Method for evaluating potential of electric vehicle v2g adjustment based on electricity price regulation

Abstract

This invention discloses a method for evaluating the regulation potential of electric vehicles (EVs) in V2G based on electricity price regulation, specifically relating to the field of power storage collaborative control technology. It addresses the grid resonance risk and battery lifespan reduction caused by power synchronization issues in existing V2G centralized dispatching. The method generates an initial instruction set by real-time fusion of dynamic electricity price time-segmentation signals and grid frequency deviation values, combined with a battery health status decay coefficient to impose dual constraints on the charging and discharging power range. Furthermore, it extracts the grid resonance frequency distribution range based on frequency sweep testing, generates asymmetric phase disturbance rules for the dominant resonance frequency points, and disrupts the harmonic superposition effect of power output through differentiated time-series offsets. During the instruction execution phase, it dynamically adjusts the phase disturbance amplitude to adapt to grid frequency fluctuations and battery aging, forming a closed-loop control mechanism. This achieves synergistic optimization of grid stability and equipment lifespan, improving the safety of EV clusters participating in grid regulation.

Description

Technical Field

[0001] This invention relates to the field of power energy storage collaborative control technology, and more specifically, to a method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation. Background Technology

[0002] In the field of power system energy storage, electric vehicles participating in grid load regulation through vehicle-to-grid (V2G) technology has become an important development direction. In existing technologies, grid operators issue charging and discharging incentive signals to users through dynamic electricity pricing mechanisms and predict the dispatchable capacity of electric vehicle clusters based on user response models in order to formulate charging and discharging plans to achieve load balancing. Such methods usually rely on economic optimization algorithms. By using aggregation platforms, the dispersed electric vehicle batteries are centrally controlled as distributed regulation resources of large-scale cross-regional power grids, providing flexibility support for ultra-high voltage transmission scenarios.

[0003] However, when a large number of electric vehicles synchronously perform charging and discharging operations under the drive of electricity price signals, the time synchronization of their power output is prone to interact with the frequency dynamic characteristics of the power grid (especially involving the interconnection oscillation mode of AC transmission systems above 750 kV), resulting in low-frequency power oscillations in the regional power grid. This threatens the stability of the large-scale power grid security and defense system, making it impossible to safely release the theoretical regulation potential of electric vehicle clusters. In severe cases, it may trigger the action of protection devices, weaken the reliability of intelligent dispatching, and exacerbate the risk of load imbalance. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The method for assessing the V2G regulation potential of electric vehicles based on electricity price regulation includes the following steps:

[0007] S1. Obtain the frequency deviation value of the power grid in the target area and the dynamic electricity price time period division signal;

[0008] S2. Synchronously collect battery parameters of each electric vehicle battery, including available capacity and health status parameters;

[0009] S3. Based on the dynamic electricity price time period division signal and battery parameters, calculate the maximum schedulable charging and discharging power range of the electric vehicle cluster in each time period, and generate the initial charging and discharging power instruction set.

[0010] S4. Collect the impedance spectrum characteristics of the power grid and extract the resonant frequency distribution range of the power grid in the target area;

[0011] S5. Within the execution time window of the initial charging and discharging power command set, generate an adaptive phase disturbance rule that matches the resonant frequency of the power grid based on the resonant frequency distribution interval.

[0012] S6. Based on the adaptive phase perturbation rule, the power output timing of the initial charge and discharge power command set is adjusted asymmetrically to generate the corrected command set.

[0013] S7. Based on the modified instruction set, control the bidirectional power converter to perform the modified charging and discharging operation, and dynamically adjust the offset amplitude of the phase disturbance rule.

[0014] In a preferred embodiment, S1 includes:

[0015] The frequency deviation value of the power grid in the target area is collected in real time by the power grid monitoring device. The frequency deviation value is the difference between the current frequency and the rated frequency of the power grid.

[0016] The communication module receives dynamic electricity price time period division signals released by the power grid operator. The dynamic electricity price time period division signals include the timestamps of peak and valley electricity price periods and the corresponding electricity price incentive levels.

[0017] The frequency deviation value and the dynamic electricity price time period segmentation signal are time-synchronized and aligned to generate a power grid status dataset with consistent timestamps.

[0018] In a preferred embodiment, S2 includes:

[0019] The on-board battery monitoring device collects the current remaining power value and battery health status degradation coefficient of each electric vehicle battery in real time. The current remaining power value is the percentage of the battery's current usable capacity to its nominal capacity. The battery health status degradation coefficient is calculated by the number of battery cycles and the rate of change of internal resistance.

[0020] The data collected by each vehicle battery monitoring device is time-stamped using a time synchronization protocol to generate a battery parameter dataset with consistent timestamps. The battery parameter dataset includes the current remaining charge value and battery health status degradation coefficient of each electric vehicle battery.

[0021] In a preferred embodiment, S3 includes:

[0022] Based on the dynamic electricity price time period segmentation signal in the power grid status dataset, the start timestamp and electricity price incentive level of each time period are determined.

[0023] Based on the current remaining battery power value in the battery parameter dataset, calculate the upper and lower limits of the initial charging and discharging power for each electric vehicle in each time period.

[0024] The initial charge and discharge power limits are constrained a second time based on the battery health state decay coefficient to generate the health state corrected charge and discharge power range for each vehicle.

[0025] The health status correction charging and discharging power range of all electric vehicles within the same time period is sorted by priority according to the electricity price incentive level, with the higher the electricity price incentive level, the higher the vehicle is ranked.

[0026] Based on the priority ranking results, the charging and discharging power ranges of each vehicle are superimposed to generate the maximum schedulable charging and discharging power range of the cluster for the corresponding time period.

[0027] Generate an initial charge and discharge power instruction set, which includes a structured data table containing the start timestamp of each time period, the maximum schedulable charge and discharge power range of the cluster, and vehicle identification codes.

[0028] In a preferred embodiment, S4 includes:

[0029] Frequency sweep test signals are injected into the power grid of the target area through power grid monitoring devices to collect voltage and current response data of each node in the power grid;

[0030] The impedance spectrum characteristics of the power grid are calculated based on voltage and current response data. The impedance spectrum characteristics include the impedance amplitude and phase angle at each frequency point.

[0031] Based on the curves of impedance amplitude and phase angle changing with frequency, identify the frequency bands corresponding to impedance amplitudes below the first preset threshold and phase angles crossing zero, and extract them as resonant frequency distribution intervals.

[0032] Adjacent frequency bands in the resonant frequency distribution range are clustered and merged. The merging condition is that the difference in impedance amplitude between adjacent frequency bands is less than a second preset threshold and the phase angle changes in the same direction.

[0033] Based on the merged resonant frequency distribution range, calculate the oscillation risk weighting coefficient for each frequency band. The oscillation risk weighting coefficient is the product of the frequency band width and the impedance amplitude attenuation slope.

[0034] The frequency band with the highest oscillation risk weight coefficient is determined as the dominant resonant frequency, and the bandwidth corresponding to the dominant resonant frequency is the effective suppression range of the resonant frequency distribution interval.

[0035] In a preferred embodiment, S5 includes:

[0036] The execution time window of the initial charge and discharge power instruction set is divided into multiple continuous sub-windows, and the length of the sub-window is dynamically adjusted according to the bandwidth of the dominant resonant frequency point in the effective suppression range of the resonant frequency distribution interval.

[0037] The phase disturbance amplitude benchmark value for each sub-window is generated based on the oscillation risk weight coefficient of the dominant resonant frequency. The phase disturbance amplitude benchmark value is positively correlated with the oscillation risk weight coefficient.

[0038] The phase perturbation direction of each sub-window is determined based on the sign of the phase angle change direction in the resonant frequency distribution interval.

[0039] A random phase disturbance factor is generated within the sub-window based on the phase disturbance amplitude reference value and direction. The amplitude of the random phase disturbance factor fluctuates around the phase disturbance amplitude reference value and does not exceed the preset fluctuation range.

[0040] The random phase disturbance factors of each sub-window are arranged in chronological order to generate an adaptive phase disturbance rule that matches the resonant frequency of the power grid.

[0041] In a preferred embodiment, S6 includes:

[0042] Analyze the timestamp, the reference value of the phase perturbation amplitude, and the direction of the phase perturbation in the adaptive phase perturbation rule;

[0043] The timestamps of each instruction in the initial charge and discharge power instruction set are asymmetrically offset according to the phase disturbance direction. The offset direction is either advancing or delaying the timestamp, and the offset amount is the reference value of the phase disturbance amplitude of the sub-window to which the corresponding timestamp belongs.

[0044] The difference between the adjusted timestamp and the original timestamp shall not exceed the hardware response delay threshold of the bidirectional power converter;

[0045] The power output timing is reconstructed based on the adjusted timestamps to generate a corrected charging and discharging power instruction set.

[0046] In a preferred embodiment, S7 includes:

[0047] The dynamic adjustment coefficient for phase disturbance amplitude is calculated based on the weighted sum of the frequency deviation value and the battery health status attenuation coefficient. The weighting weights are the frequency deviation value weight and the health status weight.

[0048] The phase disturbance amplitude benchmark value in the adaptive phase disturbance rule is scaled proportionally based on the phase disturbance amplitude dynamic adjustment coefficient to generate the updated phase disturbance rule.

[0049] The bidirectional power converter is controlled to perform charging and discharging operations according to the revised charging and discharging power command set, while the updated phase disturbance rule replaces the original rule.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. After the initial instruction set is generated, this invention dynamically injects an asymmetric timing offset during the instruction execution phase based on the resonant frequency distribution range extracted by frequency sweep testing. This offset is inversely correlated with the phase characteristics of the dominant resonant frequency point. By disrupting the periodic synchronization of the power waveform, it actively weakens the harmonic superposition effect. Compared with the global symmetric disturbance or passive filtering design in traditional methods, this invention provides targeted compensation for the actual impedance characteristics of the power grid, eliminating the resonance triggering condition while maintaining the economy of the scheduling plan.

[0052] 2. When calculating the maximum dispatchable power, the battery cycle count and internal resistance change rate are combined into a health state attenuation coefficient, which serves as a dynamic constraint on the charge and discharge power range. During the phase disturbance amplitude adjustment stage, the attenuation coefficient is further weighted and fused with the grid frequency deviation, so that the disturbance amplitude responds to real-time grid state changes while being limited by the degree of battery aging. This design enables high-health batteries to prioritize the response to regulation needs during periods of high electricity prices, while the charge and discharge power of low-health batteries is automatically limited within a safe threshold, achieving synergistic optimization of grid stability and equipment lifespan, and improving the safety of electric vehicle clusters participating in grid regulation. Attached Figure Description

[0053] Figure 1 This is a flowchart of the method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation, as proposed in this invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0055] Example: Figure 1 This invention presents a method for evaluating the V2G regulation potential of electric vehicles based on electricity price control, which includes the following steps:

[0056] S1. Obtain the frequency deviation value of the power grid in the target area and the dynamic electricity price time period division signal;

[0057] S2. Synchronously collect battery parameters of each electric vehicle battery, including available capacity and health status parameters;

[0058] S3. Based on the dynamic electricity price time period division signal and battery parameters, calculate the maximum schedulable charging and discharging power range of the electric vehicle cluster in each time period, and generate the initial charging and discharging power instruction set.

[0059] S4. Collect the impedance spectrum characteristics of the power grid and extract the resonant frequency distribution range of the power grid in the target area;

[0060] S5. Within the execution time window of the initial charging and discharging power command set, generate an adaptive phase disturbance rule that matches the resonant frequency of the power grid based on the resonant frequency distribution interval.

[0061] S6. Based on the adaptive phase perturbation rule, the power output timing of the initial charge and discharge power command set is adjusted asymmetrically to generate the corrected command set.

[0062] S7. Based on the modified instruction set, control the bidirectional power converter to perform the modified charging and discharging operation, and dynamically adjust the offset amplitude of the phase disturbance rule.

[0063] S1. Obtain the frequency deviation value and dynamic electricity price time period division signal of the target area power grid, including:

[0064] The frequency deviation of the power grid in the target area is collected in real time by a power grid monitoring device. This monitoring device is a synchronous phasor measurement unit deployed at key nodes of the power grid in the target area. The synchronous phasor measurement unit measures the current frequency of the power grid at a preset sampling rate and outputs the difference between the current frequency and the rated frequency as the frequency deviation value. The rated frequency of the power grid is set according to the operating standards of the power grid in the target area; for example, in a power grid using a 50Hz standard, the rated frequency is 50Hz.

[0065] The system receives dynamic electricity price time-sharing signals from the power grid operator via a communication module. This communication module is a hardware interface supporting standard power system communication protocols. The dynamic electricity price time-sharing signals are transmitted by the power grid operator's main station system via wired or wireless communication networks. The signal data format includes, but is not limited to, JSON or XML, and includes timestamps for peak and off-peak electricity price periods and corresponding price incentive levels. The timestamps for peak and off-peak electricity price periods are generated based on a standard time format, and the price incentive levels are divided into multiple levels; for example, peak periods correspond to high price incentive levels, and off-peak periods correspond to low price incentive levels.

[0066] The frequency deviation value and the dynamic electricity price time period segmentation signal are time-synchronized and aligned to generate a grid status dataset with consistent timestamps. The time synchronization alignment process is implemented using a standard time synchronization protocol, aligning the acquisition timestamp of the frequency deviation value with the release timestamp of the dynamic electricity price time period segmentation signal to a preset precision. The aligned grid status dataset is structured data containing timestamps, frequency deviation values, and electricity price incentive levels. The timestamps are unified time identifiers after alignment, the frequency deviation values ​​are real-time data output by the synchronized phasor measurement unit, and the electricity price incentive levels are parameters corresponding to the timestamps in the dynamic electricity price time period segmentation signal.

[0067] The hardware deployment of the power grid monitoring device and communication module is located within the same power facility, ensuring extremely low latency in physical signal transmission. The measurement accuracy of the synchronization phasor measurement unit meets power system monitoring standards, and the update interval of the dynamic electricity price time period segmentation signal matches the power grid dispatch cycle. The execution frequency of time synchronization alignment processing is consistent with the power grid status update frequency, ensuring that the power grid status dataset reflects the power grid operating status in real time.

[0068] The structured data is stored on a non-volatile storage device on the local power grid server, and the storage format includes, but is not limited to, tables or databases. The power grid status dataset is associated with the charging and discharging power instruction set in subsequent steps through a timestamp field. The association method is implemented based on time window matching logic, and the length of the time window is adapted to the frequency of power grid status updates.

[0069] S2. Synchronously collect battery parameters for each electric vehicle battery. These parameters include available capacity and health status parameters, including:

[0070] The on-board battery monitoring device collects the current remaining capacity and battery health degradation coefficient of each electric vehicle battery in real time. This device, a hardware unit integrated into the electric vehicle battery management system, measures the real-time voltage and current data of the battery using voltage and current sensors, and calculates the current remaining capacity based on this data. The current remaining capacity is a percentage of the battery's current usable capacity to its nominal capacity. The nominal capacity is the maximum capacity specified at the time of manufacture, for example, a battery model with a nominal capacity of 60 kWh.

[0071] The battery health status degradation coefficient is calculated using the battery cycle count and internal resistance change rate. The battery cycle count is the cumulative number of complete charge-discharge cycles since the battery was manufactured, calculated from the historical data recording unit of the battery management system. The internal resistance change rate is the ratio of the battery's current internal resistance to its initial internal resistance. The internal resistance is measured using the AC impedance spectroscopy method, with a measurement frequency range of 1 Hz to 1 kHz. The battery health status degradation coefficient is calculated by weighting the battery cycle count and the internal resistance change rate. The weighting coefficient is set according to the battery type; for example, the cycle count weighting coefficient is higher than the internal resistance change rate weighting coefficient for lithium iron phosphate batteries.

[0072] The data collected by each vehicle battery monitoring device is timestamped using a time synchronization protocol. The time synchronization protocol is either a Global Positioning System (GPS) time signal or a Network Time Protocol (NAT). The built-in clock chip in the vehicle battery monitoring device receives the synchronization signal and converts the local timestamps of the collected data into a unified standard time. Specifically, the timestamp alignment process involves sorting the current remaining battery capacity and battery health status degradation coefficient data of each electric vehicle battery according to the standard timestamps, and filling in missing time points with interpolated data. The interpolation method is either linear interpolation or nearest neighbor interpolation.

[0073] Generate a battery parameter dataset with consistent timestamps. This dataset is a structured table containing four fields: standard timestamp, vehicle identification code, current remaining battery capacity, and battery health degradation coefficient. The vehicle identification code is a unique identifier for each electric vehicle, such as a vehicle VI N code or license plate number. The structured table is stored on a cloud server or a local edge computing device's non-volatile memory, in a database table or CSV file format. The primary key of the database table is a combination of the standard timestamp and the vehicle identification code.

[0074] The structured data table is associated with the power grid status dataset generated in step S1 through a standard timestamp field, and the association method is equal-interval matching based on the same timestamp. The vehicle identification code field is used to distinguish the charging and discharging power commands of different electric vehicles in subsequent steps. For example, when generating the initial charging and discharging power command set, an independent dispatchable power value is assigned to each electric vehicle based on the vehicle identification code.

[0075] The weighting coefficients for the battery health state degradation coefficient are determined through experimental calibration based on the battery type. The experimental calibration method involves conducting aging tests on batteries of the same model in a laboratory environment, statistically analyzing the contribution ratios of the number of cycles and the rate of change of internal resistance to capacity degradation. For example, the weighting coefficient for the number of cycles is 0.7 for a certain model of ternary lithium battery, and the weighting coefficient for the rate of change of internal resistance is 0.3. These weighting coefficients are stored in the read-only memory of the battery management system for real-time calculation and retrieval.

[0076] The vehicle identification code is obtained by reading the VIN code stored in the vehicle controller local area network bus through the on-board diagnostic interface, or by the user manually entering the license plate number. The on-board diagnostic interface is the OBD-II standard interface, supporting the CAN bus communication protocol. The license plate number manually entered by the user is recorded through the touch screen of the on-board human-machine interface or through voice recognition. The voice recognition function is based on a pre-trained acoustic model, which is a general-purpose voice recognition model based on deep neural networks.

[0077] The association results between the battery parameter dataset and the grid status dataset are stored in an associated database. Each record in the associated database contains six fields: standard timestamp, frequency deviation value, electricity price incentive level, vehicle identification code, current remaining battery capacity, and battery health status degradation coefficient. Queries in the associated database are based on timestamp ranges or vehicle identification codes using indexes, with the index type being either a B-tree or a hash index. Access permissions to the associated database are managed through a role-based control mechanism, which tiers read and write permissions based on user identity. For example, grid operators have read and write permissions, while electric vehicle users only have read permissions.

[0078] S3. Based on the dynamic electricity price time-segmentation signal and battery parameters, calculate the maximum schedulable charging and discharging power range of the electric vehicle cluster in each time period, and generate an initial charging and discharging power instruction set, including:

[0079] Based on the dynamic electricity price time period segmentation signal in the power grid status dataset, the start timestamp and electricity price incentive level for each time period are determined. The dynamic electricity price time period segmentation signal is structured data released by the power grid operator that includes the time range of peak and valley electricity prices and the corresponding electricity price levels. For example, the peak period is from 10:00 to 14:00 every day, corresponding to the first-level electricity price incentive level, and the flat period is from 14:00 to 18:00 every day, corresponding to the second-level electricity price incentive level.

[0080] Based on the current remaining battery charge value in the battery parameter dataset, the upper and lower limits of the initial charge / discharge power for each electric vehicle in each time period are calculated. The current remaining battery charge value is the percentage of the battery's current available capacity to its nominal capacity. The upper and lower limits of the initial charge / discharge power are obtained by multiplying the current remaining battery charge value by the battery's rated charge / discharge power. For example, if an electric vehicle has a current remaining battery charge of 80% and a rated charge / discharge power of 10 kW, then the upper limit of the initial charge power is 8 kW, and the lower limit of the discharge power is -8 kW. If the frequency deviation value in the grid status dataset exceeds a preset threshold, the upper and lower limits of the initial charge / discharge power are dynamically corrected by reducing the power range proportionally to the frequency deviation.

[0081] The initial charge and discharge power limits are constrained a second time based on the battery health state decay coefficient, generating the health state-corrected charge and discharge power range for each vehicle.

[0082] The specific method of secondary constraint is to multiply the initial charging and discharging power upper and lower limits by the health state correction coefficient, which is 1-(Cmax / Chs), where Cmax is the preset maximum value of the battery health state decay coefficient, and Chs is the battery health state decay coefficient.

[0083] The health status correction charging and discharging power range of all electric vehicles within the same time period is prioritized according to the electricity price incentive level. The priority ranking rule is that the higher the electricity price incentive level, the higher the vehicle is ranked; within the same electricity price incentive level, the vehicle with the lower battery health status degradation coefficient is ranked higher. For example, vehicles during peak hours (Level 1 electricity price incentive) are ranked before vehicles during off-peak hours (Level 2 electricity price incentive), and within the same electricity price level, vehicles with a health status degradation coefficient of 50 are ranked before vehicles with a health status degradation coefficient of 70.

[0084] Based on the priority ranking, the charging and discharging power ranges of each vehicle are superimposed to generate the maximum schedulable charging and discharging power range for the corresponding time period. The superposition method involves adding the charging and discharging power ranges of each vehicle sequentially until the capacity limit threshold of the power grid line is reached. The capacity limit threshold of the power grid line is obtained through power grid monitoring devices. For example, if the rated capacity of a line is 1000 kW, the superposition stops when the total power approaches 1000 kW, and the remaining vehicles are marked as unschedulable. If the cumulative power of all vehicles in the current time period does not exceed the capacity limit threshold, the maximum schedulable charging and discharging power range of the cluster is the sum of the power ranges of all vehicles.

[0085] An initial charging and discharging power instruction set is generated. This instruction set is a structured data table containing the start timestamps of each time period, the maximum schedulable charging and discharging power range of the cluster, and vehicle identification codes. The structured data table fields include the start timestamp, the upper limit of schedulable power, the lower limit of schedulable power, a list of vehicle identification codes, and corresponding power allocation values. The vehicle identification code list is a unique set of vehicle codes sorted by priority, and the power allocation value is the actual allowed charging and discharging power value for each vehicle within that time period. For example, if the start timestamp of a certain time period is 2023-10-01 10:00:00, the maximum schedulable charging and discharging power range of the cluster is [-500kW, +500kW], the vehicle identification code list is [VIN001, VIN002, VIN003], and the corresponding power allocation value is [-100kW, -200kW, -200kW].

[0086] The structured data tables are stored in either CSV files or relational database tables. The CSV files have column headers for "Start Timestamp," "Upper Limit of Dispatchable Power," "Lower Limit of Dispatchable Power," "List of Vehicle Identifier Codes," and "Power Allocation Value," with each record corresponding to a dispatch instruction for a specific time period. The relational database tables use the start timestamp as the primary key and foreign keys linked to timestamp fields in the grid status dataset and battery parameter dataset. Data storage employs a data validation mechanism, with validation rules including checking whether the dispatchable power range exceeds grid line capacity limits, whether vehicle identifier codes are duplicated or invalid, and whether the power allocation value is within the corrected charging / discharging power range for the vehicle's health status.

[0087] The preset maximum value Cmax of the health status correction coefficient is set according to the battery type. For example, Cmax is 1000 for lithium iron phosphate batteries and 800 for ternary lithium batteries. Cmax is determined by laboratory aging tests. The test method is to conduct accelerated cycle charge and discharge experiments on batteries of the same model, record the number of cycles and the rate of change of internal resistance when the battery capacity decays to 80%, and calculate the corresponding Chs value as Cmax. The experimental data is stored in the read-only memory of the battery management system for real-time calculation and retrieval.

[0088] The capacity limitation threshold of the power grid lines is obtained in real time through power grid monitoring devices. These monitoring devices are smart meters deployed in substations or distribution cabinets. The smart meters measure the real-time current and voltage of the lines through current transformers and voltage transformers. The line capacity limitation threshold is calculated based on the line's rated current and voltage using the formula: Pmax = Vrated × Irated × cosφ. Where Pmax is the line capacity limitation threshold, Vrated is the line's rated voltage, Irated is the line's rated current, and cosφ is the power factor, which is obtained in real time through the power grid monitoring devices.

[0089] The power allocation value is determined proportionally based on the vehicle priority ranking. For example, if the maximum dispatchable power range of the cluster during a certain time period is [-500kW, +500kW], and the total power demand of the top 3 priority vehicles is -600kW, then the power allocation value for each vehicle is proportionally compressed to (500 / 600) × the original power demand. If the vehicle's power demand does not exceed the capacity limit, the original power demand is used. The calculation frequency of the power allocation value is consistent with the grid dispatch cycle, for example, updated every 15 minutes.

[0090] The invalid data handling mechanism includes automatic correction and manual intervention. Automatic correction forcibly adjusts power allocation values ​​exceeding the vehicle's health-state corrected charging / discharging power range to the upper or lower limit. For example, if a vehicle's power allocation value is -120kW, but its health-state corrected discharge power lower limit is -100kW, it will be automatically corrected to -100kW. Manual intervention is implemented through the grid dispatcher's interface, which displays a list of abnormal data and provides a manual correction option. Corrected data requires secondary verification before storage.

[0091] S4. Collect the impedance spectrum characteristics of the power grid and extract the resonant frequency distribution range of the power grid in the target area, including:

[0092] A frequency sweep test signal is injected into the target area power grid through a power grid monitoring device. The frequency range of the sweep test signal covers the low-frequency to high-frequency band, for example, from 1 Hz to 2 kHz. The signal amplitude is limited to no more than 5% of the grid's rated voltage to avoid interfering with the normal operation of the power grid. The power grid monitoring device collects voltage and current response data from each node of the power grid through voltage transformers and current transformers. The measurement accuracy of the voltage transformers meets power system standards, for example, the error does not exceed 0.2%, and the measurement range of the current transformers covers 1% to 150% of the grid's rated current.

[0093] The impedance spectrum characteristics of the power grid are calculated based on voltage and current response data. The impedance spectrum characteristics include the impedance amplitude and phase angle at each frequency point. The impedance amplitude is the ratio of the voltage amplitude to the current amplitude, and the phase angle is the difference between the voltage phase and the current phase. The impedance amplitude is calculated by dividing the voltage amplitude at a given frequency point by the current amplitude at the same frequency point. For example, if the voltage amplitude is 220 volts and the current amplitude is 10 amperes at a given frequency point, the impedance amplitude is 22 ohms. The phase angle is calculated by subtracting the current phase from the voltage phase. For example, if the voltage phase is 30 degrees and the current phase is 15 degrees, the phase angle is 15 degrees. The calculation results are stored as a three-dimensional data table of frequency-impedance-phase angle. The frequency resolution of the data table is 1 Hz, the unit of impedance amplitude is ohms, and the unit of phase angle is degrees.

[0094] Based on the curves showing the change in impedance amplitude and phase angle with frequency, the frequency bands corresponding to impedance amplitudes below a first preset threshold and phase angle zero-crossing points are identified. The first preset threshold is set as a percentage of the rated impedance of the power grid; for example, if the rated impedance is 10 ohms, the first preset threshold is 2 ohms. A phase angle zero-crossing point is the critical frequency point where the phase angle changes from a positive value to a negative value or vice versa. For example, if the phase angle is +5 degrees at 50 Hz and -3 degrees at 60 Hz, then a phase angle zero-crossing point is determined to exist between 50 Hz and 60 Hz. Continuous frequency bands satisfying the above conditions are extracted as resonant frequency distribution intervals. For example, if the impedance amplitude of the frequency band 50 Hz-70 Hz is found to be below 2 ohms and a phase angle zero-crossing point exists, then this frequency band is marked as a resonant frequency distribution interval.

[0095] Adjacent frequency bands within the resonant frequency distribution range are clustered and merged. The merging condition is that the impedance amplitude difference between adjacent frequency bands is less than a second preset threshold and the phase angle changes in the same direction. The second preset threshold is set as a percentage of the first preset threshold; for example, if the first preset threshold is 2 ohms, the second preset threshold is 0.2 ohms. The impedance amplitude difference is calculated as the absolute difference in impedance amplitude at the endpoints of adjacent frequency bands. For example, if the impedance amplitude at the ending frequency of frequency band A is 1.8 ohms and the impedance amplitude at the starting frequency of frequency band B is 1.7 ohms, the difference is 0.1 ohms. Consistent phase angle changes mean that the phase angle of adjacent frequency bands both decrease or increase with increasing frequency. For example, if the phase angle of frequency band A decreases from +10 degrees to -5 degrees and the phase angle of frequency band B decreases from -5 degrees to -15 degrees, then the phase angle changes in the same direction. The clustered and merged frequency bands are the union of the frequency ranges of the original adjacent frequency bands. For example, frequency bands 50 Hz-60 Hz and 60 Hz-70 Hz are merged into 50 Hz-70 Hz.

[0096] Based on the merged resonant frequency distribution range, the oscillation risk weighting coefficient for each frequency band is calculated. The oscillation risk weighting coefficient is the product of the band width and the impedance amplitude attenuation slope. The band width is the difference between the band's ending frequency and its starting frequency; for example, the width of a 50 Hz-70 Hz band is 20 Hz. The impedance amplitude attenuation slope is the average rate of decrease in impedance amplitude with frequency within the band. It is calculated by subtracting the ending frequency impedance amplitude from the starting frequency impedance amplitude, and then dividing by the band width. For example, if the starting frequency impedance amplitude is 1.8 ohms, the ending frequency is 1.2 ohms, and the band width is 20 Hz, then the attenuation slope is (1.8 - 1.2) / 20 = 0.03 ohms / Hz. The oscillation risk weighting coefficient is 20 Hz × 0.03 ohms / Hz = 0.6.

[0097] The frequency band with the highest oscillation risk weighting coefficient is determined as the dominant resonant frequency. The bandwidth corresponding to the dominant resonant frequency is the effective suppression range of the resonant frequency distribution interval. For example, if the oscillation risk weighting coefficient of a certain frequency band is 0.8, which is higher than 0.6 and 0.4 of other frequency bands, then this frequency band is determined to be the dominant resonant frequency, and its effective suppression range is the frequency span of this frequency band, such as 50 Hz to 70 Hz. The effective suppression range is used in subsequent steps to limit the frequency boundary of the perturbation effect when generating phase perturbation rules, for example, applying asymmetric timing offset adjustment within the 50 Hz to 70 Hz range.

[0098] S5. Within the execution time window of the initial charge / discharge power command set, generate an adaptive phase disturbance rule that matches the grid resonant frequency point based on the resonant frequency distribution interval, including:

[0099] The execution time window of the initial charge / discharge power command set is divided into multiple consecutive sub-windows. The length of each sub-window is dynamically adjusted based on the bandwidth of the dominant resonant frequency within the effective suppression range of the resonant frequency distribution interval. The dynamic adjustment rule is: the narrower the bandwidth, the shorter the sub-window length; the wider the bandwidth, the longer the sub-window length. The specific ratio is predetermined through experimental calibration. For example, when the bandwidth is 20 Hz, the sub-window length is set to 1 second; when the bandwidth is 50 Hz, the sub-window length is extended to 2.5 seconds. The start time of the time window segmentation is aligned with the start timestamp of the dynamic electricity price period division signal to ensure that the sub-windows are synchronized with the grid dispatch cycle.

[0100] The phase disturbance amplitude baseline value for each sub-window is generated based on the oscillation risk weighting coefficient at the dominant resonant frequency. The phase disturbance amplitude baseline value is positively correlated with the oscillation risk weighting coefficient, and the specific mapping relationship is achieved through a lookup table. In the preset table of the lookup method, the oscillation risk weighting coefficient is divided into multiple levels, each level corresponding to a baseline value range. For example, a weighting coefficient of 0.7 to 1.0 corresponds to a baseline value of 10 to 20 milliseconds, and a weighting coefficient of 0.4 to 0.6 corresponds to a baseline value of 5 to 10 milliseconds. The table data is stored in a configuration file on the local power grid server. The configuration file format is JSON or XML, and it is updated periodically through the power grid management software.

[0101] The phase perturbation direction of each sub-window is determined based on the sign of the phase angle change direction within the resonant frequency distribution range. The sign of the phase angle change direction is determined by the trend of the phase angle increasing with frequency: if the phase angle decreases from a positive value to a negative value, it is considered a negative change; if the phase angle increases from a negative value to a positive value, it is considered a positive change. The definition rule for the phase perturbation direction is: a negative change corresponds to a delayed charging / discharging timestamp, and a positive change corresponds to an advanced timestamp. For example, when the phase angle decreases from +5 degrees to -10 degrees, the timestamps for all charging / discharging operations within the sub-window are delayed; when the phase angle increases from -10 degrees to +5 degrees, the timestamps are advanced.

[0102] Random phase perturbation factors are generated within a sub-window based on the baseline value and direction of the phase perturbation amplitude. The amplitude of the random phase perturbation factor fluctuates around the baseline value, with the fluctuation range not exceeding 30% of the baseline value. The random perturbation value is generated using a uniform distribution based on a pseudo-random number algorithm. The pseudo-random number seed uses the millisecond-level timestamp hash value of the current system time. The hash function is the SHA-256 algorithm, and the generated hash value is converted to an integer and used as the random number seed. For example, when the timestamp is 10:00:00.500, the hash value is a hexadecimal string, which is converted to a decimal integer and used as the seed input to the pseudo-random number generator. The output perturbation value is uniformly distributed within ±30% of the baseline value.

[0103] The random phase disturbance factors of each sub-window are arranged in chronological order to generate an adaptive phase disturbance rule that matches the resonant frequency of the power grid. The adaptive phase disturbance rule is a structured data table containing timestamps, disturbance amplitudes, and directions. The data table is transmitted to the charge / discharge controllers of each electric vehicle via HTTP. After parsing the data table, the controllers execute the disturbance operations in chronological order. For example, the sub-window with timestamps from 10:00:00 to 10:00:01 corresponds to a disturbance amplitude of 8 milliseconds and a direction delay; the controller executes all charge / discharge operations within this time period with a timestamp delay of 8 milliseconds.

[0104] The execution logic of the charge / discharge controller is to read the corresponding disturbance parameters at the beginning of each sub-window. For example, if the sub-window starts at 10:00:00, the controller reads the disturbance amplitude of 8 milliseconds and the delay direction, and adjusts the charge / discharge command originally scheduled to be executed at 10:00:00 to be executed at 10:00:00.008. The update cycle of the disturbance parameters is strictly consistent with the sub-window length to ensure real-time performance. If the sub-window length is 1 second, the disturbance parameters are updated every 1 second; if the sub-window length is 2.5 seconds, they are updated every 2.5 seconds.

[0105] S6. Based on the adaptive phase perturbation rule, perform asymmetric offset adjustment on the power output timing of the initial charge and discharge power command set to generate a corrected command set, including:

[0106] The adaptive phase disturbance rule is analyzed by resolving the timestamp, phase disturbance amplitude reference value, and phase disturbance direction. The adaptive phase disturbance rule is a structured data table containing a timestamp field, a phase disturbance amplitude reference value field, and a phase disturbance direction field. The timestamp field uses the international standard time format (ISO 8601), for example, "2023-10-01 T10:00:00Z". The phase disturbance amplitude reference value field is a value in milliseconds, storing the reference value generated in step S5; for example, "10" represents 10 milliseconds. The phase disturbance direction field is an enumeration type, with values ​​of "advanced" or "delayed," consistent with the positive or negative determination of the phase angle change direction in step S5. The data table is transmitted to the charge / discharge controller via HTTPS protocol, encrypted using TLS 1.3 protocol during transmission, with an encryption key of 256 bits.

[0107] The timestamps of each command in the initial charge / discharge power command set are asymmetrically offset based on the phase disturbance direction. The offset direction is either advancing or delaying the timestamp, and the offset amount is the sum of the baseline value of the phase disturbance amplitude of the corresponding sub-window and the random fluctuation value. The range of the random fluctuation value is ±30% of the baseline value, which is determined according to the maximum permissible timing jitter specified in power grid safety standards (such as IEEE 1547). For example, when the baseline value is 10 milliseconds, the random fluctuation value is -3 milliseconds to +3 milliseconds, and the actual offset is 7 milliseconds to 13 milliseconds. The random fluctuation value is generated by uniform distribution based on a pseudo-random number algorithm. The pseudo-random number seed is the nanosecond-level timestamp hash value of the current system time, and the hash algorithm is SHA-256.

[0108] The difference between the adjusted timestamp and the original timestamp must not exceed the hardware response delay threshold of the bidirectional power converter. The hardware response delay threshold is obtained from the bidirectional power converter's technical specifications, specifically by referring to the "Maximum Response Time" parameter in the device manual. For example, if the maximum response time of a certain converter model is 15 milliseconds, then the difference between all adjusted timestamps is limited to ±15 milliseconds. If the calculated offset exceeds the threshold, the offset is automatically corrected to the threshold boundary value. For example, if the baseline value is 20 milliseconds and the threshold is 15 milliseconds, then the actual offset is forcibly adjusted to 15 milliseconds. The correction logic is implemented through a conditional statement: "If offset > threshold, then offset = threshold".

[0109] The power output timing is reconstructed based on the adjusted timestamps to generate a revised charging and discharging power instruction set. The revised instruction set retains the vehicle identification code, charging / discharging power value, and duration fields from the initial instruction set, updating only the execution timestamp field. For example, the original instruction was for vehicle identification code VI N001 to execute +5 kW charging at timestamp "2023-10-01T10:00:00Z", which is revised to VIN001 executing +5 kW charging at "2023-10-01T10:00:00.008Z". The reconstructed instruction set is stored as a CSV file, with column headers for "Timestamp", "Vehicle Identification Code", "Power Value", and "Duration", separated by commas. The CSV file is uploaded to a cloud server via Secure File Transfer Protocol (SFTP) for download and updating by each electric vehicle controller.

[0110] The revised charge / discharge power instruction set is loaded and executed through the control interface of the bidirectional power converter. The control interface is either a CAN bus or an Ethernet interface, and the communication protocol is Modbus TCP. After receiving the new instruction set, the controller backs up the original instruction set to local non-volatile memory (model example: MX25L25635FMI-10G) and replaces it with the revised instruction set. The replacement operation is implemented through atomic write instructions, ensuring that the instruction switching process will not result in data corruption due to power failure or interruption.

[0111] S7. Based on the modified instruction set, control the bidirectional power converter to perform the modified charging and discharging operation, and dynamically adjust the offset amplitude of the phase disturbance rule, including:

[0112] The dynamic adjustment coefficient for phase disturbance amplitude is calculated based on the weighted sum of the frequency deviation value and the battery health status attenuation coefficient. During the weighted summation, the weights for the frequency deviation value and the health status are pre-set through analysis of historical operating data from the grid operator. For example, based on the statistical correlation between grid stability and battery life over the past year, the weight for the frequency deviation value is set to 0.7, and the weight for the health status is set to 0.3.

[0113] The dynamic adjustment coefficient for phase disturbance amplitude is calculated as follows: the grid frequency deviation value collected in step S1 and the battery health status attenuation coefficient obtained in step S2 are multiplied by preset weights and then added together. The frequency deviation value weight is determined based on statistical analysis of historical grid operation data; for example, when the frequency deviation accounts for 70% of the stability impact, the weight is set to 0.7. The health status weight is set based on the degree of impact of battery aging on lifespan; for example, when it accounts for 30%, the weight is 0.3. For example, if the frequency deviation value is -0.2Hz and the health status attenuation coefficient is 0.8, the weighted result is (-0.2×0.7)+(0.8×0.3)=0.1, and the dynamic adjustment coefficient is 1.1 (baseline value × 1.1). The weight data is stored in the configuration file of the grid control server and retrieved using a lookup table method.

[0114] The phase disturbance amplitude reference value in the adaptive phase disturbance rule is scaled proportionally based on a dynamic adjustment coefficient for phase disturbance amplitude. Specifically, the original phase disturbance amplitude reference value is multiplied by the dynamic adjustment coefficient. For example, if the original reference value is 10 milliseconds and the dynamic adjustment coefficient is 1.2, the scaled reference value is 12 milliseconds. The scaled reference value is limited to the maximum permissible disturbance amplitude of the bidirectional power converter, which is obtained from the bidirectional power converter's technical specifications. For example, the technical specifications for a certain converter model may specify a maximum permissible disturbance amplitude of 50 milliseconds. If the scaled reference value exceeds this limit, it is forcibly corrected to the maximum value of 50 milliseconds. The correction logic is implemented through conditional statements, such as "if the scaled value > 50 milliseconds, then the scaled value = 50 milliseconds".

[0115] The bidirectional power converter is controlled to perform charging and discharging operations according to the revised charging and discharging power command set, while simultaneously replacing the original phase perturbation rules with updated ones. The revised charging and discharging power command set is transmitted to the bidirectional power converter via the Controller Area Network (CLAN) bus protocol, and the CLAN bus message format conforms to the ISO 11898 standard. When replacing the original rules, atomic write operations are used to ensure data integrity, implemented through the transaction characteristics of the file system. The updated phase perturbation rules take effect immediately, and the phase perturbation rules generated in subsequent steps S5 are executed based on the updated phase perturbation amplitude reference value.

[0116] This embodiment improves grid stability and equipment lifespan through the synergistic effect of the following technical features: First, by integrating grid frequency deviation and battery health status parameters in the dynamic electricity price time period division, it solves the problem of dispatch instructions being out of sync with the real-time grid status caused by the reliance on a single electricity price signal in traditional methods. Second, by extracting resonant frequency bands based on frequency sweep testing and generating asymmetric phase disturbance rules, it actively injects phase offsets to suppress resonance risks, significantly reducing harmonic amplification effects compared to passive filtering or fixed threshold truncation schemes in existing technologies. Third, through dynamic time window segmentation and priority ranking mechanisms, it ensures that batteries with high health status are prioritized for scheduling during high electricity price periods, avoiding accelerated aging of low-health batteries due to overcharging and discharging, while traditional average allocation strategies cannot achieve lifespan balance. Finally, by introducing dynamic weighting coefficients of frequency deviation and battery health status in the phase disturbance amplitude adjustment, a closed-loop feedback control is formed, enabling the disturbance rules to adapt to changes in grid status in real time, reducing grid frequency fluctuations, lowering the resonance failure rate, and extending battery life.

[0117] It is worth noting that this invention is particularly applicable to distribution network scenarios containing ultra-high voltage transmission systems. Its asymmetric phase disturbance mechanism can effectively block the transmission of local resonant energy to AC networks above 750 kV, providing underlying disturbance suppression support for large-scale power grid security and defense systems, and enhancing the intelligent dispatch system's ability to finely manage distributed resources.

[0118] The above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data, and are the closest to the real situation. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0119] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0120] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0121] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0123] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0125] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0127] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the V2G regulation potential of electric vehicles based on electricity price regulation, characterized in that, Includes the following steps: S1. Obtain the frequency deviation value of the power grid in the target area and the dynamic electricity price time period division signal; S2. Synchronously collect battery parameters of each electric vehicle battery, including available capacity and health status parameters; S3. Based on the dynamic electricity price time-segmentation signal and battery parameters, calculate the maximum schedulable charging and discharging power range of the electric vehicle cluster in each time period, and generate an initial charging and discharging power instruction set, including: Based on the dynamic electricity price time period segmentation signal in the power grid status dataset, the start timestamp and electricity price incentive level of each time period are determined. Based on the current remaining battery power value in the battery parameter dataset, calculate the upper and lower limits of the initial charging and discharging power for each electric vehicle in each time period. The initial charge and discharge power limits are constrained a second time based on the battery health state decay coefficient to generate the health state corrected charge and discharge power range for each vehicle. The health status correction charging and discharging power range of all electric vehicles within the same time period is sorted by priority according to the electricity price incentive level, with the higher the electricity price incentive level, the higher the vehicle is ranked. Based on the priority ranking results, the charging and discharging power ranges of each vehicle are superimposed to generate the maximum schedulable charging and discharging power range of the cluster for the corresponding time period. Generate an initial charge and discharge power instruction set, which includes a structured data table containing the start timestamp of each time period, the maximum schedulable charge and discharge power range of the cluster, and vehicle identification codes; S4. Collect the impedance spectrum characteristics of the power grid and extract the resonant frequency distribution range of the power grid in the target area; S5. Within the execution time window of the initial charging and discharging power command set, generate an adaptive phase disturbance rule that matches the resonant frequency of the power grid based on the resonant frequency distribution interval. S6. Based on the adaptive phase perturbation rule, the power output timing of the initial charge and discharge power command set is adjusted asymmetrically to generate the corrected command set. S7. Based on the modified instruction set, control the bidirectional power converter to perform the modified charging and discharging operation, and dynamically adjust the offset amplitude of the phase disturbance rule.

2. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, characterized in that, S1 includes: The frequency deviation value of the power grid in the target area is collected in real time by the power grid monitoring device. The frequency deviation value is the difference between the current frequency and the rated frequency of the power grid. The communication module receives dynamic electricity price time period division signals released by the power grid operator. The dynamic electricity price time period division signals include the timestamps of peak and valley electricity price periods and the corresponding electricity price incentive levels. The frequency deviation value and the dynamic electricity price time period segmentation signal are time-synchronized and aligned to generate a power grid status dataset with consistent timestamps.

3. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, characterized in that, S2 include: The on-board battery monitoring device collects the current remaining power value and battery health status degradation coefficient of each electric vehicle battery in real time. The current remaining power value is the percentage of the battery's current usable capacity to its nominal capacity. The battery health status degradation coefficient is calculated by the number of battery cycles and the rate of change of internal resistance. The data collected by each vehicle battery monitoring device is time-stamped using a time synchronization protocol to generate a battery parameter dataset with consistent timestamps. The battery parameter dataset includes the current remaining charge value and battery health status degradation coefficient of each electric vehicle battery.

4. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, characterized in that, S4 includes: Frequency sweep test signals are injected into the power grid of the target area through power grid monitoring devices to collect voltage and current response data of each node in the power grid; The impedance spectrum characteristics of the power grid are calculated based on voltage and current response data. The impedance spectrum characteristics include the impedance amplitude and phase angle at each frequency point. Based on the curves of impedance amplitude and phase angle changing with frequency, identify the frequency bands corresponding to impedance amplitudes below the first preset threshold and phase angles crossing zero, and extract them as resonant frequency distribution intervals. Adjacent frequency bands in the resonant frequency distribution range are clustered and merged. The merging condition is that the difference in impedance amplitude between adjacent frequency bands is less than a second preset threshold and the phase angle changes in the same direction. Based on the merged resonant frequency distribution range, calculate the oscillation risk weighting coefficient for each frequency band. The oscillation risk weighting coefficient is the product of the frequency band width and the impedance amplitude attenuation slope. The frequency band with the highest oscillation risk weight coefficient is determined as the dominant resonant frequency, and the bandwidth corresponding to the dominant resonant frequency is the effective suppression range of the resonant frequency distribution interval.

5. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, characterized in that, S5 include: The execution time window of the initial charge and discharge power instruction set is divided into multiple continuous sub-windows, and the length of the sub-window is dynamically adjusted according to the bandwidth of the dominant resonant frequency point in the effective suppression range of the resonant frequency distribution interval. The phase disturbance amplitude benchmark value for each sub-window is generated based on the oscillation risk weight coefficient of the dominant resonant frequency. The phase disturbance amplitude benchmark value is positively correlated with the oscillation risk weight coefficient. The phase perturbation direction of each sub-window is determined based on the sign of the phase angle change direction in the resonant frequency distribution interval. A random phase disturbance factor is generated within the sub-window based on the phase disturbance amplitude reference value and direction. The amplitude of the random phase disturbance factor fluctuates around the phase disturbance amplitude reference value and does not exceed the preset fluctuation range. The random phase disturbance factors of each sub-window are arranged in chronological order to generate an adaptive phase disturbance rule that matches the resonant frequency of the power grid.

6. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, characterized in that, S6 include: Analyze the timestamp, the reference value of the phase perturbation amplitude, and the direction of the phase perturbation in the adaptive phase perturbation rule; The timestamps of each instruction in the initial charge and discharge power instruction set are asymmetrically offset according to the phase disturbance direction. The offset direction is either advancing or delaying the timestamp, and the offset amount is the reference value of the phase disturbance amplitude of the sub-window to which the corresponding timestamp belongs. The difference between the adjusted timestamp and the original timestamp shall not exceed the hardware response delay threshold of the bidirectional power converter; The power output timing is reconstructed based on the adjusted timestamps to generate a corrected charging and discharging power instruction set.

7. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, characterized in that, S7 includes: The dynamic adjustment coefficient for phase disturbance amplitude is calculated based on the weighted sum of the frequency deviation value and the battery health status attenuation coefficient. The weighting weights are the frequency deviation value weight and the health status weight. The phase disturbance amplitude benchmark value in the adaptive phase disturbance rule is scaled proportionally based on the phase disturbance amplitude dynamic adjustment coefficient to generate the updated phase disturbance rule. The bidirectional power converter is controlled to perform charging and discharging operations according to the revised charging and discharging power command set, while the updated phase disturbance rule replaces the original rule.

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