A dynamic power control method and system for electric vehicle charging pile

Abstract

The invention discloses a dynamic power control method and system for an electric vehicle charging pile, and relates to the technical field of dynamic power control of charging piles, including primary regulation and secondary regulation of the charging piles; obtaining a plurality of groups of different regulation time scale arrays; applying the plurality of groups of different regulation time scale arrays to the primary regulation and the secondary regulation, respectively, and obtaining oscillation evaluation data of parameter changes and multi-pile energy distribution uniformity data during the primary regulation and the secondary regulation; and constructing a regulation time scale evaluation model based on SVR; and constructing a three-dimensional change curve of the time scale-power regulation effect, and performing curve analysis to obtain an improved regulation time scale array, which is applied to the power control of the charging piles; and solving the problem that the difference between the time scales of the two is not fully considered, resulting in a system response hysteresis and difficulty in quickly adapting to load fluctuations or other dynamic changes.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic power control of charging piles, and more specifically, to a dynamic power control method and system for an electric vehicle charging pile. Background Art

[0002] The existing dynamic power control methods for electric vehicle charging piles mainly include dynamic power allocation methods based on load balancing. This method dynamically adjusts the charging power by real-time monitoring of the grid load status in the area where the charging pile is located. When the grid load is high, the output power of a single charging pile is reduced to protect the grid; when the grid load is low, the output power is appropriately increased to meet user needs. For example, some charging piles use time-of-use electricity prices combined with dynamic power allocation strategies to guide users to avoid charging during peak hours.

[0003] The dynamic power allocation method based on user priority dynamically adjusts the power output of the charging pile according to the charging priority set by the user. The priority can be set by the user through the APP, such as selecting "fast charging mode" or "energy-saving charging mode". Users with high priority will receive higher charging power when grid resources are limited, while users with low priority will delay charging or reduce power.

[0004] Based on the power control method of dynamic optimization of new energy, combined with the real-time output of new energy power generation, the power of the charging pile is dynamically adjusted. For example, some charging piles use photovoltaic power generation as a power source. When the solar radiation intensity is high, the charging power is increased; when the radiation intensity decreases, the power is automatically reduced, thereby maximizing the utilization of clean energy.

[0005] The dynamic power control method based on multi-pile collaboration dynamically allocates power by collaboratively controlling multiple charging piles within the charging network, taking into account the real-time load status and user needs of all charging piles.

[0006] The dynamic power control method based on multi-pile coordination achieves dynamic power coordination control of multiple DC charging piles by comparing voltage deviations, introducing virtual inertia control strategies, inertia center voltage, and adaptive parameter optimization, combined with current inner loop control. When the load changes, the system can ensure that the power input of each charging pile is effectively regulated through virtual parameter adjustment, maintain DC side voltage stability, and improve charging efficiency and system stability.

[0007] This dynamic power control method can not only adapt to different charging requirements and load fluctuations, but also optimize the voltage control of charging piles and realize intelligent and coordinated charging network management.

[0008] For example, the invention patent with announcement number CN117572138B announces a metering and calibration device for a high-power charging pile, including a simulated electric vehicle charging control unit and an intelligent display unit connected to the simulated electric vehicle charging control unit; the simulated electric vehicle charging control unit includes a charging socket; the charging socket is cooled by a liquid cooling unit; the intelligent display unit controls the start and stop of the high-power charging pile to be tested connected to the charging socket, and collects the charging status data of the high-power charging pile to be tested for display; the metering and acquisition unit collects the voltage and current output by the high-power charging pile to be tested and calculates the working error of the high-power charging pile to be tested during the charging time period, and transmits the collected voltage and current and the calculated working error to the intelligent display unit; the intelligent display unit controls the metering and acquisition unit to adjust the current acquisition connection mode according to the current collected by the metering and acquisition unit. This ensures wide-range acquisition accuracy and accurate metering of high-power charging piles.

[0009] For example, the invention patent with announcement number CN110726871B announces a charging pile metering and calibration device, which includes a charging pile body and a controller. The charging pile body is connected to the controller. The controller includes an MCU, a storage module, a communication module, a power metering module, an analog-to-digital converter, a current measurement module and a voltage measurement module, wherein the MCU is bidirectionally connected to the storage module and the communication module; the current measurement module and the voltage measurement module are both connected to the charging pile body to detect the charging current and charging voltage of the charging pile body, the signal output ends of the current measurement module and the voltage measurement module are both connected to the analog signal input end of the analog-to-digital converter, the digital signal output end of the analog-to-digital converter is divided into two paths, one path is input to the power metering unit, and the other path is input to the MCU, and the signal output end of the power metering unit is connected to the signal input end of the MCU; the present invention can calibrate the power metering of the charging pile, and connect to a remote server for auxiliary calculation to obtain accurate calculation data.

[0010] In the above-mentioned disclosed technical solution, there are at least the following technical problems: in the dynamic power control method based on multi-pile collaboration, the degree of coordination between primary control and secondary control directly determines the response speed and overall stability of the dynamic power control of multi-pile collaboration. The primary control is mainly responsible for quickly responding to voltage fluctuations and ensuring real-time performance, while the secondary control focuses on power balance and optimization over a long time scale to provide global stability.

[0011] After the primary control is adjusted, the secondary control has not yet completed the calculation optimization, resulting in a lag in the overall control process;

[0012] The secondary control adjustment results were not fed back to the primary control in time, resulting in control command conflicts;

[0013] Primary control usually responds in milliseconds, while secondary control is often optimized based on seconds or longer time scales. However, the prior art does not fully consider the difference in time scales between the two, resulting in system response hysteresis and difficulty in quickly adapting to load fluctuations or other dynamic changes. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0014] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a dynamic power control method and system for an electric vehicle charging pile, which analyzes the degree of coordination between primary control and secondary control to solve the problem that the difference in the time scales of the two is not fully considered, resulting in system response hysteresis and difficulty in quickly adapting to load fluctuations or other dynamic changes.

[0015] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a dynamic power control method for an electric vehicle charging pile, comprising the following steps: determining the voltage difference between the charging pile voltage and the total voltage of the charging pile to be regulated, when the voltage difference is greater than a preset regulation threshold, based on an initial primary regulation time scale, simulating physical inertia to regulate the instantaneous response capability, performing secondary regulation based on an initial secondary regulation time scale, and applying the simulated physical inertia after multiple regulation to the charging pile power control; constructing a regulation time scale array using the initial primary regulation time scale and the initial secondary regulation time scale, and performing random perturbations on the regulation time scale array, Several groups of different control time scale arrays are obtained; several groups of different control time scale arrays are applied to primary control and secondary control respectively, and the oscillation evaluation data of each parameter change and the uniform energy distribution data of multiple piles during primary control and secondary control are obtained; a control time scale evaluation model is constructed based on SVR according to the oscillation evaluation data of each parameter change and the uniform energy distribution data of multiple piles; the output of the control time scale evaluation model and the corresponding control time scale are mapped to a three-dimensional display model, and a three-dimensional change curve of the time scale-power control effect is constructed, and the curve analysis is performed to obtain an improved control time scale array, which is applied to charging pile power control.

[0016] In a preferred embodiment, the instantaneous response capability is regulated by simulating physical inertia based on the initial one-time regulation time scale, specifically: a low-pass filter is embedded in the control system, and the one-time regulation time scale is used as the cutoff frequency of the low-pass filter; damping control is performed by adjusting the ratio of the PID controller in the low-pass filter; an inertial response model is established according to the topological structure and electrical characteristics of the charging pile to simulate the power change caused by voltage control during damping control; and the regulation frequency is adjusted according to the inertial response model to perform primary control.

[0017] In a preferred embodiment, the secondary control is performed based on the initial secondary control time scale, and the simulated physical inertia after multiple controls is applied to the charging pile power control, specifically: the secondary control time scale is divided into several small periods, and the voltage and power data are periodically sampled; according to the periodically sampled voltage and power data, the power distribution of each charging pile is determined based on the optimization objective function of the power output, load demand and total voltage fluctuation of all charging piles; the residual voltage deviation in the primary control result is decomposed to each charging pile; the physical inertia simulated in the primary control process is used to adjust the smoothing factor during each power distribution to correct the power distribution, so as to obtain the simulated physical inertia applied to the charging pile power control.

[0018] In a preferred embodiment, the initial primary control time scale and the initial secondary control time scale are used to construct a control time scale array, and the control time scale array is randomly perturbed to obtain several groups of different control time scale arrays, specifically: according to historical data, the initial values ​​of the primary control time scale and the secondary control time scale are set; the primary and secondary control time scales are combined into an initial array; random perturbations within a preset range are added to the initial time scale, and different time scale combinations are automatically explored by a computer.

[0019] In a preferred embodiment, the multi-pile energy distribution uniformity data includes a multi-pile power scheduling deviation evaluation coefficient; the specific method for analyzing the multi-pile power scheduling deviation evaluation coefficient is as follows: obtain the required power of each charging pile and the actual output power of each charging pile when power control is performed, fit a linear model to represent the relationship between the required power of the charging pile and the actual output power of each charging pile, and calculate the power scheduling of each charging pile; calculate the multi-pile power scheduling fuzzy deviation evaluation coefficient by minimizing the root mean square error between the actual output power of each charging pile and the required power of the charging pile; calculate the multi-pile power scheduling fuzzy deviation evaluation coefficient with the preset multi-pile power scheduling deviation standard value to obtain the multi-pile power scheduling deviation evaluation coefficient.

[0020] In a preferred embodiment, the oscillation assessment data includes a voltage deviation periodic oscillation assessment coefficient and a power adjustment rate change abnormal coefficient; the specific method for obtaining the voltage deviation periodic oscillation assessment coefficient is as follows: sample the voltage signal and perform FFT analysis to obtain a spectrum diagram and find out the main frequency components; judge the periodic change of voltage based on the stronger frequency components in the spectrum diagram; quantify the voltage periodic change value by calculating the frequency change trend based on the differential method; obtain the voltage periodic change value within a preset time during primary and secondary regulation; calculate the periodic standard deviation and periodic average value of the voltage periodic change value within the preset time; calculate the voltage periodic change value abnormal coefficient based on the periodic standard deviation and periodic average value; calculate the voltage deviation periodic oscillation assessment coefficient based on the voltage periodic change value abnormal coefficient based on the preset voltage deviation periodic oscillation assessment coefficient calculation formula.

[0021] In a preferred embodiment, the specific method for obtaining the power adjustment rate change abnormal coefficient is as follows: obtain the power adjustment rate within a preset time when performing primary regulation and secondary regulation; calculate the power adjustment rate standard deviation and the power adjustment rate average of the power adjustment rate at different times within the preset time; calculate the power adjustment rate change coefficient based on the power adjustment rate standard deviation and the power adjustment rate average; calculate the power adjustment rate change coefficient based on a preset power adjustment rate change abnormal coefficient calculation formula.

[0022] In a preferred embodiment, the output of the control time scale evaluation model and the corresponding control time scale are mapped to a three-dimensional display model, and a three-dimensional change curve of the time scale-power control effect is constructed, specifically: the primary control time scale is used as the X-axis of the three-dimensional display model; the secondary control time scale is used as the Y-axis of the three-dimensional display model; the output of the control time scale evaluation model is used as the Z-axis of the three-dimensional display model; each group of control time scale arrays and the output of the corresponding control time scale evaluation model are mapped to the three-dimensional model, and each control time scale array corresponds to a three-dimensional coordinate point; and a three-dimensional change curve of the time scale-power control effect is constructed in three-dimensional space using a curve fitting algorithm according to the mapped data.

[0023] In a preferred embodiment, the curve analysis is performed to obtain an improved control time scale array, specifically: several local peaks of the time scale-power control effect three-dimensional change curve are obtained, and multiple peaks with distances less than a preset short-distance threshold and greater than a preset long-distance threshold are selected as redundant peaks for exclusion; the control time scale array corresponding to the peak whose second-order derivative of the remaining peaks is closest to zero is used as an improved control time scale array, and applied to charging pile power control.

[0024] The technical effects and advantages of the dynamic power control method and system of an electric vehicle charging pile of the present invention are as follows:

[0025] 1. The present invention adjusts the power response of the charging pile by simulating physical inertia and optimizes the stability of the power grid. Specifically, the method judges the difference between the charging pile and the total voltage, and when the voltage difference is greater than the set threshold, a two-stage control mechanism is adopted. First, the physical inertia is simulated by a low-pass filter and a PID controller to smooth the instantaneous voltage fluctuation and avoid over-regulation; secondly, based on the secondary control time scale, the power distribution is optimized, the residual voltage deviation is adjusted, and the power distribution between the charging piles is ensured to be uniform. By continuously adjusting the control time scale, combined with the voltage deviation oscillation evaluation coefficient and the power adjustment rate change abnormality coefficient, the power distribution strategy of the charging pile can be optimized in real time, thereby maintaining the stability and high responsiveness of the system when the load fluctuates and the voltage changes.

[0026] 2. The present invention can optimize the regulation strategy of charging pile power control and improve the frequency regulation effect of the power system by constructing a regulation time scale evaluation model based on support vector regression (SVR). By analyzing factors such as voltage deviation periodic oscillation, abnormal power adjustment rate change, and multi-pile power scheduling deviation, the output of the regulation time scale evaluation model is generated, thereby accurately evaluating the relationship between the time scale and the power regulation effect. The model maps the time scale and the evaluation coefficient into a three-dimensional display model, extracts the optimal regulation time scale array through curve analysis, and realizes the optimization of power regulation. The advantage of this method lies in its efficient parameter optimization and precise regulation capabilities, which can dynamically adjust the power distribution of the charging pile, ensure the stability and responsiveness of the system under a changing power grid environment, avoid over-regulation or uneven power distribution, and thus improve the overall power grid frequency regulation effect and energy efficiency utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention is a flow chart of a dynamic power control method for an electric vehicle charging pile.

[0028] Figure 2 It is a structural schematic diagram of a dynamic power control system of an electric vehicle charging pile according to the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Embodiment 1, Figure 1The present invention provides a dynamic power control method for an electric vehicle charging pile, comprising the following steps:

[0031] S1, determine the voltage difference between the charging pile voltage and the total voltage of the charging pile to be regulated. When the voltage difference is greater than the preset regulation threshold, based on the initial primary regulation time scale, simulate the physical inertia to regulate the instantaneous response capability, and perform secondary regulation based on the initial secondary regulation time scale, and apply the simulated physical inertia after multiple regulation to the charging pile power control.

[0032] The method of regulating the instantaneous response capability based on the initial one-time regulation time scale by simulating physical inertia is specifically as follows: embedding a low-pass filter in the control system and using the one-time regulation time scale as the cutoff frequency of the low-pass filter;

[0033] Damping control is performed by adjusting the ratio of the PID controller in the low-pass filter;

[0034] According to the topological structure and electrical characteristics of the charging pile, an inertial response model is established to simulate the power change caused by voltage control during damping control;

[0035] And the frequency is adjusted according to the inertial response model to perform primary control.

[0036] It should be noted that the concept of physical inertia simulation: the application of physical inertia in power systems, especially in the power control of charging piles, is mainly manifested in the ability to respond to instantaneous voltage fluctuations. In primary control, the core of physical inertia simulation is how to balance the rapid response of charging piles with the stability of the system. By simulating physical inertia, the system can make appropriate adjustments to voltage fluctuations in a short period of time, rather than overreacting or generating new fluctuations.

[0037] Setting the inertial response by initial time scale: The initial primary control time scale sets how the system responds to instantaneous voltage fluctuations. In practical applications, the primary control time scale is usually short, usually in milliseconds, to ensure a quick response to voltage fluctuations. Within this time scale, physical inertia can be simulated in the following ways:

[0038] Analog inertia filter: Design a filter with a large time constant to smooth the instantaneous voltage fluctuations so that the system does not over-adjust when responding. For example, set an integral link or low-pass filter to smooth the voltage fluctuation signal and limit the rate of change in the short term.

[0039] Damping control: In order to simulate physical inertia, a damping control mechanism can be added. By setting a reasonable damping coefficient, the system can respond gradually to voltage fluctuations instead of adjusting them directly and quickly. For example, when the voltage difference is large, the output power of the charging pile can be adjusted gradually to avoid additional fluctuations caused by over-adjustment.

[0040] Inertial model: An inertial response model can be designed based on the electrical characteristics of the system, such as the instantaneous load capacity of the power supply. By calculating the relationship between voltage and current, it simulates how the system "buffers" or delays its response when the voltage fluctuates. This model will provide gradual adjustments when the voltage fluctuates greatly, rather than making full adjustments immediately.

[0041] The secondary control is performed based on the initial secondary control time scale, and the simulated physical inertia after multiple controls is applied to the charging pile power control, specifically:

[0042] Divide the secondary control time scale into several small periods and periodically sample voltage and power data;

[0043] According to the periodically sampled voltage and power data, the power allocation of each charging pile is determined based on the optimization objective function of the power output, load demand and total voltage fluctuation of all charging piles;

[0044] Decompose the residual voltage deviation in the primary control result to each charging pile;

[0045] The power distribution is corrected by simulating physical inertia during a control process and adjusting the smoothing factor during each power distribution.

[0046] It should be noted that the secondary control time scale refers to the time interval for secondary control, which is usually in seconds or longer; its selection depends on the frequency of system load fluctuations and the needs of global optimization. A shorter secondary control time scale can improve the response speed, but may increase the control frequency; a longer secondary control time scale is more suitable for smoothing global fluctuations, but may delay the response.

[0047] Virtual inertia refers to the concept of simulating physical inertia, which is used to alleviate the instability caused by too fast adjustment of voltage fluctuations, and ensure a smoother regulation process by introducing a delay or buffer mechanism;

[0048] The setting of virtual inertia coefficient and damping coefficient directly affects the smoothness and response speed of control. Too high virtual inertia may cause response hysteresis, while too low virtual inertia may cause system oscillation. It is necessary to calibrate the appropriate range through experiments. Secondary control depends on accurate prediction of load changes. If the prediction model is inaccurate, it may lead to uneven power distribution or ineffective suppression of voltage fluctuations.

[0049] Secondary control often uses optimization algorithms (such as PSO, GA, etc.) for power allocation. These algorithms may increase the computational complexity due to the increase in the number of charging piles and optimization dimensions, thus affecting the real-time performance of the system.

[0050] There are mature existing technologies for primary distribution and secondary distribution, which will not be described in detail here.

[0051] S2, constructing a control time scale array with the initial first control time scale and the initial second control time scale, and randomly perturbing the control time scale array to obtain several groups of different control time scale arrays.

[0052] The initial primary control time scale and the initial secondary control time scale are used to construct a control time scale array, and the control time scale array is randomly perturbed to obtain several groups of different control time scale arrays, specifically:

[0053] According to historical data, set the initial values ​​of the primary control time scale and the secondary control time scale;

[0054] Combine the primary and secondary regulation time scales into an initial array;

[0055] Random perturbations within a preset range are added to the initial time scale, and different time scale combinations are automatically explored by computer.

[0056] The above method sets the initial values ​​of the primary control time scale and the secondary control time scale according to historical data, and generates multiple groups of time scale combinations in combination with random disturbances within a preset range. It has the following advantages: it makes full use of the guidance of historical data to ensure the rationality of the initial values ​​and the adaptability of the system; it expands the exploration range of the time scale through random disturbances, can cover more potential optimization combinations, and improve the comprehensiveness and adaptability of the control strategy; at the same time, the use of computers to automatically generate and evaluate time scale combinations greatly improves efficiency and accuracy, and provides a reliable foundation for achieving high response speed and global stability of dynamic power control.

[0057] S3, applying several groups of different control time scale arrays to the primary control and the secondary control respectively, and obtaining the oscillation evaluation data of the changes of each parameter during the primary control and the secondary control and the uniform energy distribution data of multiple piles.

[0058] The oscillation assessment data includes a voltage deviation period oscillation assessment coefficient and a power adjustment rate change abnormality coefficient; the multi-pile energy distribution uniformity data includes a multi-pile power scheduling deviation assessment coefficient.

[0059] The multi-pile power scheduling deviation evaluation coefficient is used to quantify the uniformity of energy distribution in multi-pile coordinated regulation. The acquisition method includes: counting the power input data of all charging piles within a fixed time window, calculating the maximum and minimum power difference and standard deviation, and evaluating the degree of deviation in combination with the distribution model. This coefficient can intuitively reflect whether there is obvious unevenness in power distribution, thereby evaluating the effect of secondary regulation in achieving global power optimization. The smaller the deviation coefficient, the more balanced the multi-pile energy distribution and the higher the system efficiency.

[0060] Analyzing the multi-pile power dispatch deviation evaluation coefficient has the following advantages for screening out the optimal time scale and solving the problem of insufficient consideration of the difference between the two time scales, which leads to system response hysteresis and difficulty in quickly adapting to load fluctuations or other dynamic changes:

[0061] The multi-pile power dispatch deviation evaluation coefficient can quantify the degree of imbalance in power distribution among multiple charging piles. By analyzing the impact of different time scale combinations on this coefficient, a time scale with more uniform power dispatch can be selected, effectively avoiding the situation where some piles are overloaded or unloaded, and improving the overall coordination efficiency.

[0062] This evaluation coefficient provides a global perspective on the energy distribution deviation between multiple piles. By finding the optimal balance point in time scale between the rapid response of primary regulation and the global optimization of secondary regulation, it ensures that the system can dynamically adapt to complex load changes and achieve overall power optimization over a long time scale.

[0063] Unbalanced power distribution often reflects unreasonable time scale configuration, resulting in delayed or conflicting control responses. By optimizing the time scale to minimize the multi-pile power dispatch deviation assessment coefficient, the system's dynamic response capability can be significantly improved, allowing it to quickly adapt to load fluctuations or environmental changes.

[0064] Reasonable time scale optimization can ensure that the control process has both responsiveness and global stability. The multi-pile power dispatch deviation evaluation coefficient provides a key basis for adjusting the time scale by measuring the imbalance of energy dispatch, avoiding the problem of too fast primary control rate or too slow secondary control optimization.

[0065] Unbalanced power distribution will increase the control burden, resulting in reduced energy distribution efficiency and uneven equipment utilization. Optimizing the time scale to minimize the multi-pile power scheduling deviation evaluation coefficient can effectively reduce system operating costs, extend equipment life, and reduce additional energy consumption caused by unbalanced power scheduling.

[0066] The multi-pile power dispatch deviation evaluation coefficient directly reflects the execution effect of the coordinated control strategy. By optimizing the time scale combination, this coefficient can be used to verify the scientific nature of the control strategy and further promote the deep collaboration between primary and secondary control.

[0067] In summary, the multi-pile power dispatch deviation evaluation coefficient provides an intuitive and reliable quantitative basis for time scale optimization by evaluating the energy distribution balance and coordinated control effect, which helps to solve the system response hysteresis problem caused by the regulation time difference, thereby achieving a balance between dynamic adaptation to load changes and global optimization goals.

[0068] The specific method for obtaining the analysis coefficient of the multi-pile power dispatch deviation evaluation is as follows:

[0069] Obtain the required power of each charging pile and the actual output power of each charging pile during power regulation, fit a linear model to represent the relationship between the required power of the charging pile and the actual output power of each charging pile, and calculate the power scheduling of each charging pile;

[0070] The fuzzy deviation evaluation coefficient of multi-pile power scheduling is calculated by minimizing the root mean square error between the actual output power of each charging pile and the required power of the charging pile;

[0071] The multi-pile power scheduling deviation evaluation coefficient is calculated by the multi-pile power scheduling fuzzy deviation evaluation coefficient and the preset multi-pile power scheduling deviation standard value to obtain the multi-pile power scheduling deviation evaluation coefficient.

[0072] The specific calculation formula for the power dispatch of each charging pile is as follows:

[0073]

[0074] In the formula, Power scheduling for each charging pile, The power required for the charging pile, is the multi-pile power dispatch deviation coefficient, is the total power fluctuation value, is the error;

[0075] The calculation formula of the multi-pile power scheduling fuzzy deviation evaluation coefficient is as follows:

[0076]

[0077] In the formula, is the number of charging piles, The power required for the charging pile, is the average power demand of the charging pile, For each charging pile, the actual output power is is the average value of the actual output power of each charging pile, is the fuzzy deviation evaluation coefficient of multi-pile power scheduling, v is the number of the charging pile;

[0078] The specific formula of the multi-pile power dispatch deviation evaluation coefficient is as follows:

[0079]

[0080] In the formula, is the multi-pile power dispatch deviation evaluation coefficient, It is the standard value of multi-pile power dispatch deviation.

[0081] The voltage deviation periodic oscillation assessment coefficient is used to evaluate the periodic oscillation characteristics of voltage changes during regulation and is an important indicator for measuring the dynamic stability of primary and secondary regulation. The method for obtaining this coefficient includes: real-time monitoring of the difference between the DC side voltage of the charging pile and the rated voltage of the busbar, extracting its frequency and amplitude characteristics through Fourier transform or time series analysis, and quantifying the oscillation intensity. This coefficient mainly reflects whether the instantaneous response of voltage control is overly sensitive, and whether the secondary regulation effectively eliminates long-term oscillation phenomena.

[0082] Analyzing the voltage deviation period oscillation evaluation coefficient has the following advantages for selecting the optimal time scale and solving the problem of insufficient consideration of the difference between the two time scales, which leads to system response hysteresis and difficulty in quickly adapting to load fluctuations or other dynamic changes:

[0083] The voltage deviation periodic oscillation assessment coefficient directly reflects the impact of the control time scale on the transient voltage response by quantifying the periodicity and amplitude changes of the voltage deviation. By analyzing this coefficient, it can be determined whether the control is too fast, resulting in over-adjustment, or too slow, resulting in response hysteresis, providing an accurate basis for time scale optimization.

[0084] The regulation of different time scales will affect the real-time performance and global stability of the system. The voltage deviation periodic oscillation evaluation coefficient can effectively identify the performance when the time scales of the two are not coordinated, such as frequent oscillations in the short term or long-term lags, thereby balancing the needs of real-time rapid response and long-term stability.

[0085] This coefficient provides a clear direction for time scale optimization. For example, when the oscillation frequency is high and the amplitude is significant, it indicates that the time scale of the first regulation needs to be appropriately extended; while if the oscillation tends to be gentle but lasts too long, the time scale of the second regulation needs to be shortened to avoid the system regulation being too radical or conservative.

[0086] Under complex dynamic load conditions, the voltage deviation periodic oscillation assessment coefficient can evaluate the adaptability of the system response in real time. By screening the optimal time scale combination, it ensures that the control strategy can quickly adapt to load fluctuations and improve the overall adaptability of the system.

[0087] In summary, by introducing the voltage deviation periodic oscillation evaluation coefficient to optimize the time scale, not only can the response lag problem caused by the time scale difference be solved, but also precise regulation under dynamic load can be achieved, thereby improving the speed and stability of the system.

[0088] The specific method for obtaining the voltage deviation period oscillation evaluation coefficient is as follows:

[0089] Obtain the voltage cycle change value within the preset time during primary regulation and secondary regulation;

[0090] Calculate the period standard deviation and period average value of the voltage period variation value within a preset time;

[0091] According to the cycle standard deviation and cycle average value, calculate the abnormal coefficient of voltage cycle variation value;

[0092] The voltage deviation periodic oscillation evaluation coefficient is calculated based on the voltage periodic variation value abnormal coefficient and the preset voltage deviation periodic oscillation evaluation coefficient calculation formula.

[0093] The specific calculation formula of the voltage period variation value abnormal coefficient is as follows:

[0094]

[0095] The specific calculation formula of the voltage deviation period oscillation evaluation coefficient is as follows:

[0096]

[0097] In the formula, is the voltage cycle variation value abnormal coefficient, is the voltage cycle change value at the jth moment, is the number of voltage cycle variation values ​​collected within a preset time, and j is the time label; It is the voltage deviation period oscillation evaluation coefficient.

[0098] It should be noted that the voltage periodic change value can accurately reflect the periodic characteristics of voltage fluctuations and its changing trend. The specific acquisition method is as follows:

[0099] Sample the voltage signal and perform FFT analysis to obtain the spectrum and find the main frequency components;

[0100] Judging the periodic change of voltage based on the stronger frequency components in the spectrum diagram;

[0101] The frequency variation trend is calculated by the differential method and the voltage cycle variation value is quantified.

[0102] The power adjustment rate change abnormality coefficient is an important indicator to measure the stability and transition smoothness of the dynamic response process of power regulation. This coefficient is obtained by recording the power change of the charging pile per unit time (such as the power adjustment amplitude and rate), and combining the sliding window technology and the statistical analysis of the change rate to extract the frequency and amplitude of abnormal fluctuations. A power adjustment rate that is too high may cause frequent regulation actions, while a rate that is too low may lead to a slow response. This coefficient provides a reference for optimizing the primary and secondary regulation strategies by quantifying the abnormal fluctuations in the power adjustment process.

[0103] Analyzing the abnormal coefficient of power adjustment rate change has the following advantages for selecting the optimal time scale and solving the problem of insufficient consideration of the difference between the two time scales, which leads to system response hysteresis and difficulty in quickly adapting to load fluctuations or other dynamic changes:

[0104] The power adjustment rate change abnormality coefficient can directly measure the impact of time scale selection on control flexibility by quantifying the change of adjustment rate during power control. When the power adjustment rate fluctuates abnormally, it indicates that there is a problem with time scale matching, and the adjustment is too intense or lagging. By analyzing this coefficient, a clear adjustment direction can be provided for time scale optimization.

[0105] Under different time scales, a too fast primary regulation may cause short-term violent fluctuations, while a too slow secondary regulation may cause global optimization hysteresis. The power adjustment rate change anomaly coefficient can quantify the smoothness and mutation of power changes, and help screen the time scale combination that can quickly respond to load fluctuations and ensure stable adjustment.

[0106] In scenarios with frequent load fluctuations, this coefficient can reflect the power adjustment dynamics of the system in real time and guide the adjustment time scale to adapt to rapidly changing load demands, thereby avoiding unbalanced power distribution due to slow response or unnecessary adjustment shocks caused by too fast response.

[0107] By evaluating the abnormal coefficient of the power adjustment rate change, the time scale combination that keeps the power adjustment rate within a reasonable range can be screened out, avoiding conflicts between control instructions and improving the execution efficiency of the power allocation strategy.

[0108] Abnormal changes in the control rate will increase the load and energy consumption of the equipment. By optimizing the time scale to make the power adjustment rate stable, the system energy consumption and equipment wear caused by frequent power fluctuations can be reduced, thereby extending the life of the equipment.

[0109] In summary, the power regulation rate change abnormal coefficient plays an important guiding role in time scale optimization. By balancing rapid response and long-term stability, it solves the response lag problem caused by time scale differences, ensures that the system can efficiently adapt to load fluctuations, and achieves dynamic optimization of control objectives.

[0110] The specific method for obtaining the power adjustment rate change abnormal coefficient is as follows:

[0111] Obtaining the power adjustment rate within a preset time when performing primary regulation and secondary regulation;

[0112] Calculate the power adjustment rate standard deviation and the power adjustment rate average of the power adjustment rates at different times within a preset time;

[0113] Calculate the power regulation rate variation coefficient according to the power regulation rate standard deviation and the power regulation rate average value;

[0114] The power adjustment rate change coefficient is calculated based on a preset power adjustment rate change abnormality coefficient calculation formula.

[0115] The specific calculation formula of the power adjustment rate change coefficient is as follows:

[0116]

[0117] The specific calculation formula of the power adjustment rate change abnormal coefficient is as follows:

[0118]

[0119] In the formula, is the power adjustment rate change coefficient, is the power adjustment rate, is the total number of data collected within the preset time, j is the time label; It is the power adjustment rate change abnormal coefficient.

[0120] This embodiment adjusts the power response of the charging pile by simulating physical inertia to optimize the stability of the power grid. Specifically, the method judges the difference between the charging pile and the total voltage, and when the voltage difference is greater than the set threshold, a two-stage control mechanism is adopted. First, the physical inertia is simulated by a low-pass filter and a PID controller to smooth the instantaneous voltage fluctuations and avoid over-regulation; secondly, based on the secondary control time scale, the power distribution is optimized, the residual voltage deviation is adjusted, and the power distribution between the charging piles is ensured to be uniform. By continuously adjusting the control time scale, combined with the voltage deviation oscillation evaluation coefficient and the power adjustment rate change abnormality coefficient, the power distribution strategy of the charging pile can be optimized in real time, thereby maintaining the stability and high responsiveness of the system when the load fluctuates and the voltage changes.

[0121] Embodiment 2, S4, constructs a control time scale evaluation model based on SVR according to the oscillation evaluation data of each parameter change and the uniform energy distribution data of multiple piles.

[0122] The control time scale evaluation model is constructed based on SVR according to the oscillation evaluation data of each parameter change and the uniform energy distribution data of multiple piles, specifically:

[0123] The obtained voltage deviation period oscillation evaluation coefficient, power adjustment rate change abnormal coefficient and multi-pile power dispatch deviation evaluation coefficient are used to construct a photovoltaic power station frequency regulation effect evaluation model to generate a regulation time scale evaluation coefficient.

[0124] The specific calculation formula of the control time scale evaluation coefficient is as follows:

[0125]

[0126] In the formula, To regulate the time scale evaluation coefficient, is the preset proportional coefficient of the voltage deviation period oscillation evaluation coefficient, is the preset proportional coefficient of the power adjustment rate abnormality coefficient, is the preset proportional coefficient of the multi-pile power dispatch deviation evaluation coefficient, is the voltage deviation period oscillation evaluation coefficient, is the power adjustment rate change abnormal coefficient, It is the multi-pile power dispatch deviation evaluation coefficient.

[0127] S5, maps the output of the control time scale evaluation model and the corresponding control time scale into the three-dimensional display model, constructs a three-dimensional change curve of the time scale-power control effect, and performs curve analysis to obtain an improved control time scale array, which is applied to the charging pile power control.

[0128] The output of the control time scale evaluation model and the corresponding control time scale are mapped to the three-dimensional display model, and a three-dimensional change curve of the time scale-power control effect is constructed, specifically:

[0129] The time scale of a single regulation is used as the X-axis of the three-dimensional display model;

[0130] The secondary regulation time scale is used as the Y-axis of the three-dimensional display model;

[0131] The regulatory time scale evaluation coefficient is used as the Z axis of the three-dimensional display model;

[0132] Map each group of control time scale arrays and their corresponding control time scale evaluation coefficients to the three-dimensional model, and each time scale combination corresponds to a three-dimensional coordinate point;

[0133] According to the mapped data, a three-dimensional change curve of time scale-power regulation effect is constructed in three-dimensional space using a curve fitting algorithm.

[0134] The curve analysis is performed to obtain an improved control time scale array, specifically:

[0135] Acquire several local peaks of the three-dimensional change curve of the time scale-power regulation effect, and select multiple peaks whose distances are less than a preset short-distance threshold and greater than a preset long-distance threshold as redundant peaks for exclusion;

[0136] The control time scale array corresponding to the peak whose second-order derivative of the residual peak is closest to zero is used as the improved control time scale array and applied to the charging pile power control.

[0137] It should be noted that the density between peaks can indicate the coverage of the control effect. Usually, areas with dense peaks and high control effects are selected on the surface. If the areas corresponding to some peaks are too sparse, it may mean that these peaks do not have high robustness in practical applications.

[0138] This embodiment can optimize the regulation strategy of charging pile power control and improve the frequency regulation effect of the power system by constructing a regulation time scale evaluation model based on support vector regression (SVR). By analyzing factors such as voltage deviation periodic oscillation, abnormal power adjustment rate change, and multi-pile power scheduling deviation, the regulation time scale evaluation coefficient is generated to accurately evaluate the relationship between the time scale and the power regulation effect. The model maps the time scale and the evaluation coefficient to a three-dimensional display model, extracts the optimal regulation time scale array through curve analysis, and realizes the optimization of power regulation. The advantage of this method lies in its efficient parameter optimization and precise regulation capabilities, which can dynamically adjust the power distribution of the charging pile, ensure the stability and responsiveness of the system in a changing power grid environment, avoid over-regulation or uneven power distribution, and thus improve the overall power grid frequency regulation effect and energy efficiency utilization.

[0139] Embodiment 3, Figure 2 A dynamic power control system for an electric vehicle charging pile, comprising a power control module, a control time scale construction module, a control data acquisition module, a data analysis module and a control time improvement module;

[0140] The power control module is used to determine the voltage difference between the charging pile voltage and the total voltage of the charging pile to be controlled. When the voltage difference is greater than a preset control threshold, the instantaneous response capability is controlled by simulating physical inertia based on the initial primary control time scale, and secondary control is performed based on the initial secondary control time scale, and the simulated physical inertia after multiple controls is applied to the charging pile power control;

[0141] A control time scale construction module is used to construct a control time scale array using an initial primary control time scale and an initial secondary control time scale, and to perform random perturbations on the control time scale array to obtain a number of different control time scale arrays;

[0142] A control data acquisition module is used to apply several groups of different control time scale arrays to the primary control and the secondary control respectively, and obtain the oscillation evaluation data of the changes of various parameters and the uniform energy distribution data of multiple piles during the primary control and the secondary control;

[0143] The data analysis module is used to construct a control time scale evaluation model based on SVR according to the oscillation evaluation data of each parameter change and the uniform energy distribution data of multiple piles;

[0144] The control time improvement module is used to map the output of the control time scale evaluation model and the corresponding control time scale into a three-dimensional display model, and to construct a three-dimensional change curve of the time scale-power control effect, and to perform curve analysis to obtain an improved control time scale array for application in charging pile power control.

[0145] 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.

[0146] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0147] Those of ordinary skill in the art will appreciate that the modules 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.

[0148] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0149] 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.

[0150] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic power control method for an electric vehicle charging pile, characterized in that: The steps include: Determine the voltage difference between the charging pile voltage and the total voltage of the charging pile to be regulated. When the voltage difference is greater than a preset regulation threshold, simulate physical inertia to regulate the instantaneous response capability based on the initial primary regulation time scale, perform secondary regulation based on the initial secondary regulation time scale, and apply the simulated physical inertia after multiple regulation to the charging pile power control; The initial primary control time scale and the initial secondary control time scale are used to construct a control time scale array, and the control time scale array is randomly perturbed to obtain several groups of different control time scale arrays; Several groups of different control time scale arrays are applied to the primary control and the secondary control respectively, and the oscillation evaluation data of the changes of each parameter and the uniform energy distribution data of multiple piles during the primary control and the secondary control are obtained; According to the oscillation evaluation data of each parameter change and the uniform energy distribution data of multiple piles, a control time scale evaluation model is constructed based on SVR; The output of the control time scale evaluation model and the corresponding control time scale are mapped into the three-dimensional display model, and a three-dimensional change curve of the time scale-power control effect is constructed. After the curve analysis, an improved control time scale array is obtained and applied to the power control of charging piles.

2. The dynamic power control method of the electric vehicle charging pile according to claim 1, characterized in that: The method of regulating the instantaneous response capability based on the initial one-time regulation time scale by simulating physical inertia is specifically as follows: embedding a low-pass filter in the control system and using the one-time regulation time scale as the cutoff frequency of the low-pass filter; Damping control is performed by adjusting the ratio of the PID controller; According to the topological structure and electrical characteristics of the charging pile, an inertial response model is established to simulate the power change caused by voltage control during damping control; And the frequency is adjusted according to the inertial response model to perform primary control.

3. The dynamic power control method of the electric vehicle charging pile according to claim 2, characterized in that: The secondary control is performed based on the initial secondary control time scale, and the simulated physical inertia after multiple controls is applied to the charging pile power control, specifically: Divide the secondary control time scale into several small periods and periodically sample voltage and power data; According to the periodically sampled voltage and power data, the power allocation of each charging pile is determined based on the optimization objective function of the power output, load demand and total voltage fluctuation of all charging piles; Decompose the residual voltage deviation in the primary control result to each charging pile; By simulating physical inertia in a primary regulation process, the smoothing factor of each power distribution is adjusted to correct the power distribution, and the simulated physical inertia is applied to the power control of the charging pile.

4. The dynamic power control method of the electric vehicle charging pile according to claim 3 is characterized in that: The initial primary control time scale and the initial secondary control time scale are used to construct a control time scale array, and the control time scale array is randomly perturbed to obtain several groups of different control time scale arrays, specifically: According to historical data, set the initial values ​​of the primary control time scale and the secondary control time scale; Combine the primary and secondary regulation time scales into an initial array; Random perturbations within a preset range are added to the initial time scale, and different time scale combinations are automatically explored by computer.

5. The dynamic power control method of the electric vehicle charging pile according to claim 4, characterized in that: The multi-pile energy distribution uniformity data includes a multi-pile power scheduling deviation evaluation coefficient; the specific method for obtaining the multi-pile power scheduling deviation evaluation coefficient is as follows: Obtain the required power of each charging pile and the actual output power of each charging pile during power regulation, fit a linear model to represent the relationship between the required power of the charging pile and the actual output power of each charging pile, and calculate the power scheduling of each charging pile; The fuzzy deviation evaluation coefficient of multi-pile power scheduling is calculated by minimizing the root mean square error between the actual output power of each charging pile and the required power of the charging pile; The multi-pile power dispatching deviation evaluation coefficient is calculated by combining the multi-pile power dispatching deviation fuzzy evaluation coefficient with the preset multi-pile power dispatching deviation standard value to obtain the multi-pile power dispatching deviation evaluation coefficient; The calculation formula of the multi-pile power scheduling fuzzy deviation evaluation coefficient is as follows: In the formula, is the number of charging piles, The power required for the charging pile, is the average power demand of the charging pile, For each charging pile, the actual output power is is the average value of the actual output power of each charging pile, is the fuzzy deviation evaluation coefficient of multi-pile power scheduling, and v is the number of the charging pile.

6. The dynamic power control method of the electric vehicle charging pile according to claim 5, characterized in that: The oscillation assessment data includes a voltage deviation period oscillation assessment coefficient and a power regulation rate change abnormality coefficient; the specific method for obtaining the voltage deviation period oscillation assessment coefficient is as follows: Sample the voltage signal and perform FFT analysis to obtain the spectrum and find the main frequency components; Judging the periodic change of voltage based on the stronger frequency components in the spectrum diagram; By calculating the frequency variation trend based on the differential method, the voltage cycle variation value can be quantified; Obtain the voltage cycle change value within the preset time during primary regulation and secondary regulation; Calculate the period standard deviation and period average value of the voltage period variation value within a preset time; According to the cycle standard deviation and cycle average value, calculate the abnormal coefficient of voltage cycle variation value; The voltage deviation periodic oscillation evaluation coefficient is calculated based on the voltage periodic variation value abnormal coefficient and the preset voltage deviation periodic oscillation evaluation coefficient calculation formula.

7. The dynamic power control method of the electric vehicle charging pile according to claim 6, characterized in that: The specific method for obtaining the power adjustment rate change abnormal coefficient is as follows: Obtaining the power adjustment rate within a preset time when performing primary regulation and secondary regulation; Calculate the power adjustment rate standard deviation and the power adjustment rate average of the power adjustment rates at different times within a preset time; Calculate the power regulation rate variation coefficient according to the power regulation rate standard deviation and the power regulation rate average value; The power adjustment rate change coefficient is calculated based on a preset power adjustment rate change abnormality coefficient calculation formula.

8. The dynamic power control method of the electric vehicle charging pile according to claim 7, characterized in that: The output of the control time scale evaluation model and the corresponding control time scale are mapped to the three-dimensional display model, and a three-dimensional change curve of the time scale-power control effect is constructed, specifically: The time scale of a single regulation is used as the X-axis of the three-dimensional display model; The secondary regulation time scale is used as the Y-axis of the three-dimensional display model; The output of the regulatory time scale assessment model is used as the Z axis of the three-dimensional display model; Map each group of control time scale arrays and the output of the corresponding control time scale evaluation model to the three-dimensional model, and each time scale combination corresponds to a three-dimensional coordinate point; According to the mapped data, a three-dimensional change curve of time scale-power regulation effect is constructed in three-dimensional space using a curve fitting algorithm.

9. The dynamic power control method of the electric vehicle charging pile according to claim 8, characterized in that: The curve analysis is performed to obtain an improved control time scale array, specifically: Acquire several local peaks of the three-dimensional change curve of the time scale-power regulation effect, and select multiple peaks whose distances are less than a preset short-distance threshold and greater than a preset long-distance threshold as redundant peaks for exclusion; The control time scale array corresponding to the peak whose second-order derivative of the residual peak is closest to zero is used as the improved control time scale array and applied to the charging pile power control.

10. A system using the dynamic power control method of an electric vehicle charging pile as claimed in any one of claims 1 to 9, comprising a power control module, a control time scale construction module, a control data acquisition module, a data analysis module and a control time improvement module; The power control module is used to determine the voltage difference between the charging pile voltage and the total voltage of the charging pile to be controlled. When the voltage difference is greater than a preset control threshold, the instantaneous response capability is controlled by simulating physical inertia based on the initial primary control time scale, and secondary control is performed based on the initial secondary control time scale, and the simulated physical inertia after multiple controls is applied to the charging pile power control; A control time scale construction module is used to construct a control time scale array using an initial primary control time scale and an initial secondary control time scale, and to perform random perturbations on the control time scale array to obtain a number of different control time scale arrays; A control data acquisition module is used to apply several groups of different control time scale arrays to the primary control and the secondary control respectively, and obtain the oscillation evaluation data of the changes of various parameters and the uniform energy distribution data of multiple piles during the primary control and the secondary control; The data analysis module is used to construct a control time scale evaluation model based on SVR according to the oscillation evaluation data of each parameter change and the uniform energy distribution data of multiple piles; The control time improvement module is used to map the output of the control time scale evaluation model and the corresponding control time scale into a three-dimensional display model, and to construct a three-dimensional change curve of the time scale-power control effect, and to perform curve analysis to obtain an improved control time scale array for application in charging pile power control.

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