Control system and method based on charging pile V2G

Abstract

The invention discloses a control system and method based on charging pile V2G, and relates to the technical field of charging pile V2G control, and the method comprises the following steps: firstly, a sensor device obtains the operation state parameters of a power grid in real time, accurately monitors the actual working condition of the power grid through the real-time collection of the operation parameter data, and provides basic data support for the subsequent analysis; based on deep learning and intelligent feedback control, the self-adaptive adjustment of the output voltage and the phase angle of the electric vehicle is realized, the reactive power is accurately compensated, the power grid voltage fluctuation is reduced, and the overall stability is improved. According to the scheme, the response speed of the EV inverter is increased, millisecond-level power grid adaptation is achieved, the V2G cooperative regulation and control capacity is enhanced, dependence on traditional reactive compensation equipment is reduced, and the new energy consumption efficiency is improved. By optimizing a power regulation and control mechanism, deep fusion of the EV as distributed energy storage is promoted, development of smart energy management is promoted, and the economic value and feasibility of the V2G in the power market are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of V2G control for charging piles, and particularly to a control system and method based on V2G for charging piles. Background Art

[0002] V2G (Vehicle-to-Grid) control based on charging piles refers to an intelligent control method that uses electric vehicles (EVs) as energy storage units to perform energy interaction between the power grid and vehicles through bidirectional charging and discharging technology. Specifically, electric vehicles charge from the power grid during low power demand periods (such as at night), and during peak grid loads or power shortages, they can discharge power back to the grid through charging piles to provide power support, thereby achieving peak shaving and valley filling of the power grid, improving the consumption capacity of new energy, and enhancing the stability of the power grid. V2G control usually involves technologies such as intelligent charging scheduling, load forecasting, dynamic electricity price strategies, and communication protocols (such as OCPP, ISO15118) to ensure efficient coordination among charging piles, EVs, and the power grid, achieve optimal energy management, and at the same time provide economic benefits for vehicle owners (such as peak-valley electricity price arbitrage or auxiliary service compensation).

[0003] The prior art has the following deficiencies: When an electric vehicle (EV) discharges power to the grid, the voltage must be synchronized with the grid to ensure grid stability. Otherwise, voltage fluctuations may impact the grid and affect the normal power consumption of other users. The EV dynamically adjusts the voltage through a feedback control system to maintain synchronization, but voltage regulation alone cannot fully stabilize the grid. When the grid load changes, it is also necessary to adjust the phase angle to change the output reactive power to compensate for the power factor and reduce voltage fluctuations. However, the prior art cannot achieve adaptive phase angle adjustment, resulting in the EV possibly being unable to provide sufficient reactive power compensation, thereby causing significant voltage fluctuations in the grid. Such fluctuations will directly affect all devices connected to the grid, especially critical facilities sensitive to voltage, such as hospital life support equipment, precision industrial production lines, communication base stations, and data center servers, and may lead to equipment failures, production interruptions, large-scale communication outages, and even endanger personal safety.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a control system and method based on V2G of charging piles. Based on deep learning and intelligent feedback control, it realizes the adaptive adjustment of the output voltage and phase angle of electric vehicles (EVs), accurately compensates reactive power, reduces the voltage fluctuation of the power grid, and improves the overall stability. This solution improves the response speed of the EV inverter, realizes grid adaptation at the millisecond level, enhances the V2G collaborative regulation ability, reduces the dependence on traditional reactive power compensation equipment, and improves the new energy consumption efficiency. By optimizing the power regulation mechanism, it promotes the deep integration of EVs as distributed energy storage, promotes the development of smart energy management, and enhances the economic value and feasibility of V2G in the power market, providing innovative solutions for power grid dispatching, energy trading, and renewable energy utilization to solve the problems in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A control method based on V2G of charging piles, including the following steps: First, sensor devices obtain the grid operation state parameters in real time. By collecting the operation parameter data in real time, the actual working conditions of the grid are accurately monitored, providing basic data support for subsequent analysis. After obtaining the real-time grid operation parameters, the obtained data is integrated to form an analysis set. To ensure the data quality, the obtained data is preprocessed to improve the accuracy and consistency of the data, laying a foundation for subsequent analysis. Key features reflecting the grid operation fluctuations are extracted from the preprocessed data, and the extracted key features are analyzed and processed under a detection window. The key features after analysis are input into a pre-trained deep learning model, and the deep learning model is used to intelligently evaluate the grid operation fluctuations. When the model detects serious fluctuations in the grid, the bidirectional charging and discharging inverter of the electric vehicle will start a feedback control system. According to the deviation between the real-time grid voltage and the reference voltage, a closed-loop control algorithm is adopted to dynamically adjust the EV output voltage to keep it consistent with the grid, thereby reducing the voltage impact. At the same time, the phase angle of the EV output power is further adjusted according to the degree of grid fluctuation, thereby changing the reactive power and compensating the power factor of the grid.

[0007] Preferably, the specific steps of obtaining the grid operation state parameters in real time by sensor devices are as follows: First, intelligent sensors are deployed at key nodes of the grid, and the sensors will collect the grid operation parameters in real time. Subsequently, the data collected by the sensors is transmitted to the data acquisition server or cloud platform through wireless communication, fiber optic network or power line carrier communication, where the data is stored and processed. At the same time, the data will undergo time synchronization to ensure the time consistency of all sampling points, so as to accurately reflect the transient dynamic characteristics of the grid.

[0008] Preferably, key features reflecting the operation fluctuations of the power grid are extracted from the preprocessed data. The extracted features include the degree of mutation of the power grid line impedance within a short period of time and the degree of change of the negative sequence voltage component of the power grid. The degree of mutation of the power grid line impedance within a short period of time and the degree of change of the negative sequence voltage component of the power grid are analyzed under a detection window, and a line impedance mutation reference value and a negative sequence voltage fluctuation reference value are respectively generated. The stability of the power grid within a short period of time and the power quality status are quantified through the line impedance mutation reference value and the negative sequence voltage fluctuation reference value.

[0009] Preferably, the specific steps for analyzing the degree of mutation of the power grid line impedance within a short period of time under a detection window to generate a line impedance mutation reference value are as follows: Within the monitoring window, calculate the mutation rate of the line impedance within a short period of time to capture the drastic change of the line impedance, and quantify the transient change trend of the line impedance by calculating the impedance change rate factor. The calculation expression is as follows: , where in the formula, and respectively represent the maximum value and the minimum value of the line impedance within the detection window, and respectively represent the maximum and minimum complex powers of the power grid within the detection window, is an extremely small number to prevent the denominator from approaching zero and causing unstable calculation, is the impedance change rate factor; After obtaining the line impedance change rate factor , further introduce the dynamic impact factor of the impedance, and construct a line impedance mutation reference value through the high-order non-linear relationship between the power grid voltage and the impedance to quantitatively represent the fluctuation intensity of the line impedance. The calculation expression is as follows: , where in the formula, is the dynamic impact factor, measuring the direct impact of the line impedance mutation on the power grid voltage fluctuation, and respectively are the maximum voltage and the minimum voltage within the detection window, used to depict the voltage fluctuation degree, is the equivalent impedance ratio of the line, is the equivalent reactance, is the equivalent resistance, is the sensitivity coefficient of reactive power to voltage, representing the degree of reactive power response caused by voltage change, is the reactive power, is the voltage, is the adjustment factor, is the line impedance mutation reference value.

[0010] Preferably, the specific steps for analyzing the degree of change in the negative-sequence voltage component of the power grid under the detection window to generate the negative-sequence voltage fluctuation reference value are as follows: In the three-phase voltage system of the power grid, the symmetrical component method is used to calculate the negative-sequence voltage component, and the calculation expression is as follows: , where 、 、 are the three-phase voltages, is the rotation factor of the three-phase symmetrical transformation, is the negative-sequence voltage component; The change rate of the negative-sequence voltage is an important index to measure volatility. The discrete differential form is used to calculate the negative-sequence voltage volatility, and the calculation expression is as follows: , where is the negative-sequence voltage volatility, and are the negative-sequence voltage components calculated at time and time , is the equivalent impedance, which is the equivalent impedance of the power grid between time and time , is an extremely small number to prevent the denominator from approaching zero and causing calculation instability, is the maximum operator; In order to comprehensively quantify the dynamic instability of the negative-sequence voltage, it is necessary to combine its volatility, phase angle change and system impedance characteristics to calculate the negative-sequence voltage fluctuation reference value. The calculation expression is as follows: , where is the negative-sequence voltage fluctuation reference value, is the change amount of the negative-sequence voltage phase angle at the th node of the power grid, is the total number of nodes, is the th , is the non-linear adjustment factor, is the total number of lines, is a small amount to avoid a zero denominator.

[0011] Preferably, the analyzed line impedance mutation reference value and the negative-sequence voltage fluctuation reference value are input into a pre-trained deep learning model. The deep learning model generates the power grid fluctuation risk coefficient, and the power grid fluctuation state is intelligently evaluated through the power grid fluctuation risk coefficient.

[0012] Preferably, when the grid fluctuation risk coefficient generated by the pre-trained deep learning model for intelligent evaluation of the grid fluctuation state is compared with the pre-set reference threshold of the grid fluctuation risk coefficient, the grid fluctuation is classified, and the classification steps are as follows: If the grid fluctuation risk coefficient is greater than the reference threshold of the grid fluctuation risk coefficient, it indicates that there is a serious fluctuation in the current grid operation; if the grid fluctuation risk coefficient is less than or equal to the reference threshold of the grid fluctuation risk coefficient, it indicates that there is no fluctuation in the current grid operation.

[0013] Preferably, when the model detects a serious grid fluctuation, the specific steps for dynamically adjusting the EV output voltage and further adjusting the phase angle of the EV output power according to the degree of grid fluctuation are as follows: When the grid fluctuation risk coefficient exceeds the set threshold, first perform closed-loop feedback control on the real-time grid voltage. Let the reference voltage be and the actually collected voltage be , calculate the voltage error, and the calculation expression is as follows: , where is the voltage error; Adopt a closed-loop control algorithm, and the control signal formula is as follows: , where is the EV output voltage compensation value, is the proportional gain coefficient, is the integral gain coefficient, is the error integral term, and the voltage error is the integral of the voltage error in time, that is, the cumulative sum of historical errors, is the integral variable; On the basis of completing the voltage regulation, it is necessary to additionally adjust the phase angle of the EV output power to change the reactive power output. First, introduce the phase angle compensation formula: , where is the reference threshold of the grid fluctuation risk coefficient, is the phase angle adjustment gain, is the phase angle increment that the EV inverter needs to adjust; Then, perform phase angle update, and the updated phase angle calculation formula is as follows: , where is the updated EV inverter output phase angle, is the current phase angle of the EV inverter; By adjusting the phase angle , the EV inverter dynamically changes the reactive power output, realizes the compensation of the grid power factor, and further reduces the system risk caused by voltage fluctuation. The calculation expression is as follows: , where is the reactive power provided by the EV inverter to the power grid, is the output voltage of the EV inverter, is the current output by the EV inverter, is the key factor determining the reactive power component; Finally, the dual regulation of the EV's output voltage and phase angle will be integrated into the output control signal in complex form to achieve coordinated compensation of amplitude and phase. The calculation expression of the comprehensive control law is as follows: , where, is the EV output control signal, is the phase angle compensation factor adjusted by the EV inverter, is the natural base, is the imaginary unit.

[0014] Preferably, the control system based on the charging pile V2G includes a power grid status monitoring module, a data processing and preprocessing module, a feature extraction and analysis module, a deep learning evaluation module, and an EV feedback control and phase angle optimization module: The power grid status monitoring module. First, the sensor device obtains the power grid operation status parameters in real time. By collecting the operation parameter data in real time, it accurately monitors the actual working conditions of the power grid and provides basic data support for subsequent analysis; The data processing and preprocessing module. After obtaining the real-time operation parameters of the power grid, it integrates the obtained data to form an analysis set. To ensure the data quality, it preprocesses the obtained data to improve the accuracy and consistency of the data and lays a foundation for subsequent analysis; The feature extraction and analysis module extracts the key features reflecting the power grid operation fluctuations from the preprocessed data and analyzes and processes the extracted key features under the detection window; The deep learning evaluation module inputs the key features after analysis into the pre-learned deep learning model, and intelligently evaluates the power grid operation fluctuations through the deep learning model; The EV feedback control and phase angle optimization module. When the model detects serious fluctuations in the power grid, the bi-directional charging and discharging inverter of the electric vehicle will start the feedback control system. According to the deviation between the real-time voltage of the power grid and the reference voltage, it uses the closed-loop control algorithm to dynamically adjust the EV output voltage to keep it consistent with the power grid, thereby reducing the voltage impact. At the same time, it further adjusts the phase angle of the EV output power according to the degree of power grid fluctuations, thereby changing the reactive power and compensating the power factor of the power grid.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention: The present invention is based on deep learning-driven dynamic power grid fluctuation assessment and intelligent feedback control, enabling electric vehicles (EVs) to adaptively adjust the output voltage and phase angle, thereby accurately compensating reactive power, reducing the impact of power grid voltage fluctuations on critical loads, and improving the overall stability of the power grid. This solution optimizes the response speed of the EV inverter, enabling it to quickly adapt to power grid load changes within milliseconds. At the same time, it enhances the collaborative control ability of V2G devices, reduces the dependence on traditional reactive power compensation devices, and improves the consumption efficiency of new energy. In addition, the present invention provides an efficient power regulation mechanism for smart grids, promotes the deep integration of EVs as distributed energy storage resources, drives the development of intelligent energy management systems, and enhances the economic value and technical feasibility of V2G in future power markets, providing innovative solutions for power grid dispatching optimization, energy trading, and renewable energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of the control method based on V2G of the charging pile according to the present invention.

[0018] Figure 2 It is a schematic diagram of the modules of the control system based on V2G of the charging pile according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more complete and comprehensive, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0020] The present invention provides a control method based on V2G of the charging pile as Figure 1 shown, including the following steps: First, the sensor device obtains the power grid operation state parameters in real time. By collecting the operation parameter data in real time, it accurately monitors the actual working conditions of the power grid, providing basic data support for subsequent analysis; The specific steps to obtain the grid operation status parameters in real time through sensor devices are as follows: First, deploy intelligent sensors (such as smart meters, synchronized phasor measurement units PMU, voltage / current sensors, etc.) at key nodes of the grid (such as substations, distribution lines, user sides). These sensors will collect grid operation parameters in real time, including voltage, current, phase angle, frequency, power factor, reactive power, load changes, etc. Subsequently, the data collected by the sensors will be transmitted to the data acquisition server or cloud platform through wireless communication (5G / LoRa), fiber optic network or power line carrier communication (PLC), where data storage and preliminary processing will be carried out. At the same time, the data will undergo time synchronization (such as GPS time synchronization) to ensure the time consistency of all sampling points, so as to accurately reflect the transient dynamic characteristics of the grid. This series of operations not only ensures the real-time and integrity of grid operation information, but also provides reliable data support for subsequent data analysis, feature extraction, deep learning model evaluation and grid control.

[0021] Timely grasp the operation status and sudden changes of the grid, and provide the most real input for subsequent deep learning models and control strategies. Microsecond-level data synchronization can be achieved through PMU (Phasor Measurement Unit), and the load information can be periodically transmitted back using smart meters to ensure the time consistency and integrity of the data, laying a foundation for big data analysis.

[0022] After obtaining the real-time grid operation parameters, integrate the obtained data to form an analysis set. To ensure data quality, preprocess the obtained data to improve the accuracy and consistency of the data, laying a foundation for subsequent analysis.

[0023] After obtaining the real-time grid operation parameters, it is necessary to integrate and preprocess these data to ensure the accuracy, consistency and availability of the data. Since sensor devices may be affected by factors such as environmental noise, communication delay, and data loss, the directly obtained raw data may contain noise, missing values, duplicate values or outliers. Therefore, first, denoise the data to filter out abnormal data caused by sensor errors or sudden interferences; second, perform time alignment and interpolation to ensure the consistency of timestamps of data from different sensors and avoid affecting the analysis results due to data missing; then, normalize or standardize the data to make data with different units and ranges comparable and prevent large numerical differences from affecting the subsequent modeling effect. Through these preprocessing steps, the obtained grid data can be transformed into a high-quality analysis set, providing accurate and reliable data support for subsequent feature extraction, pattern recognition, deep learning analysis and intelligent control.

[0024] Extract the key features reflecting the grid operation fluctuations from the preprocessed data, and analyze and process the extracted key features under the detection window; Extract key features reflecting the fluctuations of power grid operation from the preprocessed data. The extracted features include the degree of sudden change in the power grid line impedance within a short period of time and the degree of change in the negative sequence voltage component of the power grid. Analyze the degree of sudden change in the power grid line impedance within a short period of time and the degree of change in the negative sequence voltage component of the power grid under the detection window, and generate a reference value for sudden change in line impedance and a reference value for negative sequence voltage fluctuation respectively. Quantify the stability and power quality status of the power grid within a short period of time through the reference value for sudden change in line impedance and the reference value for negative sequence voltage fluctuation.

[0025] A sudden increase in the power grid line impedance within a short period of time may indicate that there are serious fluctuations in the power grid during the current process of electric vehicles (EVs) supplying power to the grid. The fundamental reason is that a sharp change in the power grid impedance usually means a sudden change in the load condition, power supply capacity, or topological structure of the power grid. From the perspective of power grid fluctuations, a rapid increase in line impedance may be caused by factors such as power grid short circuits, transformer switching, sudden disconnection of large-power loads, or remote line failures. These changes will lead to sudden voltage drops, current mutations, and even power grid frequency fluctuations. For the V2G (Vehicle-to-Grid) system, a sudden increase in the power grid impedance means a decrease in the impedance matching degree between the EV inverter and the power grid, which may lead to a decline in the voltage regulation ability of the inverter, an inability to provide stable power output, and even the occurrence of inverter protection actions and disconnection from the grid. At the same time, the sudden change in line impedance may also exacerbate the reactive power imbalance in the power grid, further affecting voltage stability, resulting in voltage oscillations and an increase in negative sequence components, thereby affecting the power supply quality of the entire region. Therefore, during the process of electric vehicles supplying power to the grid, if the line impedance abnormally increases within a short period of time, it usually indicates that the power grid is experiencing serious dynamic fluctuations, which may affect the stability and power quality of the system, and rapid control measures need to be taken to prevent a wider range of power grid instability phenomena.

[0026] The specific steps for analyzing the degree of sudden change in the power grid line impedance within a short period of time under the detection window to generate a reference value for sudden change in line impedance are as follows: Within the monitoring window, calculate the sudden change rate of the line impedance within a short period of time to capture the drastic changes in the line impedance, and quantify the transient change trend of the line impedance by calculating the impedance change rate factor. The calculation expression is as follows: , where and represent the maximum and minimum values of the line impedance within the detection window respectively, used to reflect the fluctuation range of the line impedance, and represent the maximum and minimum complex powers of the power grid within the detection window respectively, is an extremely small number to prevent the denominator from approaching zero and causing calculation instability, is the impedance change rate factor; The purpose of this step is to measure the maximum disturbance degree of the line impedance within the detection window, and at the same time introduce the power factor to make it consider the dynamic changes of the grid load. If the power fluctuates violently, even if the impedance change is small, this factor will increase, so as to more accurately reflect the impact of impedance change on the grid stability.

[0027] After obtaining the line impedance change rate factor , a dynamic impact factor of the impedance is further introduced. A reference value for the sudden change of the line impedance is constructed through the high-order non-linear relationship between the grid voltage and the impedance to quantitatively represent the fluctuation intensity of the line impedance. The calculation expression is as follows: , where in the formula, is the dynamic impact factor, which measures the direct impact of the sudden change of the line impedance on the grid voltage fluctuation, and are the maximum voltage and the minimum voltage within the detection window respectively, which are used to describe the voltage fluctuation degree, is the equivalent impedance ratio of the line, is the equivalent reactance, is the equivalent resistance. This ratio reflects the inductive or resistive characteristics of the line and affects the degree of the impact of impedance sudden change on the grid voltage, is the sensitivity coefficient of the reactive power to the voltage, which represents the response degree of the reactive power caused by the voltage change, is the reactive power, is the voltage, is the adjustment factor to prevent the denominator from approaching zero in extreme cases, is the reference value for the sudden change of the line impedance.

[0028] This step synthesizes the impedance change, voltage fluctuation, line impedance characteristics and reactive power response ability, and can accurately quantify the impact of the sudden change of the line impedance on the grid fluctuation. When has a large value, it means that the grid has experienced relatively violent fluctuations, and the EV inverter may need to intervene to adjust the reactive power or adjust the power output strategy; when has a small value, it indicates that the grid is operating relatively stably and does not require additional adjustment. This reference value can not only be used for monitoring the grid state during the V2G process, but also for application scenarios such as new energy grid connection and intelligent dispatching, improving the sensitivity and accuracy of grid fluctuation detection.

[0029] The greater the reference value of line impedance mutation generated after analyzing the degree of sudden change of the power grid line impedance within a short time window, the more severe the fluctuations in the power grid during the current power supply process of the electric vehicle (EV) to the power grid. From the perspective of power grid stability, a sharp change in line impedance within a short time usually means load changes, short-term faults, reactive power fluctuations, or power supply topology adjustments within the power grid, all of which will affect the stability of the power grid voltage. When the value of the reference value of line impedance mutation increases, it means that the impedance of the power grid has changed sharply, which may lead to fluctuations in the power output of the EV inverter, thereby affecting the overall power quality of the power grid. For example, if the power grid load suddenly decreases and the line impedance increases, the power output of the EV may face the risk of voltage increase or even inverter disconnection from the grid; conversely, if the load suddenly increases or a short-circuit fault occurs and the line impedance drops sharply, the EV may require additional reactive power compensation to maintain power grid stability. Therefore, the higher the reference value of line impedance mutation, the more severe the power grid fluctuations and the worse the voltage stability, and a higher-level regulation strategy is required during the EV power supply process to avoid power grid instability.

[0030] A sudden increase in the negative sequence voltage component of the power grid may indicate severe fluctuations in the power grid during the current power supply process of the electric vehicle (EV) to the power grid, but it needs to be comprehensively judged in combination with other indicators. The negative sequence voltage component mainly reflects the degree of three-phase imbalance of the power grid. When it suddenly rises, it means that the power grid may have load imbalance, asymmetric inverter output, access of non-linear loads, or local power grid faults. If the negative sequence voltage component suddenly increases during the EV power supply process, it may mean that the output voltage of the EV inverter is not matched in three phases, resulting in power imbalance in the power grid, thereby exacerbating voltage fluctuations. In addition, if the power grid is originally in a heavy load state or has a relatively high background value of negative sequence voltage, the EV power supply may further deteriorate the voltage imbalance, causing more severe fluctuations and affecting the stable operation of other grid-connected devices. For example, motors and power conversion equipment have high requirements for three-phase balance. When the negative sequence voltage is too large, these devices may experience abnormal operation, increased vibration, or even damage. Therefore, when a sudden increase in the negative sequence voltage component is detected, it is necessary to further confirm the severity of the power grid fluctuations by combining parameters such as line impedance changes, voltage transient rate, and phase angle fluctuations, and optimize the control strategy of the EV inverter to ensure the stability of grid-connected power supply and power quality.

[0031] The specific steps for generating the reference value of negative sequence voltage fluctuation by analyzing the degree of change of the negative sequence voltage component of the power grid within a detection window are as follows: In the three-phase voltage system of the power grid, the negative sequence voltage component is used to characterize the degree of three-phase imbalance. The negative sequence voltage component is calculated using the symmetrical component method, and the calculation expression is as follows: , where 、 、 is the three-phase voltage, is the rotation factor of the three-phase symmetrical transformation, is the negative-sequence voltage component; This step converts the three-phase voltage into positive-sequence, negative-sequence, and zero-sequence components, where the negative-sequence voltage component mainly reflects the asymmetry of the three-phase voltage. The larger the value, the more serious the three-phase imbalance of the power grid, which may further lead to power grid fluctuations when electric vehicles are connected to the grid.

[0032] The rate of change of the negative-sequence voltage is an important indicator to measure the volatility. The discrete differential form is used to calculate the negative-sequence voltage volatility, and the calculation expression is as follows: , where in the formula, is the negative-sequence voltage volatility, and are the negative-sequence voltage components calculated at time and time , is the equivalent impedance, which is the equivalent impedance of the power grid between time and time , is an extremely small number to prevent the denominator from approaching zero, resulting in calculation instability and avoiding singular situations in mathematical calculations, is the maximum operator, which selects the maximum value among the volatilities calculated between all times; This step quantifies the dynamic fluctuation degree of the power grid by measuring the maximum ratio of the change in the negative-sequence voltage. If is too large, it means that during the power supply process of electric vehicles, the negative-sequence voltage fluctuates violently, which may cause a decline in the power quality of the power grid and misoperation of relay protection. Compared with the traditional standard deviation calculation, this method combines the influence of the equivalent impedance and can more accurately reflect the fluctuation transmission characteristics inside the power grid.

[0033] In order to comprehensively quantify the dynamic instability of the negative-sequence voltage, it is necessary to combine its volatility, phase angle change, and system impedance characteristics to calculate the negative-sequence voltage fluctuation reference value. The calculation expression is as follows: , where in the formula, is the negative-sequence voltage fluctuation reference value, is the change in the negative-sequence voltage phase angle of the th node of the power grid, is the total number of nodes, is the th , is the non-linear adjustment factor, which is used to amplify the influence of the high-impedance path on the voltage fluctuation, is the total number of lines, It is a small quantity to avoid a zero denominator and prevent calculation instability.

[0034] This formula comprehensively considers the negative-sequence voltage volatility ( ), phase angle change ( ), and grid impedance distribution ( ) to provide a comprehensive quantification index for negative-sequence voltage fluctuations. When has a high value, it indicates that during the current process of electric vehicles supplying power to the grid, the negative-sequence voltage fluctuation of the grid is severe, the three-phase imbalance is significant, which may affect the power factor of the grid and even trigger relay protection actions or power equipment failures.

[0035] The larger the reference value of negative-sequence voltage fluctuation generated by analyzing the change degree of the negative-sequence voltage component of the grid under the detection window, the more severe the fluctuation of the grid during the current process of electric vehicles (EVs) supplying power to the grid. The negative-sequence voltage component reflects the degree of imbalance of the three-phase voltage of the grid, and the instability of the grid negative-sequence voltage is usually caused by factors such as asymmetric loads, uneven access of distributed power sources, unbalanced reactive power distribution, or short-circuit faults. When the reference value of negative-sequence voltage fluctuation is high, it means that the asymmetry of the three-phase voltage of the grid increases, which may lead to fluctuations in the output power of the EV inverter, thus affecting the grid connection stability. For example, if the negative-sequence voltage of the grid suddenly increases, the EV inverter may face higher reactive power demands and even trigger the protection mechanism of the inverter to cause disconnection from the grid. In addition, high negative-sequence voltage will also affect the motor equipment in the grid, reducing its operating efficiency and increasing losses. Therefore, the larger the reference value of negative-sequence voltage fluctuation, the more severe the grid fluctuation and the worse the stability of EVs supplying power to the grid. Measures such as reactive power compensation and phase angle adjustment need to be taken to improve the power quality. On the contrary, when this reference value is low or stable, it indicates that the grid operates smoothly during the EV power supply process, the three-phase voltage symmetry is good, and no significant fluctuations will be caused.

[0036] Input the analyzed key features into a pre-trained deep learning model, and conduct an intelligent evaluation of the grid operation fluctuations through the deep learning model; Input the analyzed reference value of line impedance mutation and the reference value of negative-sequence voltage fluctuation into a pre-trained deep learning model, generate a grid fluctuation risk coefficient through the deep learning model, and conduct an intelligent evaluation of the grid fluctuation state through the grid fluctuation risk coefficient.

[0037] A pre-trained deep learning model refers to a neural network model that has been trained with historical power grid data and already has a certain generalization ability. This model can automatically identify the power grid operation mode and evaluate the power grid fluctuation risk based on input features (such as reference values of line impedance mutation, reference values of negative sequence voltage fluctuation, etc.). Such models usually adopt supervised learning or semi-supervised learning methods and are trained using a large amount of power grid historical data (including normal operation data, fault data, fluctuation event data, etc.) to enable them to learn the complex non-linear relationship between features and fluctuation risks under different power grid states. To improve the adaptability of the model, techniques such as data augmentation, time series modeling, and feature selection are used during training to ensure that the model can not only accurately identify common patterns of power grid fluctuations but also accurately evaluate sudden abnormal situations. Compared with traditional rule-based methods, deep learning models can extract key features from high-dimensional data and automatically adapt to different power grid operating conditions, greatly improving the accuracy of power grid fluctuation prediction and evaluation. For example, LSTM (Long Short-Term Memory Network) or Transformer time series models can be used to learn the time dependence of power grid parameters and extract the long-term trend of power grid fluctuations, while CNN (Convolutional Neural Network) or GNN (Graph Neural Network) can be used to analyze local disturbance patterns in the power grid topology to improve the anomaly detection ability.

[0038] In practical applications, a pre-trained deep learning model will receive power grid state features such as reference values of line impedance mutation and reference values of negative sequence voltage fluctuation collected in real time, and calculate these input data through the neural network layer, and finally generate a power grid fluctuation risk coefficient. This risk coefficient is a quantitative index, usually expressed in the numerical form of 0-1 or 0-100, and is used to measure the current fluctuation degree of the power grid. For example, if the risk coefficient is close to 0, it means that the power grid is stable and there is no obvious fluctuation; if it is close to 100, it indicates that the power grid may be in a high-risk state and may experience short-term voltage collapse, reactive power imbalance, or equipment failure. This model can not only provide real-time fluctuation assessment but also predict the future short-term fluctuation trend in advance to give early warnings of possible power grid instability events, enabling the dispatching center or V2G equipment to take compensation measures in time. By introducing a deep learning evaluation mechanism, the power grid can achieve an intelligent transformation from passive response (only intervening after a fault occurs) to active prediction (perceiving fluctuations in advance and making adjustments), thereby improving the overall power supply security and system stability.

[0039] The deep learning model is not limited here, and any deep learning model that can comprehensively analyze the reference value of line impedance mutation and the reference value of negative sequence voltage fluctuation to generate a power grid fluctuation risk coefficient is acceptable. To implement the technical solution of the present invention, the present invention provides a specific implementation method; Power grid fluctuation risk coefficient The generation formula is as follows: , where and are respectively the reference values of the sudden change of line impedance and the reference value of negative-sequence voltage fluctuation of the preset proportionality coefficients, and and are both greater than 0.

[0040] The preset proportionality coefficients and refer to the relative contribution weights for balancing the reference value of the sudden change of line impedance and the reference value of negative-sequence voltage fluctuation when calculating the grid fluctuation risk coefficient . Since there may be significant differences in the numerical ranges, change trends, and the degrees of influence on grid stability of different features, directly performing a linear combination may cause the influence of a certain feature on the final result to be too large or too small. Therefore, and are introduced as adjustment parameters to adjust the contribution ratios of the reference value of the sudden change of line impedance and the reference value of negative-sequence voltage fluctuation to the calculation of the grid fluctuation risk coefficient , ensuring that the calculation result can reflect the combined influence of both and avoid a single indicator from dominating the entire risk assessment result. The values of these coefficients can usually be adjusted through statistical analysis, expert experience setting, or deep learning optimization to ensure that the grid fluctuation risk coefficient can accurately quantify the grid fluctuation risk and be applicable to different grid operating environments.

[0041] It can be seen from the grid fluctuation risk coefficient that the larger the reference value of the sudden change of line impedance generated by analyzing the degree of sudden change of the grid line impedance within the detection window in a short period of time, and the larger the reference value of negative-sequence voltage fluctuation generated by analyzing the degree of change of the grid negative-sequence voltage component within the detection window, the larger the grid fluctuation risk coefficient generated by the intelligent evaluation of the grid fluctuation state through the pre-trained deep learning model, indicating that the probability of a serious grid fluctuation is greater. On the contrary, it indicates that the probability of a serious grid fluctuation is smaller.

[0042] Compare and analyze the grid fluctuation risk coefficient generated by the intelligent evaluation of the grid fluctuation state through the pre-trained deep learning model with the pre-set reference threshold of the grid fluctuation risk coefficient to divide the grid fluctuation. The division steps are as follows: If the grid fluctuation risk coefficient is greater than the threshold value of the grid fluctuation risk coefficient reference value, it indicates that there is a serious fluctuation in the current grid operation; if the grid fluctuation risk coefficient is less than or equal to the threshold value of the grid fluctuation risk coefficient reference value, it indicates that there is no fluctuation in the current grid operation.

[0043] When the model detects a serious fluctuation in the grid, the bidirectional charging and discharging inverter of the electric vehicle will start the feedback control system. According to the deviation between the real-time grid voltage and the reference voltage, a closed-loop control algorithm (such as PI control, adaptive control, etc.) is adopted to dynamically adjust the EV output voltage to keep it consistent with the grid, thereby reducing the voltage impact. At the same time, according to the degree of grid fluctuation, the phase angle of the EV output power is further adjusted, thereby changing the reactive power and compensating the power factor of the grid; When the model detects a serious fluctuation in the grid, the specific steps for dynamically adjusting the EV output voltage and further adjusting the phase angle of the EV output power according to the degree of grid fluctuation are as follows: When the grid fluctuation risk coefficient exceeds the set threshold, first perform closed-loop feedback control on the real-time grid voltage. Let the reference voltage be and the actually collected voltage be , calculate the voltage error, and the calculation expression is as follows: , where is the voltage error; Adopt a closed-loop control algorithm (such as adaptive PI control), and the control signal formula is as follows: , where is the EV output voltage compensation value, indicating the voltage increment that the bidirectional inverter of the electric vehicle needs to adjust to make the EV output voltage consistent with the grid target voltage, is the proportional gain coefficient, indicating the proportional gain in the PI controller, which determines the influence degree of the voltage error on the output voltage adjustment, is the integral gain coefficient, indicating the integral gain in the PI controller, which determines the influence degree of the long-term voltage error on the output voltage adjustment, is the error integral term, the integral of the voltage error over time, that is, the cumulative sum of historical errors, is the integral variable; Through this step, the bidirectional charging and discharging inverter of the EV adjusts the output voltage according to the voltage deviation, making it quickly approach , thereby reducing the impact of voltage shock on the grid.

[0044] On the basis of completing the voltage regulation, it is necessary to additionally adjust the phase angle of the EV output power to change the reactive power output. First, introduce the phase angle compensation formula: , where is the reference threshold of the power grid fluctuation risk coefficient, is the phase angle regulation gain, is the increment of the phase angle that needs to be adjusted by the EV inverter, which determines the change in reactive power; Then, the phase angle is updated, and the calculation formula for the updated phase angle is as follows: , where, is the output phase angle of the updated EV inverter, is the current phase angle of the EV inverter; By adjusting the phase angle , the EV inverter dynamically changes the reactive power output, realizes the compensation of the power factor of the power grid, and further reduces the system risk caused by voltage fluctuations. The calculation expression is as follows: , where, is the reactive power provided by the EV inverter to the power grid, is the output voltage of the EV inverter, is the output current of the EV inverter, is the key factor determining the reactive power component. When increases (i.e., the phase angle increases), becomes larger, and the EV provides more reactive power to the power grid to make up for the reactive power gap of the power grid and improve voltage stability; when decreases, the EV inverter reduces the reactive power output to avoid excessive reactive power in the power grid; Finally, the dual regulation of the output voltage and phase angle of the EV will be integrated into the output control signal in complex form to achieve the coordinated compensation of amplitude and phase. The calculation expression of the comprehensive control law is as follows: , where, is the EV output control signal, is the phase angle compensation factor adjusted by the EV inverter, is the natural base, is the imaginary unit.

[0045] This step means that the inverter of the EV will adjust the amplitude and phase of its output power synchronously according to the voltage change adjusted by the closed-loop feedback and the phase angle compensation to ensure that the output voltage is synchronized with the power grid, effectively compensate the reactive power gap, and reduce the power grid fluctuation risk. This coordinated control strategy constitutes an advanced, dynamic, and composite control mechanism by simultaneously adjusting the amplitude and phase, ensuring that the power grid can still operate stably when facing severe fluctuation risks.

[0046] Based on deep learning-driven dynamic power grid fluctuation assessment and intelligent feedback control, the present invention enables electric vehicles (EVs) to adaptively adjust the output voltage and phase angle, thereby accurately compensating for reactive power, reducing the impact of power grid voltage fluctuations on critical loads, and improving the overall stability of the power grid. This solution optimizes the response speed of the EV inverter, enabling it to quickly adapt to power grid load changes within milliseconds. At the same time, it enhances the collaborative control ability of V2G devices, reduces the dependence on traditional reactive power compensation devices, and improves the consumption efficiency of new energy. In addition, the present invention provides an efficient power regulation mechanism for smart grids, promotes the deep integration of EVs as distributed energy storage resources, drives the development of intelligent energy management systems, and enhances the economic value and technical feasibility of V2G in future power markets, providing innovative solutions for power grid dispatching optimization, energy trading, and renewable energy utilization.

[0047] The present invention provides a control system based on V2G of charging piles as shown in Figure 2 and includes a power grid status monitoring module, a data processing and preprocessing module, a feature extraction and analysis module, a deep learning evaluation module, and an EV feedback control and phase angle optimization module: The power grid status monitoring module. First, sensor devices acquire the operation status parameters of the power grid in real time. By collecting the operation parameter data in real time, it accurately monitors the actual working conditions of the power grid, providing basic data support for subsequent analysis. The data processing and preprocessing module. After obtaining the real-time operation parameters of the power grid, it integrates the acquired data to form an analysis set. To ensure data quality, it preprocesses the acquired data to improve the accuracy and consistency of the data, laying a foundation for subsequent analysis. The feature extraction and analysis module extracts the key features reflecting the power grid operation fluctuations from the preprocessed data and analyzes and processes the extracted key features under the detection window. The deep learning evaluation module inputs the analyzed key features into a pre-trained deep learning model, and intelligently evaluates the power grid operation fluctuations through the deep learning model. The EV feedback control and phase angle optimization module. When the model detects serious fluctuations in the power grid, the bi-directional charging and discharging inverter of the electric vehicle will start the feedback control system. According to the deviation between the real-time voltage of the power grid and the reference voltage, it adopts a closed-loop control algorithm to dynamically adjust the EV output voltage to be consistent with the power grid, thereby reducing the voltage impact. At the same time, it further adjusts the phase angle of the EV output power according to the degree of power grid fluctuations, thereby changing the reactive power and compensating the power factor of the power grid.

[0048] The control method based on the charging pile V2G provided by the embodiment of the present invention is implemented through the above-mentioned control system based on the charging pile V2G. For the specific method and process of the control system based on the charging pile V2G, please refer to the embodiments of the control method based on the charging pile V2G above, and will not be elaborated here.

[0049] The above formulas are all dimensionless and only take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0050] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

[0051] It should be noted that in this article, if there are relational terms such as first and second, they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the element.

[0052] It should be understood that in various embodiments of the present application, the size of the serial numbers of the above processes does not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0053] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0054] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0055] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0056] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0057] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by 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.

[0058] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A control method based on charging pile V2G, characterized in that: The following steps are involved: First, the sensor equipment obtains the grid operation status parameters in real time. By collecting the operation parameter data in real time, it accurately monitors the actual working condition of the grid and provides basic data support for subsequent analysis. After obtaining the real-time operating parameters of the power grid, the acquired data is integrated to form an analysis set. To ensure data quality, the acquired data is preprocessed to improve the accuracy and consistency of the data, laying the foundation for subsequent analysis; Extract key features reflecting power grid operation fluctuations from the preprocessed data, and analyze and process the extracted key features under the detection window; The analyzed key features are input into the pre-learned deep learning model, and the deep learning model is used to intelligently evaluate the fluctuation of power grid operation; When the model detects severe fluctuations in the power grid, the electric vehicle's bidirectional charging and discharging inverter will start the feedback control system. According to the deviation between the real-time voltage of the power grid and the reference voltage, a closed-loop control algorithm is used to dynamically adjust the EV output voltage to keep it consistent with the power grid, thereby reducing voltage shocks. At the same time, the phase angle of the EV output power is further adjusted according to the degree of power grid fluctuation, thereby changing the reactive power and compensating for the power factor of the power grid.

2. The control method based on charging pile V2G according to claim 1 is characterized in that: The specific steps for obtaining grid operation status parameters in real time through sensor equipment are as follows: First, smart sensors are deployed at key nodes of the power grid, which collect power grid operation parameters in real time; The sensor then transmits the collected data to a data acquisition server or cloud platform via wireless communication, optical fiber network, or power carrier communication, where the data is stored and processed; At the same time, the data will be time-synchronized to ensure the time consistency of all sampling points, thereby accurately reflecting the transient dynamic characteristics of the power grid.

3. The control method based on charging pile V2G according to claim 1 is characterized in that: The key features reflecting the fluctuation of power grid operation are extracted from the preprocessed data. The extracted features include the mutation degree of power grid line impedance in a short period of time and the variation degree of power grid negative-sequence voltage component. The mutation degree of power grid line impedance in a short period of time and the variation degree of power grid negative-sequence voltage component are analyzed under the detection window, and the line impedance mutation reference value and the negative-sequence voltage fluctuation reference value are generated respectively. The stability and power quality status of the power grid in a short period of time are quantified by the line impedance mutation reference value and the negative-sequence voltage fluctuation reference value.

4. The control method based on charging pile V2G according to claim 3 is characterized in that: The specific steps for analyzing the sudden change degree of power grid line impedance in a short period of time within the detection window to generate a line impedance sudden change reference value are as follows: In the monitoring window, the mutation rate of line impedance in a short period of time is calculated to capture the drastic changes in line impedance. The transient change trend of line impedance is quantified by calculating the impedance change rate factor. The calculation expression is as follows: , where and Respectively represent the maximum and minimum values ​​of the line impedance within the detection window, and Respectively represent the maximum and minimum complex power of the power grid in the detection window, is a very small number, which prevents the denominator from approaching zero and causing unstable calculations. is the impedance change rate factor; Obtaining the line impedance change rate factor After that, the dynamic impact factor of impedance is further introduced, and the line impedance mutation reference value is constructed through the high-order nonlinear relationship between grid voltage and impedance to quantitatively represent the fluctuation intensity of line impedance. The calculation expression is as follows: , where It is the dynamic impact factor, which measures the direct impact of line impedance mutation on grid voltage fluctuation. and They are the maximum voltage and minimum voltage in the detection window, which are used to characterize the voltage fluctuation degree. is the equivalent impedance ratio of the line, is the equivalent reactance, is the equivalent resistance, It is the sensitivity coefficient of reactive power to voltage, indicating the degree of reactive power response caused by voltage changes. is the reactive power, is the voltage, is the regulating factor, It is the reference value of line impedance mutation.

5. The control method based on charging pile V2G according to claim 3 is characterized in that: The specific steps of analyzing the change degree of the negative sequence voltage component of the power grid under the detection window to generate the negative sequence voltage fluctuation reference value are as follows: In the three-phase voltage system of the power grid, the symmetrical component method is used to calculate the negative sequence voltage component. The calculation expression is as follows: , where , , is the three-phase voltage, is the rotation factor of the three-phase symmetric transformation, is the negative sequence voltage component; The rate of change of negative sequence voltage is an important indicator to measure volatility. The negative sequence voltage fluctuation rate is calculated using discrete differential form. The calculation expression is as follows: , where is the negative sequence voltage fluctuation rate, and It is at the moment and time The calculated negative sequence voltage component is is the equivalent impedance, is the moment and time The equivalent impedance of the power grid between is a very small number, which prevents the denominator from approaching zero and causing unstable calculations. is the maximum value operator; In order to fully quantify the dynamic instability of negative sequence voltage, it is necessary to combine its fluctuation rate, phase angle change and system impedance characteristics to calculate the reference value of negative sequence voltage fluctuation. The calculation expression is as follows: , where is the negative sequence voltage fluctuation reference value, The power grid The phase angle change of the negative sequence voltage of each node is: is the total number of nodes, It is the first strip , is the nonlinear adjustment factor, is the total number of lines, is a small amount that prevents the denominator from being zero.

6. The control method based on charging pile V2G according to claim 3 is characterized in that: The analyzed line impedance mutation reference value and negative-sequence voltage fluctuation reference value are input into the pre-learned deep learning model, and the grid fluctuation risk coefficient is generated by the deep learning model. The grid fluctuation state is intelligently evaluated by the grid fluctuation risk coefficient.

7. The control method based on charging pile V2G according to claim 6 is characterized in that: The grid fluctuation risk coefficient generated by the pre-learned deep learning model when the grid fluctuation state is intelligently evaluated is compared with the pre-set grid fluctuation risk coefficient reference threshold to divide the grid fluctuation. The division steps are as follows: If the grid fluctuation risk coefficient is greater than the grid fluctuation risk coefficient reference value threshold, it indicates that severe fluctuations occur during the current grid operation; if the grid fluctuation risk coefficient is less than or equal to the grid fluctuation risk coefficient reference value threshold, it indicates that no fluctuations occur during the current grid operation.

8. The control method based on charging pile V2G according to claim 7 is characterized in that: When the model detects severe fluctuations in the power grid, it dynamically adjusts the EV output voltage and further adjusts the phase angle of the EV output power according to the degree of grid fluctuation. The specific steps are as follows: When the power grid fluctuation risk factor When the set threshold is exceeded, the real-time voltage of the power grid is firstly controlled by closed-loop feedback, and the reference voltage is set to The actual voltage collected is , calculate the voltage error, the calculation expression is as follows: , where is the voltage error; Using a closed-loop control algorithm, the control signal formula is as follows: , where is the EV output voltage compensation value, is the proportional gain coefficient, is the integral gain coefficient, is the error integral term, the voltage error The integral over time, that is, the cumulative sum of historical errors, is the integration variable; On the basis of completing voltage regulation, it is necessary to additionally adjust the phase angle of EV output power to change the reactive power output. First, the phase angle compensation formula is introduced: , where is the reference threshold of the power grid fluctuation risk coefficient, is the phase angle adjustment gain, is the phase angle increment required to adjust the EV inverter; Then, the phase angle is updated, and the updated phase angle calculation formula is as follows: , where is the updated EV inverter output phase angle, is the current phase angle of the EV inverter; By adjusting the phase angle , the EV inverter dynamically changes the reactive power output to compensate for the power factor of the grid, thereby reducing the system risk caused by voltage fluctuations. The calculation expression is as follows: , where is the reactive power provided by the EV inverter to the grid, is the EV inverter output voltage, is the current output by the EV inverter, It is the key factor in determining the reactive power component; Ultimately, the dual regulation of the EV’s output voltage and phase angle will be integrated into the output control signal in a complex form to achieve coordinated compensation of amplitude and phase. The calculation expression of the comprehensive control law is as follows: , where is the EV output control signal, is the phase angle compensation factor adjusted by the EV inverter, is the natural base, Is an imaginary unit.

9. A control system based on a charging pile V2G, used to implement the control method based on a charging pile V2G as described in any one of claims 1 to 8, characterized in that: It includes grid status monitoring module, data processing and preprocessing module, feature extraction and analysis module, deep learning evaluation module and EV feedback control and phase angle optimization module: Grid status monitoring module: First, the sensor equipment obtains the grid operation status parameters in real time. By collecting the operation parameter data in real time, it accurately monitors the actual working condition of the grid and provides basic data support for subsequent analysis. The data processing and preprocessing module integrates the acquired data to form an analysis set after obtaining the real-time operating parameters of the power grid. To ensure data quality, the acquired data is preprocessed to improve the accuracy and consistency of the data, laying the foundation for subsequent analysis. The feature extraction and analysis module extracts key features reflecting the fluctuation of power grid operation from the preprocessed data and analyzes and processes the extracted key features under the detection window; The deep learning evaluation module inputs the analyzed key features into the pre-learned deep learning model, and uses the deep learning model to intelligently evaluate the fluctuations in power grid operation; EV feedback control and phase angle optimization module. When the model detects severe fluctuations in the power grid, the bidirectional charging and discharging inverter of the electric vehicle will start the feedback control system. According to the deviation between the real-time voltage of the power grid and the reference voltage, a closed-loop control algorithm is used to dynamically adjust the EV output voltage to keep it consistent with the power grid, thereby reducing voltage shocks. At the same time, the phase angle of the EV output power is further adjusted according to the degree of power grid fluctuation, thereby changing the reactive power and compensating the power factor of the power grid.

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