Electric vehicle V2G regulation potential evaluation method based on electricity price regulation

Abstract

The invention discloses an electric vehicle V2G regulation potential evaluation method based on electricity price regulation, particularly relates to the technical field of electric energy storage cooperative control, and is used for solving the problems of power grid resonance risk and battery life loss caused by power synchronism in existing V2G centralized scheduling. Through real-time fusion of a dynamic electricity price time period division signal and a power grid frequency deviation value, carrying out dual constraint on a charging and discharging power range in combination with a battery health state attenuation coefficient, and generating an initial instruction set; a power grid resonant frequency distribution interval is further extracted based on a frequency sweep test, an asymmetric phase disturbance rule is generated for a dominant resonant frequency point, and a harmonic superposition effect of power output is destroyed through differential time sequence offset; in the instruction execution stage, the phase disturbance amplitude is dynamically adjusted to adapt to the power grid frequency fluctuation and the battery aging degree, a closed-loop control mechanism is formed, collaborative optimization of the power grid stability and the equipment service life is achieved, and the safety of the electric vehicle cluster participating in power grid adjustment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power energy storage collaborative control, and more specifically, to a method for evaluating the regulation potential of electric vehicle V2G based on electricity price regulation. Background Art

[0002] In the field of power system energy storage, the participation of electric vehicles in grid load regulation through vehicle-to-grid (V2G) technology has become an important development direction; in the prior art, grid operators issue charging and discharging incentive signals to users through a dynamic electricity price mechanism, and predict the dispatchable capacity of electric vehicle clusters based on a user response model to formulate charging and discharging plans to achieve load balance. Such methods usually rely on economic optimization algorithms, and centrally control the dispersed electric vehicle batteries as distributed regulation resources for a large-scale cross-regional power grid through an aggregation platform to provide flexibility support for ultra-high voltage power transmission scenarios.

[0003] However, when a large number of electric vehicles synchronously perform charging and discharging operations driven by electricity price signals, the time synchronization of their power outputs is likely to interact with the frequency dynamic characteristics of the power grid (especially the interconnection oscillation mode involving AC power transmission systems above 750 kV), resulting in low-frequency power oscillation phenomena in the regional power grid, threatening the safety guarantee and stability of the large-scale power grid defense system, making the theoretical regulation potential of electric vehicle clusters unable to be safely released, and in severe cases, it may trigger the action of protection devices, weakening the reliability of intelligent dispatching and increasing the risk of load imbalance. Summary of the Invention

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

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for evaluating the regulation potential of electric vehicle V2G based on electricity price regulation includes the following steps:

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

[0008] S2. Synchronously collect the battery parameters of each electric vehicle battery, and the battery parameters include available capacity and health state parameters;

[0009] S3. Calculate the maximum dispatchable charging and discharging power range of the electric vehicle cluster in each period according to the dynamic electricity price period division signal combined with the battery parameters, and generate an initial charging and discharging power instruction set;

[0010] S4. Collect the impedance spectrum characteristics of the power grid, and extract the resonant frequency distribution interval of the target regional power grid;

[0011] S5. During the execution time window of the initial charge-discharge power instruction set, generate an adaptive phase perturbation rule that matches the grid resonance frequency points according to the resonance frequency distribution interval;

[0012] S6. Based on the adaptive phase perturbation rule, perform an asymmetric offset adjustment on the power output timing of the initial charge-discharge power instruction set to generate a corrected instruction set;

[0013] S7. Based on the corrected instruction set, control the bidirectional power converter to perform the corrected charge-discharge operation and dynamically adjust the offset amplitude of the phase perturbation rule.

[0014] In a preferred embodiment, S1 includes:

[0015] Real-time collect the frequency deviation value of the power grid in the target area through the power grid monitoring device, and the frequency deviation value is the difference between the current frequency of the power grid and the rated frequency;

[0016] Receive the dynamic electricity price period division signal issued by the power grid operator through the communication module, and the dynamic electricity price period division signal includes the time stamps of the peak and valley periods of the electricity price and the corresponding electricity price incentive levels;

[0017] Perform time synchronization alignment processing on the frequency deviation value and the dynamic electricity price period division signal to generate a power grid state data set with consistent time stamps.

[0018] In a preferred embodiment, S2 includes:

[0019] Real-time collect the current remaining power value and the battery health state attenuation coefficient of each electric vehicle battery through the on-vehicle battery monitoring device. The current remaining power value is the percentage value of the current available capacity of the battery to the nominal capacity, and the battery health state attenuation coefficient is calculated through the battery cycle times and the internal resistance change rate;

[0020] Perform time stamp alignment processing on the data collected by each on-vehicle battery monitoring device through the time synchronization protocol to generate a battery parameter data set with consistent time stamps. The battery parameter data set includes the current remaining power value and the battery health state attenuation coefficient of each electric vehicle battery.

[0021] In a preferred embodiment, S3 includes:

[0022] Based on the dynamic electricity price period division signal in the power grid state data set, determine the start time stamp and the electricity price incentive level of each period;

[0023] According to the current remaining power value in the battery parameter data set, calculate the upper and lower limits of the initial charge-discharge power of each electric vehicle in each period;

[0024] Perform secondary constraints on the upper and lower limits of the initial charge and discharge power according to the battery state of health attenuation coefficient to generate the corrected charge and discharge power range of the state of health for each vehicle;

[0025] Sort the corrected charge and discharge power ranges of the state of health of all electric vehicles within the same time period according to the electricity price incentive level, and the priority sorting is that the higher the electricity price incentive level, the higher the vehicle ranking;

[0026] Based on the priority sorting result, superimpose the charge and discharge power ranges of each vehicle to generate the cluster maximum schedulable charge and discharge power range for the corresponding time period;

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

[0028] In a preferred embodiment, S4 includes:

[0029] Inject a swept-frequency test signal into the target area power grid through a power grid monitoring device, and collect the voltage and current response data of each node of the power grid;

[0030] Calculate the impedance spectrum characteristics of the power grid based on the voltage and current response data, and the impedance spectrum characteristics include the impedance amplitude and phase angle of each frequency point;

[0031] According to the curve of the impedance amplitude and phase angle changing with frequency, identify the frequency band corresponding to the impedance amplitude lower than the first preset threshold and the zero-crossing point of the phase angle, and extract it as the resonant frequency distribution interval;

[0032] Cluster and merge adjacent frequency bands in the resonant frequency distribution interval, and the merging condition is that the impedance amplitude difference between adjacent frequency bands is less than the second preset threshold and the phase angle change directions are the same;

[0033] According to the merged resonant frequency distribution interval, calculate the oscillation risk weight coefficient of each frequency band, and the oscillation risk weight coefficient is the product of the frequency band width and the impedance amplitude attenuation slope;

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

[0035] In a preferred embodiment, S5 includes:

[0036] Divide the execution time window of the initial charge and discharge power instruction set into multiple consecutive sub-windows, and the length of the sub-windows is dynamically adjusted according to the frequency band width corresponding to the dominant resonant frequency point in the effective suppression range of the resonant frequency distribution interval;

[0037] Generate the phase perturbation amplitude reference value for each sub-window according to the oscillation risk weight coefficient of the dominant resonant frequency point, and the phase perturbation amplitude reference value is positively correlated with the oscillation risk weight coefficient;

[0038] Determine the phase perturbation direction of each sub-window based on the positive or negative nature of the phase angle change direction in the resonant frequency distribution interval;

[0039] Generate a random phase perturbation factor within the sub-window according to the phase perturbation amplitude reference value and direction. The amplitude of the random phase perturbation factor fluctuates around the phase perturbation amplitude reference value and does not exceed the preset fluctuation range;

[0040] Arrange the random phase perturbation factors of each sub-window in chronological order to generate an adaptive phase perturbation rule matching the power grid resonant frequency point.

[0041] In a preferred embodiment, S6 includes:

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

[0043] Perform an asymmetric offset adjustment on the instruction timestamps of the initial charge and discharge power instruction set according to the phase perturbation direction. The offset direction is to advance or delay the timestamp, and the offset amount is the phase perturbation amplitude reference value of the sub-window to which the corresponding timestamp belongs;

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

[0045] Reconstruct the power output timing sequence based on the adjusted timestamp to generate a corrected charge and discharge power instruction set.

[0046] In a preferred embodiment, S7 includes:

[0047] Calculate the phase perturbation amplitude dynamic adjustment coefficient according to the weighted sum result of the frequency deviation value and the battery health state decay coefficient. The weighting weights are the frequency deviation value weight and the health state weight;

[0048] Scale the phase perturbation amplitude reference value in the adaptive phase perturbation rule based on the phase perturbation amplitude dynamic adjustment coefficient to generate an updated phase perturbation rule;

[0049] Control the bidirectional power converter to perform charge and discharge operations according to the corrected charge and discharge power instruction set, and at the same time replace the original rule with the updated phase perturbation rule.

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

[0051] 1. After the initial instruction set is generated, based on the resonant frequency distribution interval extracted from the sweep frequency test, an asymmetric timing offset is dynamically injected during the instruction execution phase. This offset is inversely correlated with the phase characteristics of the dominant resonant frequency point. By disrupting the periodic synchronization of the power waveform, the harmonic superposition effect is actively weakened. Compared with the global symmetric perturbation or passive filtering design in traditional methods, it compensates directionally according to the actual impedance characteristics of the power grid, eliminating the resonance trigger condition while maintaining the economy of the dispatching plan.

[0052] 2. When calculating the maximum schedulable power, the battery cycle count and the internal resistance change rate are fused into a health state decay coefficient, which serves as a dynamic constraint condition for the charge and discharge power range. During the phase perturbation amplitude adjustment phase, the decay coefficient is further weighted and fused with the power grid frequency deviation, enabling the perturbation amplitude to respond to the real-time state changes of the power grid while being limited by the battery aging degree. This design enables high-health batteries to respond preferentially to the regulation requirements during high electricity price periods, while the charge and discharge power of low-health batteries is automatically limited within the safety threshold, achieving the coordinated optimization of power grid stability and equipment service life and improving the safety of electric vehicle clusters participating in power grid regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flowchart of the method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0055] Embodiment: Figure 1 The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to the present invention is given, 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 period division signal;

[0057] S2. Synchronously collect the battery parameters of each electric vehicle battery, and the battery parameters include the available capacity and the health state parameters;

[0058] S3. According to the dynamic electricity price period division signal and in combination with the battery parameters, calculate the maximum schedulable charge and discharge power range of the electric vehicle cluster in each period, and generate an initial charge and discharge power instruction set;

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

[0060] S5. During the execution time window of the initial charge-discharge power instruction set, generate an adaptive phase perturbation rule that matches the power grid resonance frequency points according to the resonance frequency distribution interval.

[0061] S6. Based on the adaptive phase perturbation rule, perform an asymmetric offset adjustment on the power output timing sequence of the initial charge-discharge power instruction set to generate a corrected instruction set.

[0062] S7. Based on the corrected instruction set, control the bidirectional power converter to perform the corrected charge-discharge operation and dynamically adjust the offset amplitude of the phase perturbation rule.

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

[0064] Real-time collect the frequency deviation value of the power grid in the target area through a power grid monitoring device. The power grid monitoring device is a synchronized phasor measurement unit deployed at key nodes of the power grid in the target area. The synchronized phasor measurement unit measures the current frequency of the power grid at a preset sampling rate and outputs the difference between the current frequency of the power grid and the rated frequency of the power grid as the frequency deviation value. The rated frequency of the power grid is set according to the operation standard of the power grid in the target area. For example, in a power grid adopting a 50Hz standard, the rated frequency is 50Hz.

[0065] Receive the dynamic electricity price period division signal issued by the power grid operator through a communication module. The communication module is a hardware interface that supports the standard communication protocol of the power system. The dynamic electricity price period division signal is sent by the master station system of the power grid operator through a wired or wireless communication network. The signal data format includes but is not limited to JSON or XML, and contains the time stamps of the peak-valley periods of the electricity price and the corresponding electricity price incentive levels. The time stamps of the peak-valley periods of the electricity price are generated based on the standard time format. The electricity price incentive levels are divided into multiple levels. For example, the peak period corresponds to a high electricity price incentive level, and the valley period corresponds to a low electricity price incentive level.

[0066] Perform time synchronization alignment processing on the frequency deviation value and the dynamic electricity price period division signal to generate a power grid state data set with consistent time stamps. The time synchronization alignment processing is implemented through the standard time synchronization protocol, and aligns the collection time stamp of the frequency deviation value and the release time stamp of the dynamic electricity price period division signal to a preset accuracy. The aligned power grid state data set is structured data containing time stamps, frequency deviation values, and electricity price incentive levels. The time stamp is the unified time identifier after alignment, the frequency deviation value is the real-time data output by the synchronized phasor measurement unit, and the electricity price incentive level is the parameter corresponding to the time stamp in the dynamic electricity price period division signal.

[0067] The hardware deployment locations of the power grid monitoring device and the communication module are within the same power facility, ensuring that the physical signal transmission is in a state of extremely low latency. The measurement accuracy of the synchronized phasor measurement unit meets the power system monitoring standards, and the update time interval of the dynamic electricity price period division signal matches the power grid scheduling cycle. The execution frequency of the time synchronization alignment process is consistent with the power grid state update frequency, ensuring that the power grid state data set reflects the power grid operation state in real time.

[0068] The storage medium for the structured data is the non-volatile storage device of the power grid local server, and the storage format includes but is not limited to table or database form. The power grid state data set is associated with the charge and discharge power instruction set in the subsequent steps through the timestamp field, and the association method is implemented based on the time window matching logic. The length of the time window is adapted to the power grid state update frequency.

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

[0070] The current remaining power value and the battery health status attenuation coefficient of each electric vehicle battery are collected in real time through the on-vehicle battery monitoring device. The on-vehicle battery monitoring device is a hardware unit integrated into the electric vehicle battery management system, which measures the real-time voltage and current data of the battery through voltage sensors and current sensors, and calculates the current remaining power value of the battery based on the voltage and current data. The current remaining power value is the percentage value of the current available capacity of the battery to the nominal capacity of the battery. The nominal capacity of the battery is the maximum capacity value calibrated at the time of battery factory. For example, the nominal capacity of a certain model of battery is 60 kWh.

[0071] The battery health status attenuation coefficient is calculated through the battery cycle count and the internal resistance change rate. The battery cycle count is the cumulative number of complete charge and discharge cycles of the battery since it left the factory, which is statistically obtained by the historical data recording unit of the battery management system. The internal resistance change rate is the ratio of the current internal resistance value of the battery to the initial internal resistance value. The internal resistance value is obtained by measuring the battery's alternating current impedance spectroscopy method, and the measurement frequency range is from 1 Hz to 1 kHz. The calculation method of the battery health status attenuation coefficient is to sum the battery cycle count and the internal resistance change rate with weights. The weight coefficients are set according to the battery type. For example, the weight coefficient of the cycle count of lithium iron phosphate batteries is higher than that of the internal resistance change rate.

[0072] The data collected by each on-vehicle battery monitoring device is subjected to timestamp alignment processing through a time synchronization protocol. The time synchronization protocol is the time signal provided by the Global Positioning System or the Network Time Protocol. The on-vehicle battery monitoring device is built with a clock chip to receive the synchronization signal and convert the local timestamp of the collected data into a unified standard time. The specific method of timestamp alignment processing is to sort the current remaining power values and battery health state attenuation coefficient data of each electric vehicle battery according to the standard timestamp, and fill in the interpolation data for the missing time points. The interpolation method is linear interpolation or nearest neighbor interpolation.

[0073] Generate a battery parameter dataset with consistent timestamps. The battery parameter dataset with consistent timestamps is a structured data table, which includes four fields: standard timestamp, vehicle identification code, current remaining power value, and battery health state attenuation coefficient. The vehicle identification code is the unique identification code for each electric vehicle, such as the vehicle VIN code or license plate number. The storage medium of the structured data table is the non-volatile memory of the cloud server or the local edge computing device, and the storage format is a database table or a CSV file. The primary key of the database table is the combination of the standard timestamp and the vehicle identification code.

[0074] The structured data table is associated with the power grid state dataset generated in step S1 through the 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 schedulable power value is assigned to each electric vehicle according to the vehicle identification code.

[0075] The weight coefficient of the battery health state attenuation coefficient is determined by the experimental calibration method according to the battery type. The experimental calibration method is to conduct an aging test on the same type of battery in a laboratory environment, and statistically analyze the contribution ratio of the cycle number and the internal resistance change rate to the capacity attenuation. For example, the cycle number weight coefficient of a certain type of ternary lithium battery is 0.7, and the internal resistance change rate weight coefficient is 0.3. The weight coefficient is stored in the read-only memory of the battery management system for real-time calculation and call.

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

[0077] The association results of the battery parameter dataset and the grid status dataset are stored in the association database. Each record in the association database contains six fields: standard timestamp, frequency deviation value, electricity price incentive level, vehicle identification code, current remaining battery capacity value, and battery health status decay coefficient. The query method of the association database is index query based on the timestamp range or vehicle identification code, and the index type is B-tree or hash index. The access rights of the association database are managed through a role control mechanism, and the role control mechanism is a grading of 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. Combine the battery parameters according to the dynamic electricity price period division signal, calculate the maximum schedulable charge and discharge power range of the electric vehicle cluster in each period, and generate an initial charge and discharge power instruction set, including:

[0079] Based on the dynamic electricity price period division signal in the grid status dataset, determine the start timestamp and electricity price incentive level of each period. The dynamic electricity price period division signal is structured data released by the grid operator, including the time range of electricity price peak and valley periods and the corresponding electricity price levels. For example, the peak period is from 10:00 to 14:00 every day, and the corresponding electricity price incentive level is level one; the flat period is from 14:00 to 18:00 every day, and the corresponding electricity price incentive level is level two.

[0080] According to the current remaining battery capacity value in the battery parameter dataset, calculate the upper and lower limits of the initial charge and discharge power of each electric vehicle in each period. The current remaining battery capacity value is the percentage of the current available capacity of the battery to the nominal capacity, and the upper and lower limits of the initial charge and discharge power are obtained by multiplying the current remaining battery capacity value by the rated charge and discharge power of the battery. For example, if the current remaining battery capacity value of an electric vehicle is 80%, and the rated charge and discharge power is 10 kilowatts, then the upper limit of the initial charge power is 8 kilowatts, and the lower limit of the discharge power is -8 kilowatts. If the frequency deviation value in the grid status dataset exceeds the preset threshold, the upper and lower limits of the initial charge and discharge power are dynamically corrected, and the correction method is to reduce the power range according to the frequency deviation ratio.

[0081] Perform secondary constraint on the upper and lower limits of the initial charge and discharge power according to the battery health status decay coefficient, and generate the charge and discharge power range with health status correction for each vehicle.

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

[0083] Rank the priority of the corrected charge-discharge power ranges of all electric vehicles within the same time period according to the electricity price incentive levels. The priority ranking rule is that the higher the electricity price incentive level, the higher the vehicle's ranking, and among vehicles with the same electricity price incentive level, the vehicle with a lower battery health state attenuation coefficient ranks higher. For example, vehicles during peak hours (first-level electricity price incentive) are ranked ahead of vehicles during flat hours (second-level electricity price incentive), and among vehicles with the same electricity price level, the vehicle with a health state attenuation coefficient of 50 ranks before the vehicle with a health state attenuation coefficient of 70.

[0084] Based on the priority ranking results, superimpose the charge-discharge power ranges of each vehicle to generate the cluster's maximum schedulable charge-discharge power range for the corresponding time period. The superimposing method is to accumulate the charge-discharge power ranges of vehicles one by one in the sorting order until the capacity limit threshold of the power grid line is reached. The capacity limit threshold of the power grid line is obtained through a power grid monitoring device. For example, if the rated capacity of a certain line is 1000 kilowatts, stop superimposing when the total power approaches 1000 kilowatt-hours, and mark the remaining vehicles as in an unschedulable state. If the accumulated power of all vehicles within the current time period does not exceed the capacity limit threshold, the cluster's maximum schedulable charge-discharge power range is the sum of the power ranges of all vehicles.

[0085] Generate an initial charge-discharge power instruction set. The instruction set is a structured data table containing the start timestamp of each time period, the cluster's maximum schedulable charge-discharge power range, and the vehicle identification code. The fields of the structured data table include the start timestamp, the upper limit of schedulable power, the lower limit of schedulable power, the list of vehicle identification codes, and the corresponding power allocation values. The list of vehicle identification codes is a set of unique vehicle codes sorted by priority, and the power allocation value is the actual allowable charge-discharge power value of each vehicle during this time period. For example, the start timestamp of a certain time period is 2023-10-01 10:00:00, the cluster's maximum schedulable charge-discharge power range is [-500kW, +500kW], the list of vehicle identification codes is [VIN001, VIN002, VIN003], and the corresponding power allocation values are [-100kW, -200kW, -200kW].

[0086] The storage format of the structured data table is a CSV file or a relational database table. The column headers of the CSV file are "start timestamp", "upper limit of schedulable power", "lower limit of schedulable power", "list of vehicle identification codes", and "power allocation values", and each record corresponds to a scheduling instruction for a time period. The primary key of the relational database table is the start timestamp, and the foreign key is associated with the timestamp fields in the power grid status dataset and the battery parameter dataset. During the data storage process, a data verification mechanism is adopted, and the verification rules include checking whether the schedulable power range exceeds the capacity limit of the power grid line, whether there are duplicate or invalid codes in the vehicle identification codes, and whether the power allocation values are within the corrected charge-discharge power range of the vehicle's health state.

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

[0088] The capacity limit threshold of the power grid line is obtained in real time through a power grid monitoring device. The power grid monitoring device is an intelligent meter deployed in a substation or a power distribution cabinet. The intelligent meter measures the real-time current and voltage of the line through a current transformer and a voltage transformer. The line capacity limit threshold is calculated based on the rated current and voltage of the line. The calculation formula is Pmax = Vrated × Irated × cosφ. Where, Pmax is the line capacity limit threshold, Vrated is the rated voltage of the line, Irated is the rated current of the line, and cosφ is the power factor, which is obtained by real-time measurement through the power grid monitoring device.

[0089] The power distribution value is determined by proportional distribution according to the vehicle priority ranking result. For example, the maximum schedulable power range of a cluster at a certain time period is [-500kW, +500kW], and the total required power of the top 3 vehicles in the priority ranking is -600kW. Then, the power distribution value of each vehicle is compressed proportionally to (500 / 600) × the original required power. If the vehicle required power does not exceed the capacity limit, it is distributed according to the original required power. The calculation frequency of the power distribution value is consistent with the power grid scheduling cycle, for example, it is updated every 15 minutes.

[0090] The invalid data processing methods in the data verification mechanism include automatic correction and manual intervention. The automatic correction rule is to forcibly correct the power distribution value that exceeds the charge and discharge power range of the vehicle health status correction to the upper or lower limit value. For example, if the power distribution value of a vehicle is -120kW, but the lower limit of the discharge power of its health status correction is -100kW, it is automatically corrected to -100kW. Manual intervention is achieved through the operation interface of the power grid dispatcher. The operation interface displays a list of abnormal data and provides manual correction options. The corrected data needs to be verified twice before being stored.

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

[0092] Inject a swept-frequency test signal into the power grid of the target area through a power grid monitoring device. The frequency range of the swept-frequency test signal covers the low-frequency to high-frequency bands, for example, from 1 hertz to 2 kilohertz, and the signal amplitude is limited to no higher than 5% of the rated voltage of the power grid to avoid interfering with the normal operation of the power grid. The power grid monitoring device collects the voltage response data and current response data of each node of the power grid through a voltage transformer and a current transformer. The measurement accuracy of the voltage transformer meets the power system standard, for example, the error does not exceed 0.2%, and the range of the current transformer covers 1% to 150% of the rated current of the power grid.

[0093] Calculate the impedance spectrum characteristics of the power grid based on the 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 calculation method of the impedance amplitude is to divide the voltage amplitude at a certain frequency point by the current amplitude at the same frequency point. For example, if the voltage amplitude at a certain frequency point is 220 volts and the current amplitude is 10 amperes, the impedance amplitude is 22 ohms. The calculation method of the phase angle is to subtract 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 hertz, the unit of the impedance amplitude is ohm, and the unit of the phase angle is degree.

[0094] According to the curves of the impedance amplitude and phase angle changing with frequency, identify the frequency band where the impedance amplitude is lower than the first preset threshold and the corresponding zero-crossing point of the phase angle. The first preset threshold is set according to the percentage of the rated impedance of the power grid. For example, when the rated impedance is 10 ohms, the first preset threshold is 2 ohms. The zero-crossing point of the phase angle 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 the frequency point of 50 hertz and -3 degrees at 60 hertz, it is determined that there is a zero-crossing point of the phase angle between 50 hertz and 60 hertz. Extract the continuous frequency band that meets the above conditions as the resonant frequency distribution interval. For example, if it is identified that the impedance amplitudes in the frequency band of 50 hertz - 70 hertz are all lower than 2 ohms and there is a zero-crossing point of the phase angle, then mark this frequency band as the resonant frequency distribution interval.

[0095] Cluster and merge adjacent frequency bands within the resonant frequency distribution range. The merging condition is that the impedance magnitude difference between adjacent frequency bands is less than the second preset threshold and the phase angle change directions are the same. The second preset threshold is set according to a percentage of the first preset threshold. For example, when the first preset threshold is 2 ohms, the second preset threshold is 0.2 ohms. The calculation method of the impedance magnitude difference is the absolute difference of the impedance magnitudes at the endpoints of adjacent frequency bands. For example, if the impedance magnitude at the termination frequency of frequency band A is 1.8 ohms and the impedance magnitude at the starting frequency of frequency band B is 1.7 ohms, the difference is 0.1 ohms. The same phase angle change direction means that the phase angles of adjacent frequency bands both show a decreasing or increasing trend as the frequency increases. 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, it is determined that the phase angle change directions are the same. The frequency band after clustering and merging is the union of the frequency ranges of the original adjacent frequency bands. For example, the frequency bands of 50 Hz - 60 Hz and 60 Hz - 70 Hz are merged into 50 Hz - 70 Hz.

[0096] According to the merged resonant frequency distribution range, calculate the oscillation risk weight coefficient for each frequency band. The oscillation risk weight coefficient is the product of the frequency band width and the impedance magnitude attenuation slope. The frequency band width is the difference between the termination frequency and the starting frequency of the frequency band. For example, the width of the frequency band 50 Hz - 70 Hz is 20 Hz. The impedance magnitude attenuation slope is the average decreasing rate of the impedance magnitude within the frequency band as the frequency changes, and the calculation method is (the impedance magnitude at the starting frequency of the frequency band minus the impedance magnitude at the termination frequency) divided by the frequency band width. For example, if the impedance magnitude at the starting frequency is 1.8 ohms, the termination frequency is 1.2 ohms, and the frequency band width is 20 Hz, the attenuation slope is (1.8 - 1.2) / 20 = 0.03 ohms / Hz. The oscillation risk weight coefficient is 20 Hz × 0.03 ohms / Hz = 0.6.

[0097] Determine the dominant resonant frequency point as the frequency band with the highest oscillation risk weight coefficient. The frequency band width corresponding to the dominant resonant frequency point is the effective suppression range of the resonant frequency distribution range. For example, if the oscillation risk weight coefficient of a certain frequency band is 0.8, higher than 0.6 and 0.4 of other frequency bands, it is determined that this frequency band is the dominant resonant frequency point, and its effective suppression range is the frequency span of this frequency band, such as 50 Hz - 70 Hz. The effective suppression range is used to limit the frequency boundary of the perturbation effect when generating the phase perturbation rule in the subsequent steps. For example, an asymmetric timing offset adjustment is applied within the range of 50 Hz - 70 Hz.

[0098] S5. Within the execution time window of the initial charge and discharge power instruction set, generate an adaptive phase perturbation rule that matches the grid resonant frequency points according to the resonant frequency distribution range, including:

[0099] The execution time window of the initial charge-discharge power instruction set is divided into multiple consecutive sub-windows, and the length of the sub-windows is dynamically adjusted according to the bandwidth corresponding to the dominant resonant frequency point in the effective suppression range of the resonant frequency distribution interval. The dynamic adjustment rule is: the shorter the bandwidth of the sub-window, the shorter its length; the wider the bandwidth of the sub-window, the longer its length. The specific proportional relationship is determined in advance by the experimental calibration method. 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 division is aligned with the start timestamp of the dynamic electricity price period division signal to ensure that the sub-windows are synchronized with the power grid scheduling cycle.

[0100] Generate the phase perturbation amplitude reference value for each sub-window according to the oscillation risk weight coefficient of the dominant resonant frequency point. The phase perturbation amplitude reference value is positively correlated with the oscillation risk weight coefficient, and the specific mapping relationship is realized by the look-up table method. In the preset table of the look-up table method, the oscillation risk weight coefficient is divided into multiple levels, and each level corresponds to a reference value range. For example, the weight coefficient from 0.7 to 1.0 corresponds to the reference value from 10 ms to 20 ms, and the weight coefficient from 0.4 to 0.6 corresponds to the reference value from 5 ms to 10 ms. The table data is stored in the configuration file of the local power grid server, and the configuration file format is JSON or XML, which is updated regularly through the power grid management software.

[0101] Determine the phase perturbation direction of each sub-window based on the positive or negative nature of the phase angle change direction in the resonant frequency distribution interval. The positive or negative nature of the phase angle change direction is determined according to the trend of the phase angle with the increase of frequency: if the phase angle decreases from a positive value to a negative value, it is determined as a negative change; if the phase angle increases from a negative value to a positive value, it is determined as a positive change. The definition rule of the phase perturbation direction is: a negative change corresponds to a delay in the charge-discharge timestamp, and a positive change corresponds to an advance of the timestamp. For example, when the phase angle drops from +5 degrees to -10 degrees, the timestamps of all charge-discharge operations within the sub-window are delayed; when the phase angle rises from -10 degrees to +5 degrees, the timestamp is advanced.

[0102] Generate a random phase perturbation factor within the sub-window according to the phase perturbation amplitude reference value and direction. The amplitude of the random phase perturbation factor fluctuates around the reference value, and the fluctuation range does not exceed 30% of the reference value. The generation method of the random perturbation value is a uniform distribution based on the pseudo-random number algorithm, and 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 into 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 into a decimal integer and used as the seed to input the pseudo-random number generator, and the output perturbation value is uniformly distributed within the range of ±30% of the reference value.

[0103] Arrange the random phase perturbation factors of each sub-window in chronological order to generate an adaptive phase perturbation rule that matches the power grid resonance frequency points. The adaptive phase perturbation rule is a structured data table containing timestamps, perturbation amplitudes, and directions. The data table is transmitted to the charge and discharge controllers of each electric vehicle via the HTTP protocol. After parsing the data table, the controller performs perturbation operations in timestamp order. For example, for the sub-window from timestamp 10:00:00 to 10:00:01, the corresponding perturbation amplitude is 8 milliseconds and the direction is delay. The controller delays the timestamps of all charge and discharge operations within this period by 8 milliseconds for execution.

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

[0105] S6. Perform an asymmetric offset adjustment on the power output timing sequence of the initial charge and discharge power instruction set based on the adaptive phase perturbation rule to generate a corrected instruction set, including:

[0106] Parse the timestamps, phase perturbation amplitude reference values, and phase perturbation directions in the adaptive phase perturbation rule. The adaptive phase perturbation rule is a structured data table containing a timestamp field, a phase perturbation amplitude reference value field, and a phase perturbation direction field. The timestamp field is in the international standard time format (ISO8601), such as "2023-10-01 T10:00:00Z". The phase perturbation amplitude reference value field is a numerical value in milliseconds, storing the reference value generated in step S5, such as "10" representing 10 milliseconds. The phase perturbation direction field is an enumeration type, with values of "advance" or "delay", which is consistent with the positive and negative determination result of the phase angle change direction in step S5. The data table is transmitted to the charge and discharge controller via the HTTPS protocol, and is encrypted using the TLS 1.3 protocol during transmission. The encryption key is 256 bits long.

[0107] Asymmetric offset adjustment is performed on the instruction timestamps of the initial charge-discharge power instruction set according to the phase perturbation direction. The offset direction is to advance or delay the timestamp, and the offset amount is the phase perturbation amplitude reference value of the sub-window to which the corresponding timestamp belongs superimposed with a random fluctuation value. The range of the random fluctuation value is ±30% of the reference value, and this range is determined according to the maximum allowable timing jitter specified in grid safety standards (such as IEEE 1547). For example, when the reference value is 10 milliseconds, the random fluctuation value is from -3 milliseconds to +3 milliseconds, and the actual offset amount is from 7 milliseconds to 13 milliseconds. The generation method of the random fluctuation value is a uniform distribution based on a pseudo-random number algorithm, and the pseudo-random number seed is the hash value of the nanosecond-level timestamp of the current system time, and the hash algorithm is SHA-256.

[0108] The difference between the adjusted timestamp and the original timestamp does not exceed the hardware response delay threshold of the bidirectional power converter. The hardware response delay threshold is obtained from the technical specifications of the bidirectional power converter. The specific method is to consult the "maximum response time" parameter in the device manual. For example, if the maximum response time of a certain model converter is 15 milliseconds, then the difference after adjusting all timestamps is limited within ±15 milliseconds. If the calculated offset amount exceeds the threshold, the offset amount is automatically corrected to the threshold boundary value. For example, when the reference value is 20 milliseconds and the threshold is 15 milliseconds, the actual offset amount is forced to be adjusted to 15 milliseconds. The correction logic is implemented through a conditional judgment statement, and the conditional judgment statement is "if the offset amount > the threshold, then the offset amount = the threshold".

[0109] Based on the adjusted timestamp, reconstruct the power output timing sequence to generate a corrected charge-discharge power instruction set. The corrected charge-discharge power instruction set retains the vehicle identification code, charge-discharge power value, and duration fields in the initial instruction set, and only updates the execution timestamp field. For example, the original instruction is that vehicle identification code VIN001 performs +5 kW charging at the timestamp "2023-10-01T10:00:00Z", and after correction, it is VIN001 performs +5 kW charging at "2023-10-01 T10:00:00.008Z". The reconstructed instruction set is stored as a CSV format file, and the file column headers are "timestamp", "vehicle identification code", "power value", "duration", and the data separator is a comma. The CSV file is uploaded to the cloud server through the Secure File Transfer Protocol (SFTP) for download and update by each electric vehicle controller.

[0110] The corrected charge and discharge power instruction set is loaded and executed through the control interface of the bidirectional power converter. The control interface is 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 the local non-volatile memory (model example: MX25L25635FMI-10G) and replaces it with the corrected instruction set. The replacement operation is implemented through an atomic write instruction to ensure that data corruption does not occur due to power failure or interruption during the instruction switching process.

[0111] S7. Based on the corrected instruction set, control the bidirectional power converter to perform the corrected charge and discharge operations, and dynamically adjust the offset amplitude of the phase perturbation rule, including:

[0112] Calculate the dynamic adjustment coefficient of the phase perturbation amplitude according to the weighted sum result of the frequency deviation value and the battery health state attenuation coefficient. When performing the weighted sum, the weights of the frequency deviation value and the health state are preset through the analysis of the historical operation data of the grid operator. For example, based on the statistical correlation between grid stability and battery life in the past year, the weight of the frequency deviation value is set to 0.7, and the weight of the health state is set to 0.3.

[0113] The calculation method of the dynamic adjustment coefficient of the phase perturbation amplitude is: multiply the grid frequency deviation value collected in step S1 and the battery health state attenuation coefficient obtained in step S2 by the preset weights respectively and then add them. The weight of the frequency deviation value is determined according to the statistical data of the historical operation of the grid. For example, when the proportion of the impact of frequency deviation on stability is 70%, the weight is set to 0.7; the weight of the health state is set according to the degree of impact of battery aging on life. For example, when the proportion is 30%, the weight is 0.3. For example, if the frequency deviation value is -0.2 Hz and the battery health state 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 (benchmark value × 1.1). The weight data is stored in the configuration file of the grid control server and is called by the look-up table method.

[0114] Scale the phase perturbation amplitude reference value in the adaptive phase perturbation rule based on the dynamic adjustment coefficient of the phase perturbation amplitude. The specific method of scaling is to multiply the original phase perturbation amplitude reference value by the dynamic adjustment coefficient of the phase perturbation amplitude. For example, if the original reference value is 10 milliseconds and the dynamic adjustment coefficient of the phase perturbation amplitude is 1.2, the scaled reference value is 12 milliseconds. The range of the scaled reference value is limited within the maximum allowable perturbation amplitude of the bidirectional power converter. The maximum allowable perturbation amplitude is obtained from the device technical specifications of the bidirectional power converter. For example, the technical specifications of a certain model converter stipulate that the maximum allowable perturbation amplitude is 50 milliseconds. If the scaled reference value exceeds this limit, it is forced to be corrected to the maximum value of 50 milliseconds. The correction logic is implemented through a conditional judgment statement, such as "if the scaled value > 50 milliseconds, then the scaled value = 50 milliseconds".

[0115] Control the bidirectional power converter to perform charge and discharge operations according to the corrected charge and discharge power instruction set, and at the same time replace the original rule with the updated phase perturbation rule. The corrected charge and discharge power instruction set is transmitted to the bidirectional power converter through the Controller Area Network (CAN) bus protocol, and the message format of the CAN bus conforms to the ISO 11898 standard of the International Organization for Standardization. When replacing the original rule, an atomic write operation is used to ensure data integrity, and the atomic write operation is implemented through the transaction characteristics of the file system. The updated phase perturbation rule takes effect immediately, and the phase perturbation rule generated in the subsequent step S5 is executed based on the updated phase perturbation amplitude reference value.

[0116] This embodiment improves the power grid stability and equipment life through the synergistic effect of the following technical features: First, by integrating the power grid frequency deviation and the battery health state parameters in the dynamic time-of-use electricity price period division, it solves the problem that the scheduling instructions are disconnected from the real-time state of the power grid caused by the traditional method relying on a single electricity price signal; Second, based on the swept-frequency test, the resonant frequency band is extracted and an asymmetric phase perturbation rule is generated, and the resonant risk is suppressed by actively injecting a phase offset. Compared with the passive filtering or fixed threshold truncation scheme in the prior art, the harmonic amplification effect is significantly reduced; Third, through the dynamic time window segmentation and priority sorting mechanism, it is ensured that the batteries with high health status are preferentially scheduled during high electricity price periods, avoiding the accelerated aging of low-health batteries due to overcharge and over-discharge, while the traditional average distribution strategy cannot achieve life balance; Finally, by introducing the dynamic weight coefficients of the frequency deviation and the battery health state in the phase perturbation amplitude adjustment, a closed-loop feedback control is formed, so that the perturbation rule can be adapted to the changes in the power grid state in real time, the power grid frequency fluctuation is reduced, the resonant failure rate is decreased, and the battery life is extended.

[0117] It should be noted that the present invention is particularly applicable to the distribution network scenario containing an ultra-high voltage transmission system. Its asymmetric phase perturbation mechanism can effectively block the transmission of local resonant energy to the AC network above 750 kV, providing underlying perturbation suppression support for the large-scale power grid security guarantee and defense system, and improving the refined management and control ability of the intelligent scheduling system for distributed resources.

[0118] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the real situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0119] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC or other terminals with a user interface, so as to meet various hardware environments and usage requirements.

[0120] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. 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, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

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

[0122] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

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

[0124] In addition, in each embodiment of the present application, each functional module can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0125] If the above-mentioned function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0126] As mentioned above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0127] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should all be included within the protection scope of the present invention.

Claims

1. An evaluation method for the regulation potential of electric vehicle V2G based on electricity price regulation, characterized in that It includes the following steps: S1. Obtain the frequency deviation value of the target area power grid and the dynamic electricity price period division signal; S2. Synchronously collect the battery parameters of each electric vehicle battery, where the battery parameters include the available capacity and the health status parameters; S3. According to the dynamic electricity price period division signal combined with the battery parameters, calculate the maximum adjustable charging and discharging power range of the electric vehicle cluster in each period, and generate an initial charging and discharging power instruction set; S4. Collect the impedance spectrum characteristics of the power grid and extract the resonance frequency distribution interval of the target area power grid; S5. Within the execution time window of the initial charging and discharging power instruction set, generate an adaptive phase perturbation rule that matches the power grid resonance frequency point according to the resonance frequency distribution interval; S6. Based on the adaptive phase perturbation rule, perform an asymmetric offset adjustment on the power output timing of the initial charging and discharging power instruction set to generate a corrected instruction set; S7. Based on the corrected instruction set, control the bidirectional power converter to perform the corrected charging and discharging operation, and dynamically adjust the offset amplitude of the phase perturbation rule.

2. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, wherein S1 includes: Real-time collect the frequency deviation value of the target area power grid through a power grid monitoring device, where the frequency deviation value is the difference between the current frequency of the power grid and the rated frequency; Receive the dynamic electricity price period division signal issued by the power grid operator through a communication module, and the dynamic electricity price period division signal contains the time stamps of the peak and valley periods of the electricity price and the corresponding electricity price incentive levels; Perform time synchronization alignment processing on the frequency deviation value and the dynamic electricity price period division signal to generate a power grid state data set with consistent time stamps.

3. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, wherein S2 It includes: Real-time collect the current remaining power value and the battery health status attenuation coefficient of each electric vehicle battery through an on-vehicle battery monitoring device. The current remaining power value is the percentage value of the current available capacity of the battery to the nominal capacity, and the battery health status attenuation coefficient is calculated through the battery cycle times and the internal resistance change rate; Perform time stamp alignment processing on the data collected by each on-vehicle battery monitoring device through a time synchronization protocol to generate a battery parameter data set with consistent time stamps. The battery parameter data set contains the current remaining power value and the battery health status attenuation 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, wherein S3 includes: Based on the dynamic electricity price period division signal in the power grid state data set, determine the start time stamp and the electricity price incentive level of each period; According to the current remaining power value in the battery parameter data set, calculate the upper and lower limits of the initial charging and discharging power of each electric vehicle in each period; Perform secondary constraint on the upper and lower limits of the initial charging and discharging power according to the battery health status attenuation coefficient to generate the health status corrected charging and discharging power range of each vehicle; Sort the health status corrected charging and discharging power ranges of all electric vehicles in the same period according to the electricity price incentive level, and the priority sorting is that the higher the electricity price incentive level, the higher the vehicle sorting; Based on the priority sorting result, superimpose the charging and discharging power ranges of each vehicle to generate the maximum adjustable charging and discharging power range of the cluster in the corresponding period; Generate an initial charging and discharging power instruction set, which is a structured data table containing the start time stamp of each period, the maximum adjustable charging and discharging power range of the cluster, and the vehicle identification code.

5. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, wherein S4 includes: Inject a swept-frequency test signal into the target area power grid through a power grid monitoring device, and collect the voltage and current response data of each node of the power grid; Calculate the impedance spectrum characteristics of the power grid based on the voltage and current response data. The impedance spectrum characteristics include the impedance amplitude and phase angle at each frequency point; According to the curves of the impedance amplitude and phase angle changing with frequency, identify the frequency bands corresponding to the impedance amplitude lower than the first preset threshold and the phase angle zero-crossing points, and extract them as the resonant frequency distribution intervals; Cluster and merge adjacent frequency bands in the resonant frequency distribution interval. The merging condition is that the impedance amplitude difference between adjacent frequency bands is less than the second preset threshold and the phase angle change directions are the same; According to the merged resonant frequency distribution interval, calculate the oscillation risk weight coefficient for each frequency band. The oscillation risk weight coefficient is the product of the frequency band width and the impedance amplitude attenuation slope; Determine the dominant resonant frequency point as the frequency band with the highest oscillation risk weight coefficient. The frequency band width corresponding to the dominant resonant frequency point is the effective suppression range of the resonant frequency distribution interval.

6. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, wherein S5 Including: Divide the execution time window of the initial charge-discharge power instruction set into multiple consecutive sub-windows. The length of the sub-windows is dynamically adjusted according to the frequency band width corresponding to the dominant resonant frequency point in the effective suppression range of the resonant frequency distribution interval; Generate the phase perturbation amplitude reference value for each sub-window according to the oscillation risk weight coefficient of the dominant resonant frequency point. The phase perturbation amplitude reference value is positively correlated with the oscillation risk weight coefficient; Determine the phase perturbation direction of each sub-window based on the positive and negative nature of the phase angle change direction in the resonant frequency distribution interval; Generate a random phase perturbation factor within the sub-window according to the phase perturbation amplitude reference value and direction. The amplitude of the random phase perturbation factor fluctuates around the phase perturbation amplitude reference value and does not exceed the preset fluctuation range; Arrange the random phase perturbation factors of each sub-window in chronological order to generate an adaptive phase perturbation rule matching the power grid resonant frequency point.

7. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, characterized in that S6 Including: Analyze the timestamp, phase perturbation amplitude reference value, and phase perturbation direction in the adaptive phase perturbation rule; Perform an asymmetric offset adjustment on the instruction timestamps of the initial charge-discharge power instruction set according to the phase perturbation direction. The offset direction is to advance or delay the timestamp, and the offset amount is the phase perturbation amplitude reference value of the sub-window to which the corresponding timestamp belongs; The difference between the adjusted timestamp and the original timestamp does not exceed the hardware response delay threshold of the bidirectional power converter; Reconstruct the power output timing sequence based on the adjusted timestamp to generate a corrected charge-discharge power instruction set.

8. The method for evaluating the V2G regulation potential of electric vehicles based on electricity price regulation according to claim 1, wherein S7 includes: Calculate the phase perturbation amplitude dynamic adjustment coefficient according to the weighted sum result of the frequency deviation value and the battery health state attenuation coefficient. The weighting weights are the frequency deviation value weight and the health state weight; Scale the phase perturbation amplitude reference value in the adaptive phase perturbation rule proportionally based on the phase perturbation amplitude dynamic adjustment coefficient to generate an updated phase perturbation rule; Control the bidirectional power converter to perform charge-discharge operations according to the corrected charge-discharge power instruction set, and at the same time replace the original rule with the updated phase perturbation rule.

Images