other words, the instruction delay time for each subgroup actually includes a combination of delay times for different subgroups. The instruction delay time can be selected from 0 to 500ms. Based on this, any node in the two-dimensional path search space is a complete candidate scheme for delay strategy composed of historical scene vectors and subgroup delay times. The ant colony algorithm iteratively optimizes in this space to find the optimal delay time combination that fits each historical working condition.

[0031] Specifically, the number of paths explored in parallel during a single iteration is used as the ant size in the ant colony algorithm, and the pheromone update is driven by the merits of the delay strategy candidate schemes. The ant colony algorithm iteratively optimizes the set of delay strategy candidate schemes until the algorithm converges, resulting in a target delay strategy scheme set. This set includes: first, pre-setting basic parameters such as the artificial ant population size and the maximum number of iterations; and constructing a fitness function that includes an equivalent impedance step smoothness parameter. The fitness function is used to quantitatively evaluate the comprehensive performance of each delay strategy candidate scheme. The equivalent impedance step smoothness parameter is used to quantitatively evaluate the smoothness of the equivalent impedance change in the active distribution network after implementing a certain delay strategy, and it is defined as: (1) in, (2) in, The equivalent impedance step smoothness parameter, This represents the total change in equivalent impedance when all underlying inverters synchronously perform mode switching at the same time, expressed in ohms. This indicates the total time span of the asynchronous switching, in seconds. The subgroup instruction delay time for the i-th independent execution subgroup (i.e., the inverter subgroup) is expressed in milliseconds (ms). Construct a delay policy vector, Indicates the total number of independently executing subgroups; Indicates the delay strategy The time-domain trajectory function of the equivalent impedance of all inverters in an active distribution network. The denominator of formula (1) represents the absolute value of the instantaneous slope of the equivalent impedance time-domain trajectory. The physical meaning of the denominator is the average slope of the equivalent impedance change over the entire asynchronous switching time span. The larger the value, the more gradual the change in equivalent impedance per unit time (i.e., the same total impedance change is distributed over a longer period of time), the smoother the impedance step, and the lower the risk of broadband resonance. The time required for a single subgroup to complete a mode switch should be noted. It should be noted that the total time span of asynchronous switching refers to the total duration from the initiation of mode switching by the first independent execution subgroup to the completion of all mode switching operations by the last independent execution subgroup. The equivalent impedance time-domain trajectory function... The complete form is The time-domain trajectory function of equivalent impedance characterizes the evolution of equivalent impedance over time at different frequencies. The determination method is as follows: For the same grid-connected port and the same frequency, the total equivalent impedance at each moment is organized in chronological order (the acquisition of the total equivalent impedance can be found in section S102 regarding the acquisition of the total equivalent impedance), forming the time-domain trajectory of the equivalent impedance of that grid-connected port at that frequency; the time-domain trajectories corresponding to all grid-connected ports and all frequencies are summarized to obtain a complete dataset of the equivalent impedance time-domain trajectory. The method for determining the total change in equivalent impedance when all underlying inverters synchronously perform mode switching at the same moment is: let all If the value is 0, substitute it into the equivalent impedance time-domain trajectory function. In this process, we obtain the total equivalent impedance change when all the underlying inverters synchronously perform mode switching at the same time.

[0032] Secondly, a set of delay parameters for the independently executing subgroups are used as nodes in the search space. These delay nodes from different scenarios are then concatenated to form complete feasible paths. Each complete path corresponds to a set of delay policy vectors across all scenarios, and the pheromone concentration for each feasible path is initialized. Each artificial ant selects a delay combination scheme based on pheromone concentration and heuristic information, generating several candidate delay policies. In the t-th iteration, for ant k currently located at node u in the search space, the probability of it transitioning to node v is: (3) in, Let ant k be the ant currently located at node u in the search space, and let it have the probability of moving to node v. This represents the pheromone concentration on the path from node u to node v at the t-th iteration, initialized to a constant. , A value of 0.1-0.5 is acceptable. This is a heuristic factor, whose value is F, which is the fitness function value of the delay policy corresponding to node v. For pheromone-inspired factor weights, For the expected heuristic factor weights, , The values ​​for all values ​​are between 1 and 3. Let be the set of feasible neighboring nodes of ant k at node u. This represents the index variable used to traverse the nodes within the feasible neighborhood set.

[0033] Next, for each candidate delay strategy, the corresponding equivalent impedance time-domain trajectory function is solved, and the equivalent impedance step smoothness parameter is calculated according to formulas (1)-(2). Substituting this parameter into the fitness function, the fitness value of the scheme is obtained. The fitness function is constructed as follows: (4) in, For the fitness function, This is the smoothness weighting coefficient, with a value ranging from 0.5 to 1.0. The equivalent impedance step smoothness parameter, This is the delay penalty coefficient, with a value ranging from 0.05 to 0.3. The subgroup instruction delay time for the i-th independent execution subgroup (i.e., the inverter subgroup) is expressed in milliseconds (ms). Construct a delay policy vector, This represents the total number of independent execution subgroups; i represents the index of the independent execution subgroup. This represents the frequency modulation importance weight of the i-th independent execution subgroup, with a value ranging from 0 to 1, obtained by normalizing the device penetration rate of this subgroup.

[0034] Then, the merits of the candidate solutions are evaluated based on their fitness scores: higher fitness indicates a better overall effect of the delay strategy in suppressing impedance mutations; after each iteration, the pheromone concentration is updated according to the following rules, while a pheromone evaporation mechanism is introduced to avoid the algorithm getting trapped in local optima: (5) in, This indicates the updated pheromone concentration. This represents the pheromone evaporation coefficient, with a value ranging from 0.05 to 0.15. Let represent the pheromone concentration on the path from node u to node v at the t-th iteration. This indicates the size of the artificial ant population, ranging from 20 to 50. This represents the pheromone increment left by ant k on the path (u,v), and its value is related to the ant's pheromone level. The fitness function value of the current iteration path is proportional to the fitness function value.

[0035] The algorithm iteratively executes candidate strategy generation, fitness evaluation, and pheromone update processes. It is determined that the algorithm has converged when the preset maximum number of iterations is reached, or when the change in the fitness function value of the globally optimal path is less than a preset convergence threshold within several consecutive iteration cycles. Finally, the set of delay vectors with the best fitness during the iteration process is extracted to obtain the target delay strategy scheme set. Preferably, the maximum number of iterations is between 100 and 500.

[0036] For example, a two-dimensional search space is constructed: the first dimension contains 8 historical scheduling instruction scenario vectors; for each historical scheduling instruction scenario, the second dimension sets 20 optional delay policy nodes, each node corresponding to a set of switching delay time combinations of 4 independent execution subgroups. The number of artificial ants is initialized to M=30, the maximum number of iterations is set to 200, and the convergence threshold is set to 1%. Each ant traverses all 8 scheduling scenarios in turn, selects a delay policy node from the 20 feasible nodes corresponding to each scenario according to the state transition probability of equation (3), and connects them to form a complete path covering all scenarios; for the delay policy vector corresponding to each scenario in the path, the equivalent impedance time domain trajectory function is solved, the equivalent impedance step smoothness parameter is calculated according to equations (1) and (2), and substituted into equation (4) to obtain the corresponding fitness value. After all ants have been searched in one iteration, the path pheromone concentration is updated according to equation (5). The iteration continues until the number of iterations reaches 200, or the change in fitness of the global optimal path is less than 1% in 8 consecutive iterations. The algorithm is then judged to have converged. The path with the highest fitness after convergence is extracted, and the path is segmented and decoded to obtain the instruction delay time corresponding to the 4 independent execution subgroups under each historical scheduling instruction scenario, thus forming a set of target delay strategy schemes.

[0037] Preferably, determining the candidate target power ramp-up rate of the subgroup based on the preset ratio between the average inverter power and the first delay time includes: using the ratio between the average inverter power and the first delay time as the candidate target power ramp-up rate of the subgroup.

[0038] It should be noted that the electrical distance is calculated as follows: The physical architecture data of the active power distribution network topology is obtained, including the resistance and reactance of each line segment; all line segments traversed from the i-th charging node to the point of common coupling are determined, and the sum of the resistance and reactance of all line segments along this path is accumulated; the squares of the sum of resistance and reactance are taken separately, then added together, and the square root of the sum is taken. The resulting value is the electrical distance from the charging node to the point of common coupling. Obtaining the electrical distance is a conventional method for calculating impedance distance in this field, and it can quantitatively characterize the strength of the electrical coupling between the charging node and the point of common coupling. It should be noted that in this invention, one charging pile corresponds to one charging node.

[0039] S102: Upon receiving a high-frequency modulation command set and determining that there is a risk of resonance after executing the high-frequency modulation command set, intercept the high-frequency modulation command set and divide all charging piles into several charging pile subgroups based on the electrical distance of each charging pile. Furthermore, the presence of resonance risk after executing the high-frequency modulation instruction set is determined using the following methods: At each frequency, the reference admittance matrix is ​​obtained based on the reference admittance value corresponding to each grid-connected port in the vehicle-to-grid interaction system at the current frequency. For each high-frequency frequency modulation instruction in the high-frequency frequency modulation instruction set, the grid-connected ports corresponding to the current high-frequency frequency modulation instruction are taken as the affected grid-connected port set; the inverters corresponding to the affected grid-connected port set are taken as the affected inverter cluster; based on the penetration rate and the preset port equivalent impedance of the affected inverter cluster, the impedance change value of the affected grid-connected port at each time point at each frequency is determined. For each grid-connected port in the vehicle-to-grid interactive system, at each time point and at each frequency, the impedance change value of each high-frequency modulation command at the current frequency is converted into admittance and then accumulated to obtain the admittance accumulation value of each grid-connected port. For each grid-connected port in the vehicle-to-grid interactive system, at each frequency corresponding to each time point, the corresponding reference admittance value and the accumulated admittance value are superimposed and the reciprocal is taken to obtain the total equivalent impedance of each grid-connected port at each frequency corresponding to each time point. Based on the total equivalent impedance of each grid-connected port at each frequency at each time point, determine the combined impedance matrix at each frequency at each time point. At each frequency corresponding to each time point, the combined impedance matrix at the current frequency is decomposed into eigenvalues ​​to obtain several feature points on the complex plane. The feature point and the initial resonant pole at the current frequency are denoted as the point to be evaluated. Calculate the phase margin and impedance amplification factor for each point to be evaluated; If the phase margin value of the point to be evaluated is less than the preset phase angle safety threshold, or the impedance amplitude amplification factor value is greater than the preset amplitude safety threshold, then a resonance risk is determined to exist.

[0040] Furthermore, at each frequency, based on the penetration rate and preset port equivalent impedance of the affected inverter cluster, the target equivalent impedance value of the affected inverter cluster at each frequency is determined. At each frequency, starting from the reference impedance value corresponding to the affected inverter cluster at the current frequency, and ending with the target equivalent impedance value corresponding to the affected inverter cluster at the current frequency, the reference impedance value is gradually approximated to the target equivalent impedance value according to the step response law, so as to obtain the impedance change value of the affected grid-connected port corresponding to the affected inverter cluster at each time point at the current frequency.

[0041] Furthermore, based on the electrical distance of each charging station, all charging stations are divided into several subgroups, including: For each charging station, the electrical distance of the charging station is normalized to obtain the normalized electrical distance; Based on the normalized electrical distance, an unsupervised clustering algorithm is used to divide the charging piles into several subgroups.

[0042] Specifically, the reference impedance value of each grid-connected port at each frequency is determined as follows: Voltage and current time-domain waveform sampling data corresponding to the passive network of the distribution network are collected. First, wavelet threshold denoising is used to remove Gaussian white noise from the waveforms, obtaining a high signal-to-noise ratio electrical waveform sequence. Then, a digital bandpass filter is used for filtering, with the specified frequency band range set to 100Hz to 10kHz, to extract the effective transient waveform data within this frequency band. The filtered voltage and current time-domain signals are converted to the frequency domain, and the complex impedance corresponding to each discrete frequency point is calculated, forming a set of frequency domain impedance points. Using frequency as the independent variable, interpolation fitting is performed on the set of frequency domain impedance points in ascending frequency order to complete the impedance values ​​of continuous frequency points within the frequency range. Combining the port characteristics of the multi-port network, the self-impedance and mutual impedance between each grid-connected port are solved based on the fitted frequency domain impedance data, thereby determining the reference impedance value corresponding to each grid-connected port at each frequency.

[0043] Specifically, the following methods are used to determine whether there is a risk of resonance after executing the high-frequency modulation instruction set: First, at each frequency, based on the reference admittance value corresponding to each grid-connected port in the vehicle-to-grid interaction system at the current frequency, a reference admittance matrix is ​​obtained for each frequency. Specifically, a passive network impedance model is established based on the distribution network topology, line impedance, and fixed load parameters of the vehicle-to-grid interaction system; for each frequency to be analyzed, the port impedance between each grid-connected port is solved to form a reference impedance matrix; then, each element of the reference impedance matrix is ​​converted into admittance form to obtain the reference admittance matrix. (6) in, This represents the element in the i-th row and j-th column of the reference admittance matrix at the current frequency. This represents the element in the i-th row and j-th column of the reference impedance matrix at the current frequency. The reference impedance matrix consists only of passive networks such as power distribution lines and fixed loads, and does not include the influence of inverters controlled by frequency regulation commands.

[0044] For each high-frequency frequency modulation instruction in the high-frequency frequency modulation instruction set, the grid-connected ports corresponding to the current high-frequency frequency modulation instruction are taken as the affected grid-connected port set; the inverters corresponding to the affected grid-connected port set are taken as the affected inverter cluster; based on the penetration rate and preset port equivalent impedance of the affected inverter cluster, the impedance change value of the affected grid-connected port at each time point at each frequency is determined.

[0045] Specifically, firstly, based on the instruction matching the target charge / discharge mode identifier k of the underlying inverter and the equipment penetration rate r, a static combined impedance equation is constructed: (7) in, This represents the target equivalent impedance value of the affected inverter cluster at each frequency. This indicates the penetration rate of the affected inverter cluster. It should be noted that since the penetration rate of each high-frequency modulation command is uniform, the penetration rate of the affected inverter cluster is uniform across all frequencies. An index indicating the charge / discharge mode identifier. This represents the Thevenin equivalent impedance in mode k. This represents the Norton equivalent admittance under mode k. It should be noted that the Thevenin equivalent impedance under mode k is a preset port equivalent impedance parameter, obtained by: pre-establishing a parameter library of Norton and Thevenin equivalent models for the underlying inverter under various charge / discharge modes. The parameters in this library are stored in association with control bandwidth parameters such as the inverter's current loop bandwidth and voltage loop bandwidth. Based on the target charge / discharge mode identifier, the Thevenin equivalent impedance and Norton equivalent admittance for the corresponding mode are retrieved from the parameter library. After obtaining the target equivalent impedance value, a time-domain step function characterizing the inverter's synchronous response speed is introduced to describe the transient response process during mode switching. (8) in, Represents the time-domain step function. Represents a time variable. This represents the synchronization response time constant, which is the reciprocal of the inverter current loop bandwidth. This function describes the transient response process of the inverter cluster to reach the target equivalent impedance in synchronous mode.

[0046] Substituting the time-domain step function into the static combined impedance equation, we can obtain the time-varying dynamic combined impedance: (9) in, This indicates the initial equivalent impedance of the entire port of the affected inverter cluster before the command is executed. It should be noted that... The method of obtaining is as follows: before receiving the new high-frequency modulation command, read the current charging and discharging mode and current penetration rate of the affected inverter cluster; use the same static combined impedance equation as formula (7) to solve the initial impedance.

[0047] Then, to Performing a Fourier transform to complete the dynamic frequency domain mapping yields the port impedance at each time and frequency. This impedance is the impedance change of the affected grid-connected port at each time point corresponding to each frequency.

[0048] For each grid-connected port in the vehicle-to-grid (V2G) interactive system, at each time point and at each frequency, the impedance change value of each high-frequency modulation command at the current frequency is converted into admittance and accumulated to obtain the accumulated admittance value of each grid-connected port. The admittance change corresponding to a single command satisfies: (10) in, At the current frequency, at time The impedance change value, This represents the corresponding admittance value. When multiple frequency modulation commands are applied to the same grid connection port, the corresponding changes in admittance are directly summed.

[0049] For each grid-connected port in the vehicle-to-grid (V2G) interactive system, at each frequency at each time point, the corresponding reference admittance value and the accumulated admittance value are superimposed, and the reciprocal is taken to obtain the total equivalent impedance of each grid-connected port at each frequency at each time point. The elements of the total admittance matrix after superposition of all ports satisfy: (11) in, For each frequency, the total admittance matrix element, The reference admittance value at each frequency, Let represent the impedance change of the affected grid-connected port at each time point corresponding to each frequency. The formula for calculating the total equivalent impedance from the total admittance is as follows: (12) in, The total equivalent impedance at each frequency The total admittance matrix elements are defined for each frequency. Then, based on the total equivalent impedance of each grid-connected port at each time point and at each frequency, the combined impedance matrix at each frequency and at each time point is determined. Eigenvalue decomposition is then performed on the combined impedance matrix at the current frequency for each time point. (13) in, For a moment ,frequency The corresponding number 1 eigenvalue, For the corresponding feature vector, For a moment The combined impedance matrix has eigenvalues ​​whose coordinates on the complex plane correspond to several characteristic points on the complex plane. The real part of the eigenvalue represents the equivalent resistance, and the imaginary part represents the equivalent reactance. Next, the characteristic points and the initial resonant pole at the current frequency are recorded as the points to be evaluated. The initial resonant pole is obtained by scanning the current frequency range. Based on the reference impedance matrix containing only passive networks, if the imaginary part of the impedance crosses zero at a certain frequency point, and the impedance amplitude in the neighborhood of that frequency exhibits a local peak characteristic of first increasing and then decreasing, then the coordinate point on the complex plane corresponding to that frequency is the initial resonant pole. Next, the phase margin and impedance amplitude amplification factor are calculated for each point to be evaluated. If the phase margin of a point to be evaluated is less than a preset phase safety threshold, or the impedance amplitude amplification factor is greater than a preset amplitude safety threshold, then a resonance risk is identified. It should be noted that the eigenvalue decomposition operation refers to decoupling the combined impedance matrix in the form of a second-order square matrix at any time and frequency into a combination of an eigenvector matrix and a diagonal eigenvalue matrix, thereby realizing the decoupling analysis of the system's high-order coupled impedance network. Through decomposition, the multi-port coupled resonance characteristics can be transformed into complex feature points of several independent modes, each feature point corresponding to a resonant mode of the system, thus enabling a quantitative assessment of high-frequency resonance risk. Preferably, the preset phase angle safety threshold can be taken in the range of 30° to 45°, and the preset amplitude safety threshold can be taken in the range of 3dB to 6dB.

[0050] It should be noted that the phase margin is calculated as follows: Each point to be evaluated corresponds to a resonant mode. Based on this mode, a frequency domain response curve of the modal impedance as a function of frequency is constructed, including the amplitude-frequency curve (impedance amplitude changes with frequency) and the phase-frequency curve (impedance phase changes with frequency). Within the analysis frequency band, the frequency corresponding to the modal impedance amplitude being equal to the unit amplitude is searched and denoted as the gain crossover frequency. The phase corresponding to the modal impedance at the gain crossover frequency is read, and 180° is added to the phase value to obtain the phase margin.

[0051] The impedance amplitude amplification factor is determined as follows: Search for the frequency corresponding to a modal impedance phase of -180° within the analysis frequency band, and record it as the phase critical frequency; read the modal impedance amplitude corresponding to the phase critical frequency, and this amplitude is the impedance amplitude amplification factor.

[0052] Specifically, based on normalized electrical distance, an unsupervised clustering algorithm is used to divide charging piles into several subgroups. This includes: treating each charging pile as an independent sample, using the normalized electrical distance corresponding to each charging pile as a single clustering feature to construct a charging pile cluster sample dataset; pre-setting the range of the number of cluster subgroups and the clustering convergence threshold, and automatically searching for the optimal cluster center based on the distribution characteristics of the sample data without manual labeling intervention; calculating the distance between each charging pile sample and each cluster center, and assigning each charging pile sample to the subgroup corresponding to the nearest cluster center; after each round of allocation, updating the cluster center position of the corresponding subgroup based on the mean normalized electrical distance of all samples within each subgroup; repeating the sample allocation and cluster center update steps until the cluster center offset is less than the preset convergence threshold or the maximum number of iterations is reached, at which point the clustering iteration ends; after iterative convergence, all charging piles are automatically grouped, resulting in several charging pile subgroups with similar electrical coupling characteristics and concentrated electrical distance distributions. Preferably, the unsupervised clustering algorithm includes the DBSCAN algorithm, K-means algorithm, etc.

[0053] S103: Obtain the code of each instruction type in the high-frequency modulation instruction set, and obtain the matching vector based on the instruction type code and the reference impedance value of each grid-connected port in the vehicle-to-grid interaction system at each frequency; Specifically, a dimensionally complete impedance feature dataset is constructed using the historical reference impedance values ​​of each inverter at each frequency. First, a dimensionally complete impedance feature dataset is constructed using the historical reference impedance values ​​of each grid-connected port at each frequency. Then, principal component analysis (PCA) is performed on the impedance feature dataset to reduce dimensionality, remove data redundancy and invalid noise, and select and extract the top d principal components with the highest contribution rates. This reconstructs a low-dimensional, highly representative d-dimensional impedance feature vector, where d ranges from 2 to 5. Finally, the instruction type encoding is formatted; specifically, the instruction type encoding is standardized to a fixed number of bits. The integer string format eliminates the problem of inconsistent encoding formats. Next, each floating-point component of the d-dimensional impedance feature vector is uniformly formatted, retaining a fixed decimal precision (e.g., three decimal places) and discarding unnecessary decimal places. Then, string concatenation is performed according to a fixed order rule: the first part is concatenated with the formatted instruction type encoded string, and the subsequent parts are concatenated with the formatted impedance feature vector components of the 1st dimension, 2nd dimension, up to the dth dimension, strictly following the sorting order of the principal components extracted by principal component analysis. The concatenation order and data format are fixed throughout the process, finally generating a vector with a uniform format, fixed length, and unique sorting, which serves as the matching vector.

[0054] S104: Take the index primary key with the smallest Euclidean distance between the control strategy database and the vector to be matched as the target index primary key. Based on the instruction delay time of each subgroup corresponding to the target index primary key and the power ramp-up rate of each subgroup, control the inverter corresponding to each charging pile subgroup so that each charging pile subgroup participates in power regulation at the corresponding time node according to the corresponding subgroup instruction delay time and the corresponding subgroup power ramp-up rate.

[0055] Specifically, the similarity matching calculation is performed one by one between the vector to be matched and each historical scenario vector stored in the control strategy database. This scheme uses Euclidean distance as the matching criterion, traversing all historical scenario vectors corresponding to the index primary keys in the control strategy database, calculating the Euclidean distance between each historical scenario vector and the current vector to be matched, and selecting the index primary key corresponding to the historical scenario with the smallest Euclidean distance, which is then determined as the target index primary key. The instruction delay time and power ramp-up rate of each subgroup adapted to the current operating condition are obtained through the target index primary key. For each charging pile subgroup that has been divided, the corresponding subgroup instruction delay time is used as the action trigger time to realize the staggered delayed start of inverter power adjustment actions between different subgroups; at the same time, within the time node of each subgroup trigger adjustment, the power rise and fall adjustment is steadily completed strictly according to the corresponding subgroup power ramp-up rate.

[0056] It should be noted that regarding the correspondence between the command delay time and power ramp-up rate of each subgroup and which charging pile subgroup they belong to, this invention automatically assigns a unique identifier to each charging pile subgroup after dividing all charging piles into several subgroups. For example, if there are 3 charging pile subgroups, they are recorded as G1, G2, and G3 respectively. In the control strategy database, the storage format of the command delay time and power ramp-up rate of each subgroup is as follows: {G1:{Delay T1, Ramp-up Rate R1}, G2:{Delay T2, Ramp-up Rate R2}, G3:{Delay T3, Ramp-up Rate R3}}. This ensures a one-to-one correspondence between the charging pile subgroups and the command delay time and power ramp-up rate of each subgroup. It should also be noted that the number of control strategies included in the control strategy database of this invention is the same as the number of subgroups of the charging pile subgroups.

[0057] Specifically, the subgroup instruction delay time of each subgroup is formatted as a global absolute execution timestamp. This is achieved by adding the subgroup instruction delay time to the current absolute time, which is then used as the global absolute execution timestamp. This timestamp is injected into the timestamp field of the switching instruction's message packet, and the power ramp-up rate is written numerically into the power adjustment parameter field of the switching instruction. After this processing, each switching instruction, along with its attached global absolute execution timestamp and power ramp-up rate, constitutes a microinstruction unit. All microinstruction units are then encapsulated using a timing protocol according to the order of their global absolute execution timestamps. The encapsulated protocol data packets are then distributed to the corresponding inverters according to the subgroup.

[0058] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; One embodiment of the present invention provides a frequency modulation device for a vehicle-to-grid interaction system, comprising: a data acquisition module 201, a charging pile subgroup determination module 202, a vector to be matched determination module 203, and a control module 204; The data acquisition module 201 is used to acquire the impedance phase angle contribution sensitivity and electrical distance of each charging pile in the vehicle-to-grid interaction system; and to acquire the preset control strategy database. The control strategy database includes: the index key of each control strategy, the instruction delay time of each subgroup corresponding to each index key, and the power ramp-up rate of each subgroup. The charging pile subgroup determination module 202 is used to intercept the high-frequency modulation command set when it receives the high-frequency modulation command set and determines that there is a risk of resonance after executing the high-frequency modulation command set, and divide all charging piles into several charging pile subgroups based on the electrical distance of each charging pile. The matching vector determination module 203 is used to obtain the code of each instruction type in the high-frequency frequency modulation instruction set, and obtain the matching vector based on the instruction type code and the reference impedance value of each grid-connected port in the vehicle-to-grid interaction system at each frequency; The control module 204 is used to take the index primary key with the smallest Euclidean distance between the control strategy database and the vector to be matched as the target index primary key, and control the inverter corresponding to each charging pile subgroup based on the instruction delay time of each subgroup and the power ramp-up rate of each subgroup corresponding to the target index primary key, so that each charging pile subgroup participates in power regulation at the corresponding time node according to the corresponding subgroup instruction delay time and the corresponding subgroup power ramp-up rate.

[0059] Furthermore, the charging pile subgroup determination module 202 includes: a normalized electrical distance determination subunit and a division subunit; The normalized electrical distance determination subunit is used to normalize the electrical distance of each charging pile to obtain the normalized electrical distance. Sub-units are used to divide charging piles into several subgroups based on normalized electrical distance using an unsupervised clustering algorithm.

[0060] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the frequency modulation method of the vehicle-to-grid interactive system provided by any of the above-described method embodiments of the present invention.

[0061] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0062] Based on the above-described embodiments of the frequency tuning method for the vehicle-to-grid interactive system, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the frequency tuning method for the vehicle-to-grid interactive system of any embodiment of the present invention.

[0063] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0064] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0065] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0066] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the frequency modulation method of the vehicle-to-grid interactive system described in any of the above-described method embodiments of the present invention.

[0067] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0068] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A frequency modulation method for a vehicle-to-everything (V2X) interactive system, characterized in that, include: Acquire the impedance phase angle contribution sensitivity and electrical distance of each charging pile in the vehicle-to-grid interaction system; Retrieve the preset control strategy database; The control strategy database includes: the primary key of each control strategy, the instruction delay time of each subgroup corresponding to each primary key, and the power ramp-up rate of each subgroup; If a high-frequency modulation command set is received and it is determined that there is a risk of resonance after executing the high-frequency modulation command set, the high-frequency modulation command set is intercepted, and all charging piles are divided into several charging pile subgroups based on the electrical distance of each charging pile. Obtain the code of each instruction type in the high-frequency modulation instruction set, and obtain the matching vector based on the instruction type code and the reference impedance value of each grid-connected port in the vehicle-to-grid interaction system at each frequency; The index key with the smallest Euclidean distance to the vector to be matched in the control strategy database is used as the target index key. Based on the instruction delay time of each subgroup and the power ramp-up rate of each subgroup corresponding to the target index key, the inverter corresponding to each charging pile subgroup is controlled so that each charging pile subgroup participates in power regulation at the corresponding time node according to the corresponding subgroup instruction delay time and the corresponding subgroup power ramp-up rate.

2. The frequency modulation method of the vehicle-to-grid interactive system as described in claim 1, characterized in that, The control policy database is determined in the following ways: Acquire several sets of historical scheduling instruction scenarios; each set of historical scheduling instruction scenarios includes: a historical instruction type code and the historical reference impedance value of each inverter at each frequency; For each historical dispatch instruction scenario, the historical scenario vector corresponding to the historical dispatch instruction scenario is determined based on the corresponding historical instruction type code and the historical reference impedance value of each grid-connected port at each frequency. A two-dimensional path search space is constructed, and each node in the space is defined as a candidate delay strategy. The number of paths explored in parallel in a single iteration is used as the ant size of the ant colony algorithm, and the pheromone is updated based on the quality of the candidate delay strategy. The set of candidate delay strategy schemes is iteratively optimized through the ant colony algorithm until the algorithm converges, resulting in a target delay strategy scheme set, which serves as the target value of the instruction delay time for each subgroup in each historical scheduling instruction scenario. For each historical dispatch instruction scenario, the target power ramp rate of each subgroup corresponding to the historical dispatch instruction scenario is determined based on the target value of the instruction delay time of each subgroup and the preset average inverter power. A control strategy database is constructed using historical scene vectors as the primary index key in the control strategy, the target values ​​of instruction delay time for each subgroup corresponding to the historical scene vectors as the instruction delay time for each subgroup in the control strategy, and the target power ramp rate for each subgroup corresponding to the historical scene vectors as the power ramp rate for each subgroup in the control strategy.

3. The frequency modulation method of the vehicle-to-grid interactive system as described in claim 2, characterized in that, For each historical scheduling instruction scenario, based on the target delay time value of each subgroup instruction and the preset average inverter power, the target power ramp rate for each subgroup corresponding to the historical scheduling instruction scenario is determined, including: For each subgroup in each historical scheduling instruction scenario, the target value of the subgroup instruction delay time corresponding to the subgroup is taken as the first delay time; Based on the preset ratio between the average inverter power and the first delay time, the candidate target power ramp rate of the subgroup is determined. When the candidate target power ramp rate is greater than the preset ramp rate limit, the preset ramp rate limit is used as the subgroup target power ramp rate; when the candidate target power ramp rate is less than or equal to the preset ramp rate limit, the candidate target power ramp rate is used as the subgroup target power ramp rate.

4. The frequency modulation method of the vehicle-to-grid interactive system as described in claim 3, characterized in that, The following methods can be used to determine whether there is a risk of resonance after executing a high-frequency modulation instruction set: At each frequency, based on the reference admittance value corresponding to each grid-connected port in the vehicle-to-grid interaction system at the current frequency, the reference admittance matrix at each frequency is obtained; For each high-frequency frequency modulation instruction in the high-frequency frequency modulation instruction set, the grid-connected ports corresponding to the current high-frequency frequency modulation instruction are taken as the set of affected grid-connected ports; The inverters corresponding to the affected grid-connected port set are considered as the affected inverter cluster. Based on the penetration rate of the affected inverter cluster and the preset port equivalent impedance, determine the impedance change value of the affected grid-connected port at each time point at each frequency. For each grid-connected port in the vehicle-to-grid interactive system, at each time point and at each frequency, the impedance change value of each high-frequency modulation command at the current frequency is converted into admittance and then accumulated to obtain the admittance accumulation value of each grid-connected port. For each grid-connected port in the vehicle-to-grid interactive system, at each frequency corresponding to each time point, the corresponding reference admittance value and the accumulated admittance value are superimposed and the reciprocal is taken to obtain the total equivalent impedance of each grid-connected port at each frequency corresponding to each time point. Based on the total equivalent impedance of each grid-connected port at each frequency at each time point, determine the combined impedance matrix at each frequency at each time point. At each frequency corresponding to each time point, the combined impedance matrix at the current frequency is decomposed into eigenvalues ​​to obtain several feature points on the complex plane. The feature point and the initial resonant pole at the current frequency are denoted as the point to be evaluated. Calculate the phase margin and impedance amplification factor for each point to be evaluated; If the phase margin value of the point to be evaluated is less than the preset phase angle safety threshold, or the impedance amplitude amplification factor value is greater than the preset amplitude safety threshold, then a resonance risk is determined to exist.

5. The frequency modulation method of the vehicle-to-grid interactive system as described in claim 4, characterized in that, Based on the penetration rate of the affected inverter cluster and the preset port equivalent impedance, the impedance change value of the affected grid-connected port at each time point at each frequency is determined, including: At each frequency, the target equivalent impedance value of the affected inverter cluster at each frequency is determined based on the penetration rate and the preset port equivalent impedance of the affected inverter cluster. At each frequency, starting from the reference impedance value corresponding to the affected inverter cluster at the current frequency, and ending with the target equivalent impedance value corresponding to the affected inverter cluster at the current frequency, the reference impedance value is gradually approximated to the target equivalent impedance value according to the step response law, so as to obtain the impedance change value of the affected grid-connected port corresponding to the affected inverter cluster at each time point at the current frequency.

6. The frequency modulation method of the vehicle-to-grid interactive system as described in claim 5, characterized in that, Based on the electrical distance of each charging station, all charging stations are divided into several subgroups, including: For each charging station, the electrical distance of the charging station is normalized to obtain the normalized electrical distance; Based on the normalized electrical distance, an unsupervised clustering algorithm is used to divide the charging piles into several subgroups.

7. A frequency modulation device for a vehicle-to-everything (V2X) interactive system, characterized in that, include: The system includes a data acquisition module, a charging pile subgroup determination module, a matching vector determination module, and a control module. The data acquisition module is used to acquire the impedance phase angle contribution sensitivity and electrical distance of each charging pile in the vehicle-to-grid interaction system; and to acquire the preset control strategy database. The control strategy database includes: the index key of each control strategy, the instruction delay time of each subgroup corresponding to each index key, and the power ramp-up rate of each subgroup. The charging pile subgroup determination module is used to intercept the high-frequency modulation command set when it receives the high-frequency modulation command set and determines that there is a risk of resonance after executing the high-frequency modulation command set. Based on the electrical distance of each charging pile, it divides all charging piles into several charging pile subgroups. The module for determining the vector to be matched is used to obtain the code of each instruction type in the high-frequency frequency modulation instruction set, and to obtain the vector to be matched based on the instruction type code and the reference impedance value of each grid-connected port in the vehicle-to-grid interaction system at each frequency. The control module is used to take the index primary key with the smallest Euclidean distance between the control strategy database and the vector to be matched as the target index primary key. Based on the instruction delay time of each subgroup corresponding to the target index primary key and the power ramp-up rate of each subgroup, the module controls the inverter corresponding to each charging pile subgroup so that each charging pile subgroup participates in power regulation at the corresponding time node according to the corresponding subgroup instruction delay time and the corresponding subgroup power ramp-up rate.

8. The frequency modulation device for the vehicle-to-grid interactive system as described in claim 7, characterized in that, The charging pile subgroup determination module includes: a normalized electrical distance determination subunit and a division subunit; The normalized electrical distance determination subunit is used to normalize the electrical distance of each charging pile to obtain the normalized electrical distance. Sub-units are used to divide charging piles into several subgroups based on normalized electrical distance using an unsupervised clustering algorithm.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the frequency modulation method of the vehicle-to-everything (V2X) interactive system as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the frequency modulation method of the vehicle-to-everything (V2X) interactive system as described in any one of claims 1-6.

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