Alternating current charging pile ordered charging power regulation and control method

Abstract

The invention discloses an alternating current charging pile ordered charging power regulation and control method, and relates to the technical field of alternating current charging pile charging power regulation and control, and the method specifically comprises the following steps: obtaining the operation state information of each alternating current charging pile in real time, and generating a voltage fluctuation influence index and a load dynamic response index of each alternating current charging pile; constructing a power influence evaluation model for the generated voltage fluctuation influence index and the load dynamic response index of each alternating current charging pile, generating a power influence evaluation coefficient of each alternating current charging pile, performing analysis after generation, and evaluating the influence degree of the three-phase voltage phase difference fluctuation on the charging power of each alternating current charging pile. And the alternating current charging piles are divided into low-influence charging piles, medium-influence charging piles and high-influence charging piles according to the evaluation result. According to the method, the problem that the charging power is influenced by three-phase voltage phase difference fluctuation is solved, and the efficient and stable effects of dynamic evaluation and accurate regulation and control of the power of the charging pile are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of AC charging pile charging power regulation and control, and in particular to an AC charging pile orderly charging power regulation and control method. Background Art

[0002] AC charging pile is a device that provides electric energy for electric vehicles. It is connected to the vehicle through a standardized interface, transmits AC power from the power grid to the vehicle's on-board charger, and converts AC power into DC power and stores it in the battery. With the rapid growth of the number of electric vehicles, the demand for charging has also increased sharply, resulting in a large pressure on the power grid load when multiple charging piles are running at the same time, especially when the power grid is at peak load, which may cause local overload, increased power loss, and reduced power grid operation efficiency. Therefore, it is particularly important to regulate the charging power of AC charging piles to achieve balanced load distribution, reduce the impact on the power grid, and improve charging efficiency. At the same time, orderly charging power regulation of AC charging piles can scientifically allocate charging priorities and power, avoid waste of power grid resources while meeting the diversified charging needs of vehicles, and realize dynamic regulation of peak and valley loads, reduce power grid operation costs and improve new energy consumption capacity, thereby improving user charging experience and overall energy utilization efficiency while ensuring stable operation of the power grid.

[0003] The existing AC charging pile orderly charging power control technology mainly relies on real-time monitoring and analysis of multi-dimensional information such as vehicle charging demand, power grid operation status, and user usage priority, and realizes efficient charging management by optimizing power allocation strategy. Specifically, these technologies first dynamically adjust the charging power allocation order according to the remaining power of the vehicle, the urgency of charging demand, and the user's reservation or usage plan, combined with the load situation of the power grid and the peak and valley electricity price period. During the control process, higher power is given priority to vehicles with urgent charging needs, while for non-emergency vehicles, the power output is appropriately delayed or reduced to avoid overloading the power grid due to excessive concentrated charging. At the same time, through peak and valley load adjustment, the charging of some vehicles is arranged to low-load periods to reduce the peak pressure of the power grid and improve charging efficiency and energy utilization. The entire control process is centered on the real-time feedback mechanism, dynamically monitors the operating status of the power grid and charging piles, and adjusts the charging power allocation in time to ensure the safety of the power grid and the smooth and orderly charging process.

[0004] The prior art has the following deficiencies: In an electric bus parking lot, the AC charging pile is connected to the three-phase power supply line of the public power grid. Due to the long line and dynamic load changes, especially during the morning and evening peak hours, a large number of vehicles are connected to the charging pile at the same time, resulting in the three-phase voltage phase difference of the power supply network often fluctuating slightly. Since the fluctuation of the three-phase voltage phase difference will directly affect the power factor of the charging pile, and the decrease in power factor will cause the actual charging efficiency of some charging piles to be significantly reduced. However, the existing orderly charging power control technology of AC charging piles cannot evaluate the impact of this voltage phase difference on the charging power in real time, and only responds to the load demand through a fixed power allocation method, and fails to dynamically adjust the power strategy according to the fluctuation of the phase difference. This kind of efficiency loss problem caused by ignoring the phase difference fluctuation will cause some charging piles to have insufficient output power or even a significant decrease in efficiency, affecting the normal charging process of the vehicle, which may lead to a series of adverse consequences such as extended charging queues, reduced grid resource utilization efficiency, and reduced user experience.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an AC charging pile orderly charging power control method to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: an AC charging pile orderly charging power control method, specifically comprising the following steps: When multiple vehicles are connected to the AC charging pile connected to the three-phase power supply line of the public power grid at the same time, the fluctuation of the three-phase voltage phase difference of the power supply network is detected in real time, and after the fluctuation of the three-phase voltage phase difference is detected, the orderly charging power regulation process is triggered; Acquire the operating status information of each AC charging pile in real time, analyze it after acquisition, and generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively; A power impact assessment model is constructed for the voltage fluctuation impact index and load dynamic response index of each AC charging pile, and a power impact assessment coefficient of each AC charging pile is generated. After the generation, the power impact assessment coefficient is analyzed to assess the impact of the three-phase voltage phase difference fluctuation on the charging power of each AC charging pile, and each AC charging pile is divided into a low-impact charging pile, a medium-impact charging pile and a high-impact charging pile according to the assessment results; According to the classification results of each AC charging pile, corresponding power control measures are taken for low-impact charging piles, medium-impact charging piles and high-impact charging piles respectively; Continuously monitor the three-phase voltage phase difference fluctuations and the operating status information of each AC charging pile, and dynamically adjust the power distribution evaluation model and control strategy based on the real-time monitoring results. At the same time, store and analyze historical data to optimize model parameters and control strategies.

[0008] Preferably, the operation status information of each AC charging pile is acquired in real time, and analyzed after acquisition to generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively, which specifically includes the following steps: Acquire the operating status information of each AC charging pile in real time and perform preprocessing after acquisition; Extracting voltage fluctuation characteristic information and load response change information from the pre-processed operating status information of each AC charging pile; The extracted voltage fluctuation characteristic information and load response change information are analyzed to generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively.

[0009] Preferably, the logic for obtaining the voltage fluctuation impact index of each AC charging pile is as follows: The voltage fluctuation characteristic information is extracted from the pre-processed operating status information of each AC charging pile, including the three-phase voltage mean value of each AC charging pile access point at different times within a period of time, the difference between the instantaneous maximum and minimum values ​​of the three-phase voltage, and the instantaneous change rate of the phase difference of the three-phase voltage, and calibrated as , and , Indicates that within a period of time Moment The average three-phase voltage of the AC charging pile access point, Indicates that within a period of time Moment The difference between the instantaneous maximum and minimum values ​​of the three-phase voltage at the AC charging pile access point, Indicates that within a period of time Moment The instantaneous change rate of the phase difference of the three-phase voltage at the AC charging pile access point, , , and are all positive integers; Calculate the voltage fluctuation impact index of each AC charging pile. The specific calculation formula is as follows: In the formula, For the The voltage fluctuation impact index of an AC charging pile.

[0010] Preferably, the logic for obtaining the load dynamic response index of each AC charging pile is as follows: The load response change information in the preprocessed operating status information of each AC charging pile is extracted, including the real-time load current, instantaneous load power change rate and historical load current average fluctuation amplitude of each AC charging pile at different times within a period of time, and calibrated as , and , Indicates that within a period of time Moment The real-time load current of each AC charging pile, Indicates that within a period of time Moment The instantaneous load power change rate of an AC charging pile, Indicates The average fluctuation range of the historical load current of each AC charging pile is: , , and are all positive integers; Calculate the load dynamic response index of each AC charging pile. The specific calculation formula is as follows: In the formula, For the Load dynamic response index of an AC charging pile.

[0011] Preferably, a power impact assessment model is constructed for the generated voltage fluctuation impact index and load dynamic response index of each AC charging pile to generate a power impact assessment coefficient for each AC charging pile, specifically including the following steps: The voltage fluctuation impact index, load dynamic response index and corresponding power impact assessment coefficient of several AC charging piles generated in the past period of time are collected and calibrated as , and , Indicates the voltage fluctuation impact index, load dynamic response index and corresponding power impact assessment coefficient number of each AC charging pile generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; Select the multivariate regression model as the power impact assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Using the final regression coefficient, the constructed power impact assessment model is input with the voltage fluctuation impact index of each AC charging pile generated in real time. and load dynamic response index , generate the power impact assessment coefficient of each AC charging pile in real time .

[0012] Preferably, the power impact assessment coefficient of each AC charging pile generated is The power impact assessment coefficient threshold interval is set in advance. A comparison is made, and the impact of the three-phase voltage phase difference fluctuation on the charging power of each AC charging pile is evaluated based on the comparison results. Based on the evaluation results, each AC charging pile is divided into low-impact charging pile, medium-impact charging pile and high-impact charging pile. The specific comparison analysis and division are as follows: like , if the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is low, the AC charging pile is classified as a low-impact charging pile; like , the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is medium, then the AC charging pile is classified as a medium-impact charging pile; like If the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is high, the AC charging pile is classified as a high-impact charging pile.

[0013] Preferably, according to the classification results of each AC charging pile, corresponding power control measures are taken for low-impact charging piles, medium-impact charging piles and high-impact charging piles respectively, specifically: For AC charging piles classified as low-impact charging piles, the specific power control measures are as follows: when the system power resources are sufficient, maintain the existing charging power allocation strategy, do not give priority to adjusting its power allocation ratio, and continuously monitor its operating status and charging demand changes; when the system power resources are insufficient, give priority to reducing the power allocation to low-impact charging piles, and regularly evaluate its charging power stability after adjustment; For AC charging piles classified as medium-impact charging piles, the power control measures taken are as follows: dynamically allocate charging power resources according to real-time three-phase voltage fluctuations and load status; adjust the power allocation strategy in the case of severe power fluctuations to prioritize grid stability, and periodically analyze and evaluate the operating status of medium-impact charging piles to optimize the power control strategy during the charging process; For AC charging piles classified as high-impact charging piles, the power control measures taken are as follows: prioritize reducing their load power fluctuations, alleviate the efficiency decline caused by voltage fluctuations by allocating stable power resources, and monitor their voltage fluctuations and load changes in real time; in addition, regularly conduct in-depth analysis of the operating parameters of high-impact charging piles, dynamically optimize their control models, and gradually reduce their impact on the system.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention can accurately quantify the dynamic impact of three-phase voltage phase difference fluctuations on the charging power of each AC charging pile by real-time monitoring of three-phase voltage phase difference fluctuations and charging pile operating status information, combined with the calculation of voltage fluctuation influence index and load dynamic response index. Compared with the traditional technology of responding to load demand through fixed power distribution, the present invention associates complex fluctuation phenomena with charging pile power characteristics through advanced mathematical models, making the evaluation results more objective, quantitative and real-time. This precise evaluation capability can effectively identify which charging piles in the system have suffered serious damage to charging power due to voltage fluctuations, providing a scientific basis for subsequent classification and regulation, thereby significantly improving the accuracy and real-time performance of the evaluation.

[0015] 2. The present invention classifies charging piles according to the power impact assessment coefficient, and proposes differentiated power control measures for low-impact, medium-impact and high-impact charging piles, which solves the shortcomings of the "one-size-fits-all" power allocation method in the prior art. By dynamically adjusting the power allocation ratio of low-impact charging piles, resource utilization efficiency is optimized; by real-time monitoring and strategy adjustment of medium-impact charging piles, charging efficiency and grid stability are balanced; risk control and deep optimization measures are prioritized for high-impact charging piles to reduce the negative impact of voltage fluctuations on the overall system. This classification control strategy enhances the flexibility and adaptability of the system, which not only ensures the charging efficiency of each charging pile, but also reduces the system instability caused by fluctuations, making the operation of the power grid and charging system more efficient and reliable.

[0016] 3. The present invention has established a set of sustainable optimization intelligent systems through the storage and analysis of historical data and the dynamic adjustment of power distribution evaluation models and control strategies. By optimizing model parameters using historical data, the system can gradually adapt to the complex and changeable power grid environment in long-term operation, and improve the model's ability to capture fluctuation characteristics and the intelligence level of the control strategy. In addition, through the combination of real-time monitoring and data analysis, the present invention has the ability to efficiently discover and respond to system anomalies, ensuring that the system can maintain stable operation under high load or severe voltage fluctuations. This technical effect of combining real-time and long-term optimization not only improves the reliability and robustness of the system, but also lays the foundation for further optimization of power grid resource utilization efficiency and user charging experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic diagram of the flow of the orderly charging power control method of the AC charging pile of the present invention. DETAILED DESCRIPTION

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

[0020] The present invention provides Figure 1 The AC charging pile orderly charging power control method shown specifically includes the following steps: When multiple vehicles are connected to the AC charging pile connected to the three-phase power supply line of the public power grid at the same time, the fluctuation of the three-phase voltage phase difference of the power supply network is detected in real time, and after the fluctuation of the three-phase voltage phase difference is detected, the orderly charging power regulation process is triggered; Real-time detection of the three-phase voltage phase difference fluctuation of the power supply network can be achieved by deploying phase measurement units (PMUs) and voltage monitoring modules at the access points of the AC charging piles. These devices can collect electrical signals of three-phase voltage in real time, including voltage amplitude and phase information, and calculate the instantaneous phase value of each phase voltage through high-precision synchronous sampling technology. The collected phase data is transmitted to the central control system through the communication module of the charging pile, and the real-time phase difference fluctuation is calculated using advanced mathematical algorithms (such as Fourier transform or phase difference calculation formula). To ensure the accuracy of the data, the system calibrates the collection time through a time synchronization protocol (such as GPS signals) to ensure that the phase data of each charging pile access point is analyzed under the same time reference. At the same time, the central control system presets the threshold of phase difference fluctuation and monitors the amplitude of the change of the three-phase voltage phase difference. If the detected fluctuation value exceeds the set threshold, the system determines that the three-phase voltage phase difference has fluctuated.

[0021] After detecting the fluctuation of the three-phase voltage phase difference, the orderly charging power control process is triggered by the central control system. The specific implementation method is to adopt an event-driven mechanism. When the output signal of the detection module exceeds the preset threshold, the event processing module in the system is triggered to enter the control mode. First, the system sends a real-time status acquisition instruction to each connected AC charging pile to obtain the current operating status information of all charging piles (including real-time power factor, load current, vehicle charging demand, etc.). Subsequently, the preset power allocation control algorithm is called to recalculate the power allocation strategy of each charging pile based on the current operating status. For example, by constructing an evaluation model, the operating efficiency and load demand of each charging pile are evaluated and divided into different categories. Finally, the power distribution of each charging pile is automatically adjusted according to the classification results to ensure the load balance of the power grid and optimize the overall charging efficiency.

[0022] The reason for implementing this process is that the fluctuation of the three-phase voltage phase difference directly affects the power factor of the AC charging pile, resulting in a decrease in charging efficiency. If the detection and control are not triggered in time, some charging piles may be in an inefficient operating state for a long time, and even have a negative impact on other equipment in the power grid. By detecting phase difference fluctuations in real time, the dynamic changes of the power supply network can be quickly identified to provide data support for the control system. By triggering the orderly charging power control process, the power distribution of each charging pile can be dynamically adjusted to minimize the impact of fluctuations on charging efficiency and system stability, thereby improving the utilization rate of power grid resources and user charging experience, and solving the problem that existing technologies cannot cope with fluctuations and cause efficiency losses.

[0023] Acquire the operating status information of each AC charging pile in real time, analyze it after acquisition, and generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively; In this embodiment, the operating status information of each AC charging pile is acquired in real time, and analyzed after acquisition to generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively, which specifically includes the following steps: Acquire the operating status information of each AC charging pile in real time and perform preprocessing after acquisition; Real-time acquisition of the operating status information of each AC charging pile can be achieved in a variety of ways. First, each charging pile can be equipped with an embedded device with communication function (such as a data acquisition module), through which the various operating parameters of the charging pile, such as voltage, current, power factor and other information, can be monitored in real time. These data can be transmitted to the central monitoring system or cloud platform through wired or wireless communication protocols (such as Modbus, MQTT, TCP / IP, etc.). In addition, in order to ensure real-time performance, data can be obtained through periodic polling or event-driven methods (such as triggering data transmission when abnormal fluctuations are detected). The data acquisition module will regularly read and upload relevant operating status information, such as voltage fluctuations, load conditions, charging pile working status, etc. This information is recorded in real time and transmitted to the system for subsequent analysis.

[0024] The purpose of preprocessing is to ensure the accuracy and consistency of the acquired raw data. Since sensors and data acquisition processes may introduce noise or abnormal data, the preprocessing step is crucial. Common preprocessing includes data denoising, outlier detection and correction, missing data filling, etc. For example, for voltage fluctuation data, high-frequency noise can be removed by filters (such as low-pass filtering); for abnormal fluctuations in current or power data, extreme values ​​can be found and corrected by statistical analysis (such as standard deviation detection). In addition, missing data can be filled by interpolation algorithms (such as linear interpolation or spline interpolation) to ensure data continuity. Preprocessing can also include data standardization or normalization, which converts data from different sources into a unified scale, which is crucial for subsequent analysis and model generation. The purpose of this is to remove interference factors and ensure the accuracy and stability of subsequent data analysis, index generation and power regulation.

[0025] Extracting voltage fluctuation characteristic information and load response change information from the pre-processed operating status information of each AC charging pile; The extracted voltage fluctuation characteristic information and load response change information are analyzed to generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively.

[0026] In this embodiment, the logic for obtaining the voltage fluctuation impact index of each AC charging pile is as follows: The voltage fluctuation characteristic information is extracted from the pre-processed operating status information of each AC charging pile, including the three-phase voltage mean value of each AC charging pile access point at different times within a period of time, the difference between the instantaneous maximum and minimum values ​​of the three-phase voltage, and the instantaneous change rate of the phase difference of the three-phase voltage, and calibrated as , and , Indicates that within a period of time Moment The average three-phase voltage of the AC charging pile access point, Indicates that within a period of time Moment The difference between the instantaneous maximum and minimum values ​​of the three-phase voltage at the AC charging pile access point, Indicates that within a period of time Moment The instantaneous change rate of the phase difference of the three-phase voltage at the AC charging pile access point, , , and are all positive integers; Extracting the voltage fluctuation characteristic information from the pre-processed operating status information of each AC charging pile can be achieved by combining real-time data acquisition equipment and a central processing system. A voltage sensor and a phase monitoring module are installed at each AC charging pile access point to continuously collect the amplitude and phase data of the three-phase voltage. These raw data are transmitted to the central control system through the communication module. Within the set time window, the system will sample the voltage and phase information of each charging pile multiple times and record the data at each moment. Subsequently, the data processing module in the central system will analyze these data and extract the key characteristics of the three-phase voltage, including the mean value of the three-phase voltage, the difference between the maximum and minimum values, and the instantaneous rate of change of the three-phase voltage phase difference, to provide the required data support for subsequent calculations and analysis.

[0027] These three types of quantitative data are obtained in different ways. The three-phase voltage mean is calculated by the three-phase voltage amplitude at each sampling moment to reflect the overall voltage level of the AC charging pile. The difference between the instantaneous maximum and minimum values ​​of the three-phase voltage is determined by comparing the maximum and minimum values ​​of the three-phase voltage amplitude at the same moment. This value directly reflects the degree of imbalance between the three-phase voltage amplitudes. The instantaneous rate of change of the phase difference of the three-phase voltage is calculated by analyzing the phase difference data at two consecutive sampling moments and calculating the rate of phase change. This value reflects the dynamic fluctuation characteristics of the three-phase voltage phase. The collection and processing of these data are automatically completed by the software system to ensure the real-time and accuracy of the data, laying the foundation for subsequent index generation.

[0028] Calculate the voltage fluctuation impact index of each AC charging pile. The specific calculation formula is as follows: In the formula, For the The voltage fluctuation impact index of an AC charging pile.

[0029] This formula comprehensively considers multiple key variables to accurately evaluate the impact of three-phase voltage fluctuations on the operating status of AC charging piles, which is scientific and reasonable. It represents the square of the difference between the instantaneous maximum and minimum values ​​of the three-phase voltage. This operation enhances the sensitivity to large voltage fluctuations, because the larger the fluctuation, the more significant its impact on the operation of the charging pile. is the instantaneous rate of change of the phase difference of the three-phase voltage, which is used to describe the dynamic characteristics of the voltage phase fluctuation. Combined with the voltage amplitude change, it reflects the overall complexity of the fluctuation. Subsequently, the product of these two variables and the voltage mean Normalization is performed to reduce the interference of the overall voltage level on the fluctuation impact assessment through the denominator. The logarithmic term further corrects the effect of the voltage mean, making the fluctuation effect smoother when the voltage is high. Finally, by The accumulation and averaging of the voltage fluctuations at each moment comprehensively considers the time characteristics of voltage fluctuations, ensuring that the results can represent the overall fluctuation impact of the charging pile access point over the entire time period. Each calculation step of the formula is designed to accurately quantify the nonlinear impact of voltage fluctuations on the operation of the charging pile, providing a reliable basis for subsequent evaluation and optimization.

[0030] No. Voltage fluctuation impact index of AC charging piles The size of directly reflects the degree of influence of the three-phase voltage phase difference fluctuation on the charging power of the charging pile, and the two are positively correlated. Specifically, the larger the index, the greater the fluctuation amplitude of the three-phase voltage of the charging pile, and the more drastic the instantaneous change rate of the voltage phase difference. These fluctuations will lead to increased voltage imbalance and dynamic instability, thereby reducing the charging efficiency of the charging pile and increasing the unreliability of operation. At the same time, the normalization of the voltage mean ensures that this index can reflect the relative impact of fluctuations on the charging power, rather than being determined solely by the absolute size of the voltage level. Finally, the level of the voltage fluctuation impact index is calculated by combining the fluctuation characteristics of multiple moments in the time dimension. The higher the index of the charging pile, the more susceptible it is to voltage fluctuations, the worse the stability of the charging power, and the priority attention and regulation are required. Therefore, this index is an important quantitative indicator for accurately evaluating the degree of influence of the three-phase voltage phase difference fluctuation on the charging power of the charging pile.

[0031] In this embodiment, the logic for obtaining the load dynamic response index of each AC charging pile is as follows: The load response change information in the preprocessed operating status information of each AC charging pile is extracted, including the real-time load current, instantaneous load power change rate and historical load current average fluctuation amplitude of each AC charging pile at different times within a period of time, and calibrated as , and , Indicates that within a period of time Moment The real-time load current of each AC charging pile, Indicates that within a period of time Moment The instantaneous load power change rate of an AC charging pile, Indicates The average fluctuation range of the historical load current of each AC charging pile is: , , and are all positive integers; Extracting the load response change information from the pre-processed operating status information of each AC charging pile can be achieved through the collaboration of real-time data acquisition equipment and the central processing system. Install high-precision current sensors and power monitoring modules at each AC charging pile access point. These devices can record the instantaneous load current and power data of the charging pile in real time. By setting a fixed time window (such as 1 second or several seconds), the system will sample these data regularly and record the load current and power changes at each moment. The raw data collected by the sensor is uploaded to the central processing system through a communication module (such as a wireless communication protocol or a wired bus protocol). Based on the collected time series data, the system extracts the real-time load current and load power change rate at each moment through software preprocessing steps (such as denoising, outlier removal and time alignment), and calculates the average fluctuation amplitude of the load current in combination with historical data. These processes are completed through an automated software analysis process to ensure the integrity and accuracy of the data.

[0032] These three types of quantitative data are obtained in the following ways: the real-time load current is directly collected by the current sensor installed at the charging pile access point, recording the instantaneous load current value of the charging pile at each moment to reflect the current load status of the charging pile; the instantaneous load power change rate is calculated by the power data at two consecutive sampling moments to calculate the rate of power change. The system performs time difference processing based on the load power data recorded by the power monitoring module to quantify the load power fluctuation intensity of the charging pile; the historical load current average fluctuation range is obtained by statistical analysis of the load current over a period of time. The system calculates the fluctuation range of the load current within the set time window and takes the average value to measure the long-term stability of the load current. These three types of data are pre-processed and saved in the database for subsequent calculation of the load dynamic response index to accurately describe the load dynamic characteristics of each charging pile.

[0033] Calculate the load dynamic response index of each AC charging pile. The specific calculation formula is as follows: In the formula, For the Load dynamic response index of an AC charging pile.

[0034] The purpose of this formula is to fully quantify the response of each AC charging station to grid fluctuations under dynamic load conditions, reflecting the combined impact of load current, power fluctuations and historical characteristics. The sensitivity to drastic power changes is enhanced through the square operation, reflecting the impact of dynamic fluctuations in load power on the power grid; The historical load current fluctuation amplitude is increased to the cube, which amplifies the impact weight when the load fluctuates violently, reflecting the long-term impact of historical data on the dynamic characteristics of the current load. It is used for normalization processing to ensure that the result can truly reflect the relative characteristics of the load dynamic response and avoid calculation anomalies caused by too small load current. Finally, the exponential term in the formula The sensitivity to load fluctuations is improved, further highlighting the impact of extreme fluctuations on load response. The formula effectively integrates the dynamic change characteristics of the time dimension, ensuring that the results have statistical stability and dynamic adaptability. Overall, the calculation steps of the formula are reasonably designed and of moderate complexity, which can fully reflect the dynamic response characteristics of the charging pile load and is an important basis for evaluating load stability and grid adaptability.

[0035] No. Load dynamic response index of AC charging piles The size of directly reflects the sensitivity of the charging pile to the stability of charging power under the condition of three-phase voltage phase difference fluctuation, and the two are positively correlated. Specifically, the larger the index, the more unstable the load characteristics of the charging pile under dynamic fluctuation conditions, which is manifested as a higher instantaneous load power change rate or a larger historical load current fluctuation amplitude. These characteristics will increase the sensitivity of the load to voltage fluctuation response, making the charging power more susceptible to impact. At the same time, the index term further highlights the instability when the historical fluctuation is drastic and the current load is low, allowing the index to comprehensively evaluate the adaptability of the charging pile in a dynamic power grid environment. In summary, the larger the load dynamic response index, the worse the adaptability of the charging pile to the fluctuation of the three-phase voltage phase difference, the larger the fluctuation amplitude of the charging power, and the higher priority attention should be given in power regulation.

[0036] A power impact assessment model is constructed for the voltage fluctuation impact index and load dynamic response index of each AC charging pile, and a power impact assessment coefficient of each AC charging pile is generated. After the generation, the power impact assessment coefficient is analyzed to assess the impact of the three-phase voltage phase difference fluctuation on the charging power of each AC charging pile, and each AC charging pile is divided into a low-impact charging pile, a medium-impact charging pile and a high-impact charging pile according to the assessment results; In this embodiment, a power impact assessment model is constructed based on the voltage fluctuation impact index and load dynamic response index of each AC charging pile, and a power impact assessment coefficient of each AC charging pile is generated, which specifically includes the following steps: The voltage fluctuation impact index, load dynamic response index and corresponding power impact assessment coefficient of several AC charging piles generated in the past period of time are collected and calibrated as , and , Indicates the voltage fluctuation impact index, load dynamic response index and corresponding power impact assessment coefficient number of each AC charging pile generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; The collection of voltage fluctuation impact index, load dynamic response index and corresponding power impact assessment coefficient of each AC charging pile generated in the past period of time can be achieved by building a data acquisition and storage system. First, the data recording function is embedded in the operation monitoring module of each AC charging pile, and the real-time generated voltage fluctuation impact index and load dynamic response index are uploaded to the central data storage system through the communication network (such as Modbus protocol, MQTT or other cloud platform transmission protocols) at regular intervals. The data storage module in the central system will store the received index value in a relational database or a time series database according to the preset time window (such as every hour, every day), and associate the generated power impact assessment coefficient and mark it. Through the historical data management module, the system can automatically classify and archive all index data within a specific time period to ensure the continuity and integrity of the data. In addition, the software can also extract the corresponding historical index records for the unique identifier (such as device ID) of each AC charging pile through the query interface, sort and format them in time series, and provide a complete data basis for subsequent model training and evaluation. The whole process runs automatically, and continuous historical data collection and storage are achieved through the scheduler and database management system.

[0037] Will The value range of is limited to positive integers greater than or equal to 3, which is a reasonable choice based on the basic principles of the multiple regression model and data requirements. The goal of the multiple regression model is to calculate the regression coefficient of the model by minimizing the error between the predicted value and the actual value. , and . Since there is only one target equation and three regression coefficients, in order to accurately solve the values ​​of these coefficients, at least three independent sample data are required to generate three independent equations, thus forming an equation system and ensuring that it has a unique solution. In addition, in practical applications, the more data there is, the more robust the model is to noise and the more accurate the fitting is. Therefore, The value of not only meets the minimum sample requirement for solving the equations (at least 3 data points), but also provides more samples for the model to optimize the calculation accuracy and generalization ability of the regression coefficient. This design not only ensures the mathematical solvability, but also ensures the stability and accuracy of the statistical model, thus laying a solid foundation for the reliability of the evaluation results.

[0038] Select the multivariate regression model as the power impact assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; The multivariate regression model is a statistical analysis method used to explore the linear relationship between multiple independent variables (input variables) and dependent variables (output variables) and to predict the value of the dependent variable by fitting a mathematical equation. In the power impact assessment model, the multivariate regression model is selected because it can effectively integrate two key variables - voltage fluctuation impact index and load dynamic response index, and the regression coefficient ( , and ), optimize the prediction of the power impact assessment coefficient. By training on historical data, the multivariate regression model can minimize the error between the predicted value and the true value, ensuring that the model has a high degree of fit and prediction accuracy. The meanings of the three regression coefficients are as follows: is a constant term, which represents the base value of the power impact assessment coefficient when both exponents are zero; is the regression coefficient of the voltage fluctuation influence index, indicating Linear impact strength on power impact assessment coefficient; is the regression coefficient of the load dynamic response index, reflecting The contribution of the power impact assessment coefficients. Through historical data training, these three coefficients are optimized to best reflect the actual impact of the two types of indexes on power assessment, thereby improving the accuracy and reliability of the assessment model.

[0039] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Optimizing the regression coefficient by minimizing the error between the predicted value and the actual value is the core goal of the multivariate regression model and a key step to improve the accuracy and reliability of the model. The purpose of this is to enable the model to fit historical data more accurately and find the best linear relationship between the dependent variable (power impact assessment coefficient) and the independent variable (voltage fluctuation impact index and load dynamic response index), so as to have higher credibility and adaptability in future data forecasts. The specific implementation method is to establish an error function (such as the mean square error function), square and average the deviation between the model predicted value and the actual value, and use it as the goal of model optimization. Through optimization methods such as gradient descent algorithm or least squares method, the regression coefficient is gradually adjusted. , and The value of is taken until the error function reaches the minimum value, thereby obtaining the best regression coefficient. This method can ensure that the model fits the historical data to the best state, and at the same time has good generalization ability, providing a scientific basis for subsequent power impact assessment.

[0040] Using the final regression coefficient, the constructed power impact assessment model is input with the voltage fluctuation impact index of each AC charging pile generated in real time. and load dynamic response index , generate the power impact assessment coefficient of each AC charging pile in real time .

[0041] In this embodiment, the power impact assessment coefficient of each AC charging pile generated is The power impact assessment coefficient threshold interval is set in advance. A comparison is made, and the impact of the three-phase voltage phase difference fluctuation on the charging power of each AC charging pile is evaluated based on the comparison results. Based on the evaluation results, each AC charging pile is divided into low-impact charging pile, medium-impact charging pile and high-impact charging pile. The specific comparison analysis and division are as follows: like , if the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is low, the AC charging pile is classified as a low-impact charging pile; This situation means that the charging power of the charging pile is less affected by the fluctuation of the three-phase voltage phase difference. Specifically, the charging pile can maintain a high charging efficiency and operating stability under voltage fluctuation conditions, and its voltage fluctuation impact index and load dynamic response index are both at a low level, indicating that the voltage and load states of the charging pile are relatively balanced. This low-impact charging pile has a strong adaptability to grid fluctuations, and the charging process is hardly significantly disturbed. It is suitable for normal operation and no additional regulatory measures are required. Its existence can effectively stabilize the operation of the overall power grid, but if the number accounts for too large a proportion, it may mask other charging piles with obvious problems.

[0042] like , the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is medium, then the AC charging pile is classified as a medium-impact charging pile; This situation means that the charging power of the charging pile is affected by the fluctuation of the three-phase voltage phase difference to a medium level. Specifically, the voltage fluctuation impact index and load dynamic response index of the charging pile show certain fluctuations, and the charging efficiency and operating status may be slightly unstable. Although this type of charging pile can still basically meet normal operation, it may cause certain power losses when the power grid fluctuates violently or load conditions change. For medium-impact charging piles, it may be necessary to moderately adjust the charging power allocation strategy to optimize its operating efficiency, avoid further deterioration to a high-impact charging pile, and reduce the potential impact on the power grid.

[0043] like If the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is high, the AC charging pile is classified as a high-impact charging pile.

[0044] This situation means that the charging power of the charging pile is highly affected by the fluctuation of the three-phase voltage phase difference. Specifically, the voltage fluctuation impact index and load dynamic response index of the charging pile are both at a high level, the voltage fluctuates violently, and the load changes frequently, resulting in a significant decrease in its charging efficiency and may cause instability in the operating state. This type of high-impact charging pile is likely to bring significant pressure to the overall power grid, and even cause imbalance in the local power grid, affecting the operation of other equipment. Therefore, it is necessary to take key control measures for this type of charging pile, such as reducing its charging power, adjusting load distribution, or reducing its dependence on power grid fluctuations by optimizing the control model to ensure the overall stability and efficiency of the system.

[0045] Pre-set power impact assessment coefficient threshold range It can be determined by analyzing historical data and combining statistical methods and optimization algorithms. First, collect the power impact assessment coefficient values ​​of each AC charging pile in a large amount of historical data, and use software to clean and preprocess the data (such as removing outliers, filling missing values, etc.) to ensure the integrity and accuracy of the data. Then, based on the preprocessed data, use statistical analysis methods (such as frequency distribution analysis or cluster analysis) to find the main distribution range and central trend of the data. For example, the center position and dispersion degree of the data distribution can be determined by calculating the mean and standard deviation of the data. Specifically, Set to the first quantile of the data distribution, indicating the boundary of the lower impact level; Set to the third quantile, indicating the boundary of a higher degree of impact. To further optimize the threshold, you can also use machine learning algorithms in the software (such as decision trees or support vector machines) to classify the data and dynamically adjust the threshold by minimizing the classification error rate. and , making it more in line with the actual operation needs. This method not only ensures the scientificity and rationality of the threshold, but also can flexibly adjust the threshold range according to different scenarios to meet diverse application needs.

[0046] According to the classification results of each AC charging pile, corresponding power control measures are taken for low-impact charging piles, medium-impact charging piles and high-impact charging piles respectively; In this embodiment, according to the classification results of each AC charging pile, corresponding power control measures are taken for low-impact charging piles, medium-impact charging piles and high-impact charging piles, respectively, specifically: For AC charging piles classified as low-impact charging piles, the specific power control measures are as follows: when the system power resources are sufficient, maintain the existing charging power allocation strategy, do not give priority to adjusting its power allocation ratio, and continuously monitor its operating status and charging demand changes; when the system power resources are insufficient, give priority to reducing the power allocation to low-impact charging piles to ensure the smooth operation of the overall system, and regularly evaluate its charging power stability after adjustment to ensure that it remains in a low-impact state; The power regulation of low-impact charging piles can be achieved by building a real-time resource monitoring and power allocation strategy module. First, the system determines whether its impact on grid resources is at a low level by monitoring the operating status information of the charging pile (such as real-time load current, charging demand, voltage fluctuation, etc.). For low-impact charging piles, the software will give priority to maintaining the existing power allocation strategy. When the system power resources are sufficient, it will not actively adjust its power allocation ratio to avoid interference with the operating status of the charging pile. At the same time, when the power resources are insufficient, the software dynamically analyzes the overall power demand of the system, reduces the power allocation of low-impact charging piles, and allocates more resources to medium and high-impact charging piles. The real-time monitoring module continuously collects the operating data of such charging piles and regularly evaluates their power stability to ensure that the low-impact charging piles can still meet their basic charging needs after adjustment. The purpose of this is to ensure the optimal configuration of grid resources by reducing the resource allocation to low-impact charging piles, while maintaining their low-impact characteristics and reducing the pressure on the overall operation of the system.

[0047] For AC charging piles classified as medium-impact charging piles, the power control measures taken are as follows: dynamically allocate charging power resources according to real-time three-phase voltage fluctuations and load status to ensure that the charging piles do not consume excessive power resources while maintaining charging efficiency; in the case of severe power fluctuations, adjust their power allocation strategy to prioritize grid stability, and periodically analyze and evaluate the operating status of medium-impact charging piles to optimize their power control strategy during the charging process; The power regulation of medium-impact charging piles can be achieved by combining a dynamic power allocation algorithm with a real-time control module. The software first analyzes the current power demand and operating status of the medium-impact charging pile by monitoring the three-phase voltage fluctuations, load changes and other operating data in real time. During the power allocation process, the system dynamically adjusts the charging power based on the preset power allocation rules and real-time data. For example, when the three-phase voltage fluctuates violently, the system can reduce the power fluctuation amplitude of the charging pile by smoothly allocating power resources; and when the voltage fluctuation weakens, the power allocation is appropriately increased to improve the charging efficiency. At the same time, the system will periodically analyze the medium-impact charging piles, such as evaluating their load change trends and fluctuation frequencies by aggregating historical operating data, so as to dynamically optimize the power allocation strategy. The purpose of this is to maximize the balance between the charging efficiency and the impact on the power grid while ensuring the stable operation of the medium-impact charging piles, thereby optimizing the overall performance of the system.

[0048] For AC charging piles that are classified as high-impact charging piles, the power control measures taken are as follows: give priority to reducing their load power fluctuations, alleviate the efficiency decline caused by voltage fluctuations by allocating stable power resources, and monitor their voltage fluctuations and load changes in real time. When necessary, limit their charging power to reduce their impact on the stability of the power grid; in addition, conduct in-depth analysis of the operating parameters of high-impact charging piles on a regular basis, dynamically optimize their control models, and gradually reduce their impact on the system.

[0049] The power regulation of high-impact charging piles needs to be achieved through real-time limitation of power fluctuations and long-term optimization of the regulation model. First, the software accurately captures the operating status data of high-impact charging piles through the real-time monitoring module, including indicators such as voltage fluctuation impact index, current fluctuation amplitude and power dynamic change rate. When excessive fluctuations are detected, the system will actively limit the upper limit of its charging power to avoid triggering the protection mechanism due to voltage fluctuations or further aggravating system instability. At the same time, through the dynamic power allocation algorithm, stable and low power resources are allocated to high-impact charging piles to reduce their interference with the power grid. In addition, the system will regularly analyze the historical operation data of high-impact charging piles and continuously optimize the regulation model using machine learning algorithms. For example, by analyzing the voltage fluctuation characteristics and load change trends, the regulation parameters are adjusted to gradually reduce their adverse effects on the power grid. The purpose of this is to prioritize the control of the threat of high-impact charging piles to system stability, and at the same time, by optimizing their operating status, create a more balanced operating environment for the entire system, and ensure the overall efficiency and safety of charging piles and power grids.

[0050] Continuously monitor the three-phase voltage phase difference fluctuations and the operating status information of each AC charging pile, and dynamically adjust the power distribution evaluation model and control strategy according to the real-time monitoring results. At the same time, store and analyze historical data to optimize model parameters and control strategies, and improve the long-term adaptability and stability of the system.

[0051] Continuous monitoring of the three-phase voltage phase difference fluctuations and the operating status information of each AC charging pile can be achieved by establishing a real-time data acquisition and monitoring system. Voltage sensors, phase monitoring modules and load current acquisition devices are installed at each AC charging pile access point. These devices can collect the amplitude of the three-phase voltage, the phase difference change rate, the load current and other key operating data in real time. The collected data is transmitted to the central control system through a communication module (such as wired TCP / IP, wireless MQTT protocol, etc.). In the central system, the software uses a time series data analysis method to analyze the collected data in real time, calculate the dynamic changes of the three-phase voltage phase difference and the operating status indicators of the charging pile. In order to ensure the timeliness and integrity of the data, the system can set a fixed sampling frequency (such as sampling several times per second) and pre-process the collected data (denoising, outlier removal, etc.). The purpose of this is to obtain accurate voltage fluctuations and charging pile operation information, provide real-time support for dynamic power regulation and subsequent optimization, and ensure that the system can quickly respond to any abnormal fluctuations or state changes.

[0052] Dynamic adjustment of the power distribution evaluation model and control strategy can be achieved by building a control optimization module based on real-time feedback. Based on real-time monitoring data, the software analyzes the current three-phase voltage phase difference fluctuation and the operating status of the charging pile through data analysis and modeling algorithms, and evaluates whether the existing power distribution evaluation model is in line with the current operating conditions of the power grid and charging piles. For example, by calculating the power impact evaluation coefficient and classification results in real time, the system determines the rationality of the power distribution of each charging pile. If it is detected that the output of the power distribution model cannot adapt to the current load fluctuation or power grid status, the software will dynamically adjust the power distribution strategy based on the latest monitoring data, such as recalculating the power distribution ratio, optimizing the power upper limit or priority resource allocation allocated to high-impact charging piles. The purpose of this is to use real-time data to optimize the model adaptability, ensure that the power distribution strategy can continue to operate effectively, and enhance the system's dynamic response capabilities to changes in the operating status of the power grid and charging piles.

[0053] The storage and analysis of historical data and the optimization of model parameters can be achieved through the combination of data storage modules and machine learning algorithms. While collecting and analyzing data in real time, the system stores the operating status information of each AC charging pile (such as three-phase voltage fluctuations, load dynamic response, and power impact assessment coefficient) in a central database or a time series database to form a long-term historical data set. During the storage process, the data is labeled, such as the classification level of the charging pile to which the data belongs or the characteristics of the power grid fluctuation scene, for subsequent analysis. Based on the stored historical data, the software analyzes historical trends through regression analysis, clustering algorithms or deep learning models, finds out the key factors affecting the parameters of the power distribution evaluation model, and dynamically optimizes the model structure. By periodically updating the model parameters (such as retraining the model based on the latest historical data every week or month), the long-term adaptability and stability of the power distribution model are further improved. The purpose of this is to use historical data to mine long-term laws, optimize the control strategy, and make the system more robust and forward-looking when facing complex scenarios.

[0054] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0055] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). 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 data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0056] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0057] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0058] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0059] 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 on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0061] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for regulating and controlling the orderly charging power of an AC charging pile, characterized in that: The specific steps include: When multiple vehicles are connected to the AC charging pile connected to the three-phase power supply line of the public power grid at the same time, the fluctuation of the three-phase voltage phase difference of the power supply network is detected in real time, and after the fluctuation of the three-phase voltage phase difference is detected, the orderly charging power regulation process is triggered; Acquire the operating status information of each AC charging pile in real time, analyze it after acquisition, and generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively; A power impact assessment model is constructed for the voltage fluctuation impact index and load dynamic response index of each AC charging pile, and a power impact assessment coefficient of each AC charging pile is generated. After the generation, the power impact assessment coefficient is analyzed to assess the impact of the three-phase voltage phase difference fluctuation on the charging power of each AC charging pile, and each AC charging pile is divided into a low-impact charging pile, a medium-impact charging pile and a high-impact charging pile according to the assessment results; According to the classification results of each AC charging pile, corresponding power control measures are taken for low-impact charging piles, medium-impact charging piles and high-impact charging piles respectively; Continuously monitor the three-phase voltage phase difference fluctuations and the operating status information of each AC charging pile, and dynamically adjust the power distribution evaluation model and control strategy based on the real-time monitoring results. At the same time, store and analyze historical data to optimize model parameters and control strategies.

2. The method for controlling the orderly charging power of an AC charging pile according to claim 1, characterized in that: The operation status information of each AC charging pile is obtained in real time, and analyzed after acquisition to generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively, which specifically includes the following steps: Acquire the operating status information of each AC charging pile in real time and perform preprocessing after acquisition; Extracting voltage fluctuation characteristic information and load response change information from the pre-processed operating status information of each AC charging pile; The extracted voltage fluctuation characteristic information and load response change information are analyzed to generate the voltage fluctuation impact index and load dynamic response index of each AC charging pile respectively.

3. The method for controlling the orderly charging power of an AC charging pile according to claim 2, characterized in that: The logic for obtaining the voltage fluctuation impact index of each AC charging pile is as follows: The voltage fluctuation characteristic information is extracted from the pre-processed operating status information of each AC charging pile, including the three-phase voltage mean value of each AC charging pile access point at different times within a period of time, the difference between the instantaneous maximum and minimum values ​​of the three-phase voltage, and the instantaneous change rate of the phase difference of the three-phase voltage, and calibrated as , and , Indicates that within a period of time Moment The average three-phase voltage of the AC charging pile access point, Indicates that within a period of time Moment The difference between the instantaneous maximum and minimum values ​​of the three-phase voltage at the AC charging pile access point, Indicates that within a period of time Moment The instantaneous change rate of the phase difference of the three-phase voltage at the AC charging pile access point, , , and All are positive integers; Calculate the voltage fluctuation impact index of each AC charging pile. The specific calculation formula is as follows: In the formula, For the The voltage fluctuation impact index of an AC charging pile.

4. The method for controlling the orderly charging power of an AC charging pile according to claim 3, characterized in that: The logic for obtaining the load dynamic response index of each AC charging pile is as follows: The load response change information in the preprocessed operating status information of each AC charging pile is extracted, including the real-time load current, instantaneous load power change rate and historical load current average fluctuation amplitude of each AC charging pile at different times within a period of time, and calibrated as , and , Indicates that within a period of time Moment The real-time load current of each AC charging pile, Indicates that within a period of time Moment The instantaneous load power change rate of an AC charging pile, Indicates The average fluctuation range of the historical load current of each AC charging pile is: , , and All are positive integers; Calculate the load dynamic response index of each AC charging pile. The specific calculation formula is as follows: In the formula, For the Load dynamic response index of an AC charging pile.

5. The method for controlling the orderly charging power of an AC charging pile according to claim 4, characterized in that: A power impact assessment model is constructed based on the voltage fluctuation impact index and load dynamic response index of each AC charging pile, and a power impact assessment coefficient of each AC charging pile is generated, which specifically includes the following steps: The voltage fluctuation impact index, load dynamic response index and corresponding power impact assessment coefficient of several AC charging piles generated in the past period of time are collected and calibrated as , and , Indicates the voltage fluctuation impact index, load dynamic response index and corresponding power impact assessment coefficient number of each AC charging pile generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; Select the multivariate regression model as the power impact assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Using the final regression coefficient, the constructed power impact assessment model is input with the voltage fluctuation impact index of each AC charging pile generated in real time. and load dynamic response index , generate the power impact assessment coefficient of each AC charging pile in real time .

6. The method for controlling the orderly charging power of an AC charging pile according to claim 5, characterized in that: The power impact assessment coefficient of each AC charging pile generated The power impact assessment coefficient threshold interval is set in advance. A comparison is made, and the impact of the three-phase voltage phase difference fluctuation on the charging power of each AC charging pile is evaluated based on the comparison results. Based on the evaluation results, each AC charging pile is divided into low-impact charging pile, medium-impact charging pile and high-impact charging pile. The specific comparison analysis and division are as follows: like , if the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is low, the AC charging pile is classified as a low-impact charging pile; like , the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is medium, then the AC charging pile is classified as a medium-impact charging pile; like If the influence of the three-phase voltage phase difference fluctuation on the charging power of the AC charging pile is high, the AC charging pile is classified as a high-impact charging pile.

7. The method for controlling the orderly charging power of an AC charging pile according to claim 6, characterized in that: According to the classification results of each AC charging pile, corresponding power control measures are taken for low-impact charging piles, medium-impact charging piles and high-impact charging piles, specifically: For AC charging piles classified as low-impact charging piles, the specific power control measures are as follows: when the system power resources are sufficient, maintain the existing charging power allocation strategy, do not give priority to adjusting its power allocation ratio, and continuously monitor its operating status and charging demand changes; when the system power resources are insufficient, give priority to reducing the power allocation to low-impact charging piles, and regularly evaluate its charging power stability after adjustment; For AC charging piles classified as medium-impact charging piles, the power control measures taken are as follows: dynamically allocate charging power resources according to real-time three-phase voltage fluctuations and load status; adjust the power allocation strategy in the case of severe power fluctuations to prioritize grid stability, and periodically analyze and evaluate the operating status of medium-impact charging piles to optimize the power control strategy during the charging process; For AC charging piles classified as high-impact charging piles, the power control measures taken are as follows: prioritize reducing their load power fluctuations, alleviate the efficiency decline caused by voltage fluctuations by allocating stable power resources, and monitor their voltage fluctuations and load changes in real time; in addition, regularly conduct in-depth analysis of the operating parameters of high-impact charging piles, dynamically optimize their control models, and gradually reduce their impact on the system.

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