Charging pile dynamic power distribution method and device based on load prediction

Abstract

The invention relates to the technical field of charging pile power distribution, and discloses a charging pile dynamic power distribution method and device based on load prediction, and the method comprises the steps: carrying out the system identification analysis of the real-time operation data of a charging pile, and constructing a charging pile system small signal model; training to obtain a low-sensitivity load prediction model and a high-sensitivity load prediction model, and generating charging load prediction data; performing control optimization calculation to obtain a dynamic power compensation control parameter; genetic optimization and multi-stage power distribution are carried out, and a multi-stage power distribution scheme including a conventional load distribution mode and a peak load distribution mode is obtained; power limitation is carried out by reducing charging power of non-priority users, load transfer is carried out through power coordination of adjacent charging piles, an emergency power adjustment control instruction is obtained, a closed-loop optimization mechanism is adopted, and stable operation of a charging pile system under various working conditions is guaranteed through parameter online optimization and dynamic adjustment of a control strategy.

Description

Technical Field

[0001] The present application relates to the technical field of charging pile power distribution, and in particular to a method and device for dynamic power distribution of a charging pile based on load prediction. Background Art

[0002] As charging demand continues to increase, the charging pile system faces problems such as low load prediction accuracy, poor power allocation efficiency and poor system stability. These problems are mainly due to the randomness and uncertainty of electric vehicle users' charging behavior, which makes it difficult to accurately predict the charging load, thus affecting the power scheduling and allocation of charging piles.

[0003] The current charging pile system generally adopts a fixed power allocation strategy, which cannot dynamically adjust the power output according to the real-time load changes, and easily causes system overload during peak load periods. At the same time, the existing power allocation method lacks consideration of user charging priorities, and cannot reasonably allocate charging resources when the system capacity is limited, affecting the user's charging experience. In addition, under large-scale access conditions, the traditional charging pile control system is difficult to cope with the power fluctuations caused by random charging demand due to the lack of effective load prediction and dynamic power compensation mechanism. Especially during peak charging periods, if effective dynamic power allocation measures are not implemented, it is easy to cause local charging pile overload or power grid fluctuations, affecting the safe and stable operation of the entire charging service system. Summary of the invention

[0004] The present application provides a method and device for dynamic power allocation of a charging pile based on load prediction. The present application adopts a closed-loop optimization mechanism to ensure the stable operation of the charging pile system under various working conditions through online parameter optimization and dynamic adjustment of control strategies.

[0005] In a first aspect, the present application provides a method for dynamic power allocation of a charging pile based on load prediction, and the method for dynamic power allocation of a charging pile based on load prediction includes: Conduct system identification and analysis on the real-time operation data of charging piles and build a small signal model of the charging pile system; Based on the charging pile system small signal model, a low-sensitivity load prediction model and a high-sensitivity load prediction model are trained to generate charging load prediction data; According to the deviation value between the charging load prediction data and the actual load data, a control optimization calculation is performed on the proportional compensation unit and the integral compensation unit to obtain a dynamic power compensation control parameter; Based on the dynamic power compensation control parameters, the power distribution ratio of the charging pile is genetically optimized and multi-level power distribution is performed to obtain a multi-level power distribution scheme including a conventional load distribution mode and a peak load distribution mode; According to the multi-level power allocation scheme, power limitation is performed by reducing the charging power of non-priority users, and load transfer is performed by coordinating the power of adjacent charging piles to obtain an emergency power adjustment control instruction.

[0006] The second aspect of the present application provides a charging pile dynamic power allocation device based on load prediction, and the charging pile dynamic power allocation device based on load prediction includes: Identification module, used to perform system identification analysis on the real-time operation data of the charging pile and build a small signal model of the charging pile system; A training module, used for training a low-sensitivity load prediction model and a high-sensitivity load prediction model based on the charging pile system small signal model, and generating charging load prediction data; A calculation module, used to perform control optimization calculation on a proportional compensation unit and an integral compensation unit according to a deviation value between the charging load prediction data and the actual load data, so as to obtain a dynamic power compensation control parameter; A distribution module, used to perform genetic optimization and multi-level power distribution on the power distribution ratio of the charging pile based on the dynamic power compensation control parameters, and obtain a multi-level power distribution scheme including a conventional load distribution mode and a peak load distribution mode; The processing module is used to perform power limitation by reducing the charging power of non-priority users according to the multi-level power allocation scheme, and to transfer the load by coordinating the power of adjacent charging piles to obtain an emergency power regulation control instruction.

[0007] The third aspect of the present application provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned dynamic power allocation method for charging piles based on load prediction.

[0008] A fourth aspect of the present application provides a computer-readable storage medium, in which instructions are stored. When the computer-readable storage medium is executed on a computer, the computer executes the above-mentioned method for dynamic power allocation of charging piles based on load prediction.

[0009] Compared with the prior art, the present application has the following beneficial effects: by establishing a small signal model of the charging pile system and combining the system identification method to accurately describe the operating characteristics of the charging pile, the limitations of the traditional modeling method in large-scale charging pile systems are overcome; a dual-model prediction strategy combining the response surface method and the neural network is adopted to perform load prediction for normal working conditions and peak working conditions respectively, which significantly improves the accuracy of load prediction; a dynamic power compensator is designed based on the pole configuration method, and the influence of load prediction deviation on system stability is effectively suppressed by optimizing the proportional integral control parameters; a genetic algorithm is used to perform multi-objective optimization of the charging pile power distribution, and load fluctuations are minimized while ensuring the minimum system loss; an emergency power adjustment mechanism based on user priority is designed, and the system overload problem during peak charging is solved by combining power limitation and load transfer; the present application adopts a closed-loop optimization mechanism, and ensures the stable operation of the charging pile system under various working conditions through online parameter optimization and dynamic adjustment of the control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0012] Figure 1 It is a flow chart of a method for dynamic power allocation of charging piles based on load prediction provided by an embodiment of the present invention; Figure 2 is a schematic block diagram of the structure of a charging pile dynamic power distribution device based on load prediction provided by an embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] 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 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.

[0014] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0015] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0016] It should be further understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. Figure 1 In the embodiment of the present application, an embodiment of a method for dynamic power allocation of charging piles based on load prediction includes: Step 100: Perform system identification analysis on the real-time operation data of the charging pile and construct a small signal model of the charging pile system; It is understandable that the execution subject of the present application can be a charging pile dynamic power allocation device based on load prediction, or a terminal or a server, which is not limited here. The embodiment of the present application is described by taking the server as the execution subject as an example.

[0017] Specifically, data collection is performed on the output power of the charging pile, local user load data and charging demand data to obtain the real-time operating status of the charging pile. The collected data includes dynamic information such as charging current, voltage, power, charging mode and user load, covering the diverse operating status of the system under different load conditions. The real-time operating data of the charging pile is preprocessed to ensure the accuracy and effectiveness of subsequent analysis. Data preprocessing includes operations such as outlier removal, noise filtering, data interpolation and smoothing to eliminate existing external interference or acquisition errors and generate a preprocessed operating data sequence with good continuity and consistency. The preprocessed operating data sequence is input into the recursive least squares algorithm model, and the rated power coefficient and power regulation coefficient of the charging pile are identified by iterative calculation. The recursive least squares algorithm is an online identification tool that can dynamically update parameter estimation results based on time series data and is suitable for use in real-time systems. Through the calculation of the algorithm, a set of system characteristic parameter sets that characterize the dynamic characteristics of the charging pile are obtained, reflecting the core physical characteristics and operating status of the charging pile. Based on the system characteristic parameter set, the state space model is used to model and analyze the steady-state operating characteristics of the charging pile under rated conditions to construct a steady-state operation model. The state space model describes the dynamic behavior of the system under the influence of external input. Its mathematical expression includes state equations and output equations, which can fully reflect the dynamic relationship between the input and output of the charging pile under steady-state conditions. In the modeling process, attention is paid to the stability and sensitivity near the rated power point to ensure that the model can accurately describe the steady-state characteristics of the charging pile under normal working conditions. At the same time, the transfer function method is used to model and analyze the power fluctuation response characteristics caused by random charging demand. The transfer function method is a frequency domain analysis tool used to describe the dynamic relationship between the input and output of the system. At this stage, the dynamic response model of the charging pile under the disturbance of random charging demand is constructed through the system characteristic parameter set, so as to reveal the response characteristics of the charging pile to the external load disturbance. The dynamic response model is suitable for describing the dynamic characteristic changes of the charging pile in a short time. The steady-state operation model and the dynamic response model are decoupled and calculated, and the independent characteristics of the two in the frequency domain are extracted respectively. The frequency domain characteristic equation of the charging pile system is obtained by mapping the steady-state model and the dynamic model to the frequency domain space respectively. The frequency domain characteristic equation is converted into the time domain state equation by inverse Laplace transform to more intuitively describe the dynamic behavior of the system and obtain the small signal mathematical model of the charging pile system. Perform eigenvalue analysis and stability criterion calculation on the small signal mathematical model. By solving the eigenvalue of the state equation, the dynamic stability and response characteristics of the system are judged, and the model parameters are optimized to make it more suitable for actual operation. The stability criterion calculation comprehensively evaluates the stability of the system based on the eigenvalue distribution to ensure the reliability of the model. Combining the results of eigenvalue analysis and stability criterion calculation, a small signal model of the charging pile system is obtained, which includes the rated power parameters of the charging pile, load fluctuation parameters and system response parameters.

[0018] Step 200: Based on the charging pile system small signal model, a low-sensitivity load prediction model and a high-sensitivity load prediction model are trained to generate charging load prediction data; Specifically, the real-time operating data is classified according to the small signal model of the charging pile system. By analyzing the load fluctuation amplitude, the operating data is divided into normal working condition data and peak working condition data. The division process is based on the different sensitivity characteristics of load changes to the dynamic response of the charging pile system. Normal working condition data is used to describe stable load fluctuations, while peak working condition data is used to characterize the characteristics of sudden and drastic load changes. A response surface polynomial function model is constructed based on normal working condition data. By introducing polynomial expressions of linear terms, square terms and cross terms, this model can capture the global trend and mutual relationship of charging load under normal conditions. In the modeling process, the least squares method is used to fit and calculate the polynomial coefficients. The least squares method optimizes the model coefficients by minimizing the sum of squares of the error between the predicted value and the actual value, ensuring that the model can accurately characterize the regularity of load changes under normal working conditions. Through this process, a low-sensitivity load prediction model is generated, which is suitable for predicting load changes over a long time scale. Model evaluation of the low-sensitivity load prediction model is carried out to verify its reliability and accuracy. The cross-validation method is used to calculate the root mean square error and determination coefficient of the model through multiple training and testing of data subsets. The root mean square error is used to evaluate the prediction error range of the model, while the determination coefficient reflects the model's ability to explain data fluctuations. These evaluation indicators ensure that the low-sensitivity model can provide robust results for load forecasting under normal conditions and obtain reliable low-sensitivity prediction results. At the same time, for peak operating condition data, a three-layer neural network structure is constructed to establish a high-sensitivity load forecasting model. The neural network consists of an input layer, a hidden layer, and an output layer. The input layer contains time feature nodes, historical load nodes, and weather factor nodes. These feature variables reflect the main driving factors of peak load changes. The hidden layer uses the Sigmoid activation function, which can introduce nonlinear mapping to capture complex peak load dynamic behaviors, while the output layer corresponds to the predicted load node and directly generates the peak load prediction results. In the data processing stage, the peak operating condition data is divided into training set, validation set, and test set in a ratio of 7:2:1 to ensure that the model has comprehensive and balanced data support in the training, validation, and testing stages. During the training of the neural network, the back propagation algorithm is used to optimize the network weights. The algorithm calculates the error between the predicted value and the actual value, propagates the error back to each layer in the network, and adjusts the weights to minimize the error. During the optimization process of training, the Adam optimizer is used to update the model parameters. The Adam optimizer combines the momentum method and the adaptive learning rate method to accelerate model convergence and avoid falling into local optimality. At the same time, the mean square error is used as the loss function to ensure that the model can better fit the actual peak load data and obtain a high-sensitivity load prediction model. The low-sensitivity prediction results and the high-sensitivity prediction results are weighted and combined to obtain the charging load prediction data.The weighted combination assigns different weights to low-sensitivity forecast results and high-sensitivity forecast results based on the forecast characteristics of the two models. These weights are dynamically adjusted through historical forecast performance to achieve comprehensive coverage of load forecasts with different time scales and sensitivity requirements.

[0019] The conventional operating data of charging piles are analyzed, including multiple variables such as charging pile output power, local user load and charging demand. The strength of the relationship between these variables and the target load is evaluated through correlation analysis to identify the most important variable set for charging load prediction and form a target influencing factor set. The correlation analysis method uses statistical means to ensure that the selected variables are both physically meaningful and can statistically explain the load changes significantly. The basic form of the response surface polynomial function is constructed based on the target influencing factor set. The response surface polynomial function comprehensively describes the direct effect, nonlinear effect and interaction effect between variables through the combination of linear terms, square terms and cross terms. In this process, the linear term coefficient is set for each variable to reflect the basic influence of the variable; the square term coefficient is set to capture the nonlinear characteristics of the variable; and the interaction between the variables is described by the cross term coefficient. These terms are combined to obtain the initial polynomial structure. The coefficients of the initial polynomial structure are optimized. The least squares method is used to compare the difference between the model predicted value and the actual value, minimize the sum of the squares of this difference, and obtain the optimal solution of the coefficient. By constructing the error square sum objective function, the optimization problem is transformed into a linear equation system solution problem. In order to ensure the numerical stability and accuracy of the solution process, matrix decomposition technology is used, and the QR decomposition method is used to process the linear equations. QR decomposition can efficiently decompose the matrix and extract the optimal solution of the coefficients. These optimal solutions directly determine the performance of the polynomial model. After obtaining the optimal solution of the coefficients of the polynomial model, the model is tested for significance to evaluate whether each variable and its interaction terms have an important contribution to the prediction results of the model. If some variables or terms have a small contribution, they are eliminated through the test results, thereby simplifying the model structure and retaining the most critical factors for load forecasting. Through this process, the complexity of the model is effectively reduced, while the generalization ability and computational efficiency of the model are improved. After the significance test is completed, the generated simplified model is subjected to variance analysis to verify its fitting performance. Variance analysis is a method for evaluating the predictive ability of a model. It measures the ability of the model to explain the change of variables by dividing the total change of the data into the regression part and the residual part. The goodness of fit of the model is obtained by calculating the regression sum of squares, the residual sum of squares and the total sum of squares. This indicator reflects the overall adaptation of the model to the data. If the goodness of fit is high, it means that the model is reliable in predictive ability. The response surface polynomial function is modified based on the goodness of fit. In the process of modification, the weights of some parameters in the polynomial are limited by introducing penalty terms to avoid overfitting problems caused by excessive complexity of the model. The modification method includes constraining the weights of higher-order terms or cross terms to simplify the polynomial structure while retaining the main prediction ability of the model. The modified polynomial model achieves a balance between complexity and accuracy and can more effectively adapt to the actual load forecasting needs. Through the above steps, a modified polynomial model is generated, which is a low-sensitivity load forecasting model.

[0020] Step 300: According to the deviation value between the charging load prediction data and the actual load data, the proportional compensation unit and the integral compensation unit are controlled and optimized to obtain dynamic power compensation control parameters; It should be noted that the charging load prediction data and actual load data are obtained in real time, and the difference between the two is calculated to generate the input deviation signal of the power compensation unit, reflecting the dynamic difference between the predicted load and the actual load. Based on the input deviation signal, mathematical models are respectively performed on the proportional compensation unit and the integral compensation unit. For the proportional compensation unit, a first-order transfer function model is established, in which the proportional gain coefficient is a key parameter. The model directly generates the control output by multiplying the input deviation signal with the proportional gain coefficient to achieve a rapid response to the deviation. The main function of the proportional compensation unit is to correct the deviation in time and provide a fast power adjustment capability in a dynamic load environment to ensure that the charging system can adapt to load changes in time. At the same time, for the integral compensation unit, a mathematical model including an integral time constant is established. The model eliminates the steady-state error of the system by accumulating the deviation signal, thereby maintaining the accuracy of the system in long-term operation. The integral time constant is a key parameter of the model, which directly affects the dynamic characteristics of the integral compensation, including the response speed and stability of the system. The overall transfer function of the power compensator is constructed by combining the transfer function models of the integral compensation unit and the proportional compensation unit in parallel. Based on the overall transfer function of the power compensator, the expected closed-loop pole position of the system is set. The pole placement method is used to determine the appropriate closed-loop pole position according to the system performance requirements (such as response speed, overshoot and stability), and the characteristic equation is established based on this. The coefficients of the characteristic equation are determined by the proportional gain coefficient and the integral time constant, and these parameters are optimized to meet the dynamic performance requirements of the system. The characteristic equation is substituted into the Russ-Horwitz stability criterion, and the stability constraints are determined by comparing the coefficients. These constraints define the value range of the proportional gain coefficient and the integral time constant to ensure that the system remains stable during the dynamic adjustment process. Within the determined parameter value range, the proportional gain coefficient is iteratively optimized using the binary search method. The binary search method is an efficient numerical calculation method that quickly finds the optimal proportional gain coefficient that meets the system overshoot requirements by gradually narrowing the search interval. Overshoot is an important indicator for measuring the response performance of the system, and its optimization aims to balance the rapidity and stability of the system. Based on the optimal proportional gain coefficient, the integral time constant is scanned and calculated. By gradually adjusting the value of the integral time constant, comparing the system response time under different parameter combinations, selecting the parameter combination that can achieve the shortest response time, and obtaining the dynamic power compensation control parameters, including the optimal proportional gain coefficient and integral time constant.

[0021] Step 400: Based on the dynamic power compensation control parameters, the power distribution ratio of the charging pile is genetically optimized and multi-level power distribution is performed to obtain a multi-level power distribution scheme including a conventional load distribution mode and a peak load distribution mode; Specifically, according to the operating characteristics and load requirements of the charging pile system, the dynamic power compensation control parameters are divided into conventional load compensation parameters and peak load compensation parameters to form a classified compensation parameter set. The classification process is based on the compensation requirements of the charging pile under different operating conditions. The conventional load compensation parameters correspond to the adjustment requirements of the system under stable load, while the peak load compensation parameters are for the dynamic response requirements of sudden load changes. The optimization objective function of the charging pile power distribution is constructed based on the classified compensation parameter set. The optimization objective function contains two core parts, namely the system power loss term and the load fluctuation term. Among them, the power loss term is used to minimize the energy consumption during the operation of the charging pile, and the load fluctuation term aims to reduce the fluctuation effect caused by the unbalanced load during the power distribution process. By combining these two objective items, the optimization objective function of the charging pile power distribution is formed. The power distribution ratio of the charging pile is encoded and designed. The distribution ratio of each charging pile is encoded as a gene fragment to form a chromosome encoding format. This encoding method regards the power distribution of each charging pile as an independent gene, so that the power distribution state of the entire system is represented by chromosomes. After completing the encoding design, the key parameters of the genetic algorithm, including population size, crossover probability and mutation probability, are set according to the chromosome encoding format to form a genetic algorithm optimization model. In the genetic optimization stage, the optimization objective function of the charging pile power distribution is input into the genetic algorithm optimization model, and the iterative optimization process begins by generating the initial population. High-quality individuals are selected from the initial population as the parent population. These individuals have high fitness and can better meet the requirements of the objective function. The parent population is simulated by binary crossover operation to achieve gene recombination and generate a new generation of individuals. At the same time, combined with polynomial mutation operation, random changes are introduced to increase the diversity of the population and avoid falling into the local optimal solution. The process of crossover and mutation is continuously iterated, and the optimal power allocation scheme is gradually approached through multiple generations of evolution. While iterative optimization, the power allocation ratio of each charging pile is constrained. Ensure that the allocation ratio meets the rated capacity constraint and power balance constraint of the charging pile. The rated capacity constraint ensures that the allocated power of each charging pile does not exceed its physical limit, while the power balance constraint ensures that the overall power demand and supply of the system are in a dynamic balance state. Through constraint processing, individuals that do not meet the conditions are eliminated to form a feasible solution set. For the feasible solution set, the advantages and disadvantages are sorted to select the optimal solution and generate a multi-level power allocation scheme. The sorting process is evaluated based on the value of the optimization objective function, and solutions with low power loss and low load fluctuation are preferred as high-quality solutions. The final output multi-level power distribution solution includes a conventional load distribution mode and a peak load distribution mode. Among them, the conventional load distribution mode is mainly used for the power distribution strategy of the system under stable load, and has the characteristics of stability and efficiency; while the peak load distribution mode is designed for sudden load changes, and can quickly adjust the power distribution ratio to meet the dynamic needs of high-load periods.

[0022] Step 500: According to the multi-level power allocation scheme, power limitation is performed by reducing the charging power of non-priority users, and load transfer is performed by coordinating the power of adjacent charging piles to obtain an emergency power adjustment control instruction.

[0023] Specifically, based on the multi-level power allocation scheme, the charging time and charging demand of the user are scored and calculated. The charging time score is determined by analyzing the user's cumulative charging time and current charging status, while the charging demand score is calculated based on the remaining charging capacity and target charging amount of the user's device. In order to combine the influence of these two scores, the user priority index is generated by linear weighted combination to reflect the priority demand of the user for charging resources. The power allocation scheme is graded according to the user priority index. Power restrictions are imposed on low-priority users in order from high to low priority. By calculating the power restriction threshold, the charging power of low-priority users is gradually reduced according to the set level to form a power restriction instruction. The reduction process adopts a step-by-step reduction method to dynamically adjust the power output according to the user's priority and charging demand to ensure that the charging demand of high-priority users is met first, while avoiding the system power allocation exceeding the total load capacity. While limiting the power, the restricted load is transferred. A load transfer cost function is established based on the physical distance and remaining capacity between charging piles. The cost function comprehensively considers the transfer distance, load capacity and matching degree of user demand of adjacent charging piles to generate a load transfer scheme. By optimizing this scheme, the dynamic balance of the internal load of the system is achieved, so that the low-load charging piles can share the pressure of the high-load charging piles and improve the overall operation efficiency of the system. According to the load transfer scheme, the piecewise linear interpolation method is used to calculate the power distribution ratio between adjacent charging piles. The linear interpolation method ensures the stability and continuity of the load transfer process by gradually adjusting the power distribution of adjacent charging piles. At the same time, in order to ensure the accuracy of power transfer, the transfer power value is optimized through closed-loop iterative calculation, and specific power coordination instructions are generated, including the target power adjustment amount and execution order of each charging pile, to ensure that the system can respond efficiently under dynamic load conditions. After the power coordination instruction is generated, the execution timing is planned to avoid the impact of large-scale power adjustment on the system operation. In the planning process, the buffer time and smoothing coefficient of power adjustment are set, and the smooth transition of charging pile power adjustment is achieved through buffering and progressive control of the power change process. At the same time, the planned power adjustment execution sequence is sampled and quantized according to the control cycle, and the continuous power limit and transfer power values ​​are converted into digital quantities to generate a control quantity sequence that can be directly used for control execution. After the control quantity sequence is generated, in order to ensure the effectiveness of the emergency power regulation control instruction, the overload trigger condition and the recovery release condition are set. The overload trigger condition is used to determine whether the system needs to start emergency power regulation, while the recovery release condition is used to determine when the system can exit the emergency state and return to the normal operation mode. Through the logical judgment mechanism, these conditions are monitored and judged in real time, and finally an emergency power regulation control instruction with a clear status is generated.

[0024] In the embodiments of the present application, a small signal model of the charging pile system is established, and the operating characteristics of the charging pile are accurately described in combination with the system identification method, thereby overcoming the limitations of traditional modeling methods in large-scale charging pile systems; a dual-model prediction strategy combining the response surface method and the neural network is adopted to perform load prediction for normal operating conditions and peak operating conditions respectively, which significantly improves the accuracy of load prediction; a dynamic power compensator is designed based on the pole configuration method, and the influence of load prediction deviation on system stability is effectively suppressed by optimizing the proportional-integral control parameters; a genetic algorithm is used to perform multi-objective optimization of the charging pile power distribution, and load fluctuations are minimized while ensuring minimum system loss; an emergency power adjustment mechanism based on user priority is designed, and the system overload problem during peak charging periods is solved by combining power limitation and load transfer; the present application adopts a closed-loop optimization mechanism, and through online parameter optimization and dynamic adjustment of control strategies, the stable operation of the charging pile system under various operating conditions is ensured.

[0025] In a specific embodiment, the process of executing step 100 may specifically include the following steps: Collect data on the output power of the charging pile, local user load data, and charging demand data to obtain real-time operation data of the charging pile, and preprocess the real-time operation data of the charging pile to obtain a preprocessed operation data sequence; The pre-processed operating data sequence is input into the recursive least squares algorithm model to identify and calculate the rated power coefficient and power regulation coefficient of the charging pile to obtain the system characteristic parameter set; Based on the system characteristic parameter set, the state space model is used to model and analyze the steady-state operation characteristics of the charging pile under rated conditions to obtain a steady-state operation model; According to the system characteristic parameter set, the transfer function method is used to model and analyze the power fluctuation response characteristics caused by random charging demand, and a dynamic response model is obtained; Decouple the steady-state operation model and the dynamic response model to obtain the frequency domain characteristic equation of the charging pile system, and then convert the frequency domain characteristic equation into a time domain state equation through inverse Laplace transform to obtain the small signal mathematical model of the charging pile system. The eigenvalue analysis and stability criterion calculation of the small signal mathematical model of the charging pile system are performed to obtain the small signal model of the charging pile system including the rated power parameters of the charging pile, the load fluctuation parameters and the system response parameters.

[0026] Specifically, the real-time operation data of the charging pile is collected, including the output power of the charging pile , local user load data and the charging needs of users , forming the original operation data set of the charging pile system. The real-time operation data of the charging pile is preprocessed, including outlier removal, interpolation completion, and filtering noise reduction. Outlier removal eliminates measurement errors by identifying data points that are beyond a reasonable range (such as excessively high instantaneous power values); interpolation completion is used to repair missing data caused by acquisition delays or transmission interruptions; filtering noise reduction uses a low-pass filter to remove high-frequency noise to obtain a smooth operation data sequence. Assume that the acquisition frequency is , the preprocessed data is represented as a discrete time series ,in is the time index, is the preprocessed power data. The preprocessed operating data sequence is input into the recursive least squares (RLS) algorithm model for parameter identification. The recursive least squares algorithm is a dynamic parameter estimation method used to identify the system characteristic parameters of the charging pile in real time, including the rated power factor and power regulation factor The RLS algorithm updates the model parameters by minimizing the following objective function: ; in, is the objective function, It is The output power of is the input data vector (such as voltage, current, load, etc.), is the parameter vector to be identified (including and ), is the forgetting factor, which is used to control the weight of historical data. Through recursive updating, the system characteristic parameter set is obtained Based on the identified characteristic parameter set, the state space model is used to model and analyze the rated working conditions of the charging pile. The basic form of the state space model is: ; ; in, is the system state vector, is the input vector (such as load fluctuation), is the output vector (i.e., power response), is the state space matrix. By analyzing the steady-state data of the system, we can determine and The matrix structure of is used to construct a steady-state operation model to describe the stable operation characteristics of the system under rated load. At the same time, according to the characteristic parameter set, the transfer function method is used to model the power fluctuation response characteristics caused by random charging demand. The form of the transfer function is: ; in, is the transfer function of the power fluctuation, and are the Laplace transforms of the output and input, respectively, is a time constant. By constructing a transfer function model, the dynamic response behavior of the system under random load disturbance is revealed, and a dynamic response model is formed. After obtaining the steady-state operation model and the dynamic response model, the two are decoupled and calculated to obtain the frequency domain characteristic equation of the charging pile system. By mapping the steady-state model and the dynamic model to the frequency domain space respectively, a comprehensive frequency domain characteristic equation is obtained: ; in, represents the overall frequency domain characteristics of the system. To facilitate time domain analysis, the inverse Laplace transform is used to Convert it into a time domain state equation to form a time domain model that describes the small signal characteristics of the charging pile system. Perform eigenvalue analysis and stability criterion calculation on the small signal mathematical model. The goal of eigenvalue analysis is to solve the state matrix The eigenvalues ​​of are used to determine whether the dynamic behavior of the system is stable. If the real parts of all eigenvalues ​​are less than zero, the system is asymptotically stable; otherwise, the system is at risk of oscillation or instability. The stability of the model is further verified by using stability criteria (such as root locus analysis or gain margin evaluation), and a small signal model of the charging pile system is obtained, which includes the rated power parameters of the charging pile, the load fluctuation parameters, and the system response parameters.

[0027] In a specific embodiment, the process of executing step 200 may specifically include the following steps: Based on the small signal model of the charging pile system, the operating conditions are classified and the operating data is divided into normal operating condition data and peak operating condition data according to the load fluctuation amplitude; A response surface polynomial function model is constructed based on conventional operating condition data. The response surface polynomial function model includes linear terms, square terms and cross terms. The least square method is used to fit and calculate the polynomial coefficients to obtain a low-sensitivity load prediction model. The cross-validation method is used to evaluate the low-sensitivity load prediction model, and the model accuracy is verified by calculating the root mean square error and the determination coefficient to obtain the low-sensitivity prediction results. A three-layer neural network structure is constructed based on the peak operating condition data. The three-layer neural network structure includes an input layer, a hidden layer, and an output layer. The input layer contains time feature nodes, historical load nodes, and weather factor nodes. The hidden layer uses a Sigmoid activation function, and the output layer is a predicted load node. The peak operating condition data is divided into training set, validation set and test set in a ratio of 7:2:1. The back propagation algorithm is used to optimize the neural network weights and obtain a high-sensitivity load prediction model. The Adam optimizer is used to update the parameters of the high-sensitivity load forecasting model, and the mean square error function is used as the loss function to obtain the high-sensitivity forecasting results; The low-sensitivity prediction results and the high-sensitivity prediction results are weighted combined to obtain the charging load prediction data.

[0028] Specifically, based on the small signal model of the charging pile system, the operating data is analyzed and the data is divided into normal working condition data and peak working condition data based on the load fluctuation amplitude. The load fluctuation amplitude is calculated by calculating the standard deviation of the power change. To measure, It is The power value at a time point, is the average value of power, is the total number of time points. Below a certain threshold, the data is classified as normal; When the threshold is exceeded, the data is classified as peak condition. For normal condition data, a response surface polynomial function model is constructed to predict low-sensitivity load changes. The response surface model takes the following form: ; in, is the load forecast value, is the input feature variable (such as time, temperature, number of users, etc.), is a constant term, , and are the coefficients of linear, square and cross terms respectively. The coefficients of the model are fitted using the least squares method to minimize the actual load With forecast load The sum of squared errors between: ; By the objective function The optimal solution of the model parameters is obtained to form a low-sensitivity load forecasting model. After the model is built, the cross-validation method is used to evaluate the low-sensitivity load forecasting model. The data is divided into Each time, select one subset as the test set and the remaining subsets as the training set, and repeat the training and testing times, and finally calculate the root mean square error : ; And the coefficient of determination : ; in, is the average value of the observed value. These indicators verify the prediction accuracy and reliability of the model and generate low-sensitivity prediction results. For peak operating data, a three-layer neural network is used for modeling. The network structure includes input layer, hidden layer and output layer. The input layer contains time feature nodes , Historical load nodes and weather factor nodes , these nodes reflect the main factors affecting the peak load; the hidden layer uses the Sigmoid activation function, which is defined as: ; The output layer contains only one prediction node, which is used to generate load prediction values. The peak operating condition data is divided into training set, validation set and test set in a ratio of 7:2:1. The training set is used to optimize the network weights, the validation set is used to prevent overfitting, and the test set is used to evaluate the generalization performance of the model. During the training phase, the back propagation algorithm is used to update the weights and biases of the neural network. Back propagation adjusts the parameters by calculating the gradient of the loss function. The loss function uses the mean square error (MSE): ; In order to improve training efficiency and stability, the Adam optimizer is used to update the model parameters. The Adam optimizer combines the momentum method and the adaptive learning rate method to dynamically adjust the parameter update step size and accelerate convergence. After multiple iterations of training, a high-sensitivity load forecasting model is finally formed and the forecast results are generated. The low-sensitivity forecast results and the high-sensitivity forecast results are weighted and combined to obtain the final charging load forecast data. The formula for weighted combination is: ; in, and is the weight coefficient, satisfying .

[0029] In a specific embodiment, the execution step constructs a response surface polynomial function model based on conventional operating condition data, the response surface polynomial function model includes linear terms, square terms and cross terms, and the least squares method is used to fit the polynomial coefficients to obtain a low-sensitivity load prediction model. The process may specifically include the following steps: Based on the normal working condition data, the correlation analysis of the charging pile output power, local user load and charging demand is carried out to obtain the target influencing factor set; The basic form of the response surface polynomial function is constructed based on the target influencing factor set, and the linear term coefficient, square term coefficient and cross term coefficient are set for each influencing factor to obtain the initial structure of the polynomial; Substitute the initial structure of the polynomial into the least squares calculation formula, construct the error square sum objective function, obtain the coefficient equation group to be solved, and perform matrix decomposition operation on the coefficient equation group, use QR decomposition method to solve the linear equation group, and obtain the optimal solution of the polynomial coefficients; According to the optimal solution of the polynomial coefficients, the significance test of the response surface polynomial function is carried out to obtain the simplified polynomial model, and the variance analysis is carried out on the simplified polynomial model to calculate the regression sum of squares, residual sum of squares and total sum of squares to obtain the goodness of fit of the model; The response surface polynomial function is modified based on the goodness of fit, and the complexity of the model is controlled by introducing a penalty term to obtain the modified polynomial function, which is then used as a low-sensitivity load prediction model.

[0030] Specifically, the normal working condition data of the charging pile system is collated and analyzed, including the output power of the charging pile , Local User Load and charging needs These variables directly reflect the characteristics of load changes during the operation of the charging pile. By calculating the correlation coefficients between these variables, their correlation with the target load (i.e. the variables to be predicted) is evaluated, so as to screen out the most influential variables and form a target influencing factor set. Correlation analysis quantifies the linear correlation between variables by calculating the Pearson correlation coefficient. For two variables and , the calculation formula of its correlation coefficient is: ; in, and Respectively The variable value of the sample point, and They are and The mean of is the total number of samples. Correlation coefficient The value range is [-1, 1], The closer it is to 1 or -1, the stronger the correlation. , and The correlation with the target load determines whether these variables have a significant impact on model construction and screens out the target influencing factor set. Based on the target influencing factor set, the basic form of the response surface polynomial function model is constructed. The response surface model captures the relationship between the input variables and the target load through a polynomial function, and the specific form is: ; in, represents the target load forecast value, It is Influencing factor variables, is a constant term, is the linear term coefficient, is the square term coefficient, is the cross-term coefficient, is the number of variables. The model contains linear effects, nonlinear effects (square terms) and interaction effects (cross terms), which can fully describe the impact of influencing factors on the target load. Substitute the above polynomial initial structure into the least squares calculation formula to construct the error square sum objective function. The objective function is in the form of: ; in, is the actual observed value, is the model prediction value, is the total number of samples. In order to minimize , find the optimal solution for the polynomial coefficients. This optimization problem is transformed into the form of a system of linear equations: ; in, is the design matrix, which contains all input variables and their combinations. is the coefficient vector to be solved, is the observation vector. The QR decomposition method is used to solve the linear equations. By Decompose into an orthogonal matrix and the upper triangular matrix , calculate , and obtain the optimal solution of the polynomial coefficients. After obtaining the optimal solution, the significance test of the response surface polynomial function is performed to screen out the variables and items that have an important impact on the target load, and at the same time, the redundant items that contribute less to the model are eliminated to simplify the model structure. By testing each coefficient -Statistics and -value, determine its significance level. -If the value is greater than the preset threshold (such as 0.05), the coefficient is considered insignificant and the corresponding variable or term is removed from the model. ANOVA is performed on the simplified model to evaluate the goodness of fit of the model. ANOVA calculates the coefficient of determination of the model by decomposing the total sum of squares (SST) into the regression sum of squares (SSR) and the residual sum of squares (SSE): ; Among them, SST reflects the overall variability of observations, SSR reflects the variability explained by the model, and SSE reflects the variability not explained by the model. The value range of is [0,1]. The closer it is to 1, the better the model fits the data. Based on the results of variance analysis, the response surface polynomial function is modified and a penalty term is introduced to control the complexity of the model. For example, Lasso regression is used to add -Regularization term: ; in, is the regularization coefficient, which is used to balance the model complexity and fitting accuracy. , simplify the model structure and improve the generalization ability at the same time, obtain the modified polynomial function, and use the modified polynomial function as the low-sensitivity load forecasting model.

[0031] In a specific embodiment, the process of executing step 300 may specifically include the following steps: Perform real-time difference calculation on charging load prediction data and actual load data to obtain input deviation signal of power compensation unit; Based on the input deviation signal, mathematical modeling is performed on the proportional compensation unit, a first-order transfer function including a proportional gain coefficient is constructed, and a transfer function model of the proportional compensation link is obtained; According to the input deviation signal, mathematical modeling is performed on the integral compensation unit, a first-order differential equation including an integral time constant is constructed, and a transfer function model of the integral compensation link is obtained; The transfer function model of the proportional compensation link and the transfer function model of the integral compensation link are combined in parallel to obtain the overall transfer function of the power compensator; Based on the overall transfer function of the power compensator, the expected closed-loop pole position of the system is set, and the characteristic equation is established by the pole placement method to obtain the parameter optimization target equation. The parameter optimization target equation is substituted into the Russ-Horwitz stability criterion, and the stability constraint conditions are determined by coefficient comparison to obtain the parameter value range. According to the parameter value range, the proportional gain coefficient is iteratively optimized by using the binary search method to control the system overshoot within the set range and obtain the optimal proportional gain coefficient. Based on the optimal proportional gain coefficient, the integral time constant is scanned and calculated, and the parameter combination with the shortest system response time is selected to obtain the dynamic power compensation control parameters.

[0032] Specifically, by obtaining the charging load prediction data and the actual load data, the real-time difference between the two is calculated to form the input deviation signal of the power compensation unit, which is expressed by the formula: ; in, It's time The deviation signal, is the actual load power, is the predicted load power. Deviation signal Directly reflects the difference between the actual operating status of the charging pile and the prediction model. According to the input deviation signal, the proportional compensation unit is mathematically modeled and a proportional gain coefficient is established. The proportional compensation unit quickly responds to system errors by simply linearly amplifying the deviation signal, and its mathematical expression is: ; in, is the Laplace transform of the scale-compensated output, is the Laplace transform of the error signal. By introducing the proportional gain coefficient , which can adjust the response strength of the compensation unit. At the same time, the integral compensation unit is modeled and a time constant including the integral The integral compensation unit eliminates the steady-state error by accumulating the deviation signal, and its Laplace domain expression is: ; in, is the integral compensation output, is the integral time constant, which determines the dynamic characteristics of the integrator. Integral compensation is used to compensate for long-term deviations to ensure that the system can accurately reach the set value. The transfer functions of the proportional compensation unit and the integral compensation unit are combined in parallel to obtain the overall transfer function of the power compensator: ; This transfer function combines the rapid response capability of proportional control and the precise compensation capability of integral control, and can effectively adjust the dynamic performance of the charging pile system. After constructing the overall transfer function, the desired closed-loop pole position of the system is set, and the characteristic equation is established through the pole configuration method. The characteristic equation reflects the dynamic characteristics of the system, and its form is: ; in, is the feedback path transfer function of the system. Substituting in, we get: ; By adjusting and , change the position of the closed-loop pole to meet the dynamic performance requirements of the system. In order to ensure the stability of the system, the characteristic equation is substituted into the Russ-Horwitz stability criterion to analyze whether its coefficient distribution meets the stability conditions. This criterion defines the proportional gain coefficient by comparing the coefficients of the characteristic equation. and the integration time constant For example, for the second-order system , need to meet and After determining the parameter range, the binary method is used to Iterative optimization is performed to control the overshoot of the system within the set range. is the peak deviation in the system response and is given by: ; Continuously adjust through dichotomy , find The optimal value that meets the requirements. After that, Perform a scan calculation to determine the parameter combination that can achieve the shortest response time and obtain the dynamic power compensation control parameters. is the time it takes for the system to reach a steady state from its initial state, and its magnitude is related to Directly related.

[0033] In a specific embodiment, the process of executing step 400 may specifically include the following steps: Classifying the dynamic power compensation control parameters, dividing the dynamic power compensation control parameters into conventional load compensation parameters and peak load compensation parameters, and obtaining a classified compensation parameter set; Based on the classified compensation parameter set, the objective function of the system power loss term and the load fluctuation term is constructed to obtain the optimization objective function of the charging pile power distribution; The power distribution ratio of the charging piles is coded and designed, and the distribution ratio of each charging pile is encoded as a gene fragment to obtain a chromosome coding format. According to the chromosome coding format, the genetic algorithm parameters are set to obtain a genetic algorithm optimization model. The optimization objective function of the charging pile power distribution is input into the genetic algorithm optimization model, and high-quality individuals are selected to obtain the parent population. The parent population is simulated with binary crossover operations and polynomial mutation operations to obtain an iterative optimization sequence. According to the iterative optimization sequence, the power allocation ratio of each charging pile is constrained so that the power allocation ratio meets the rated capacity and power balance constraints of the charging pile, and a feasible solution set is obtained. The feasible solution set is sorted by merit to obtain a multi-level power allocation scheme including conventional load distribution mode and peak load distribution mode.

[0034] Specifically, the dynamic power compensation control parameters are classified, and the control parameters are divided into conventional load compensation parameters and peak load compensation parameters according to different load conditions. Conventional load compensation parameters are used to handle the power distribution of the system under stable load conditions, while peak load compensation parameters are mainly used to deal with dynamic demands of sudden increase or decrease of load. By classifying the parameters, a classified compensation parameter set is formed, which is recorded as and , respectively represent the normal and peak compensation parameters. Based on the classified compensation parameter set, the optimization objective function of charging pile power allocation is constructed. The objective function needs to comprehensively consider the two aspects of system power loss and load fluctuation. The power loss term is used to minimize the transmission and conversion loss of electric energy, and its expression is: ; in, It is The output power of each charging station, is the efficiency coefficient of the charging pile, is the total number of charging posts. The load fluctuation term is used to reduce the unbalanced load distribution between charging posts and avoid local overload of the system. Its expression is: ; in, is the average power output of all charging piles. Combining these two objectives, we get the overall objective function: ; in, and is the weight coefficient, which is used to balance the influence of power loss and load fluctuation. The power allocation ratio of the charging pile is coded and designed, and the power allocation ratio of each charging pile is represented as a gene fragment. Assume that the total power of the charging pile is , the allocation ratio of each charging column is ,satisfy By encoding the distribution ratio into gene fragments, a chromosome encoding format is formed: ; To achieve optimization, set the key parameters of the genetic algorithm, including the population size , crossover probability and mutation probability . These parameters determine the algorithm's search efficiency and result quality, and are combined with chromosome coding to form a genetic algorithm optimization model. In the optimization stage, the constructed objective function is input into the genetic algorithm optimization model, starting from the randomly generated initial population, and the optimal allocation scheme is found through iterative optimization. According to the value of the objective function, high-quality individuals are selected as the parent population. These individuals have high fitness and can provide excellent genes for subsequent crossover and mutation. Use simulated binary crossover (SBX) to perform genetic recombination on the parent population to generate new individuals. The basic formula of SBX is: ; in, is the coefficient that controls the crossover range, and is the gene value of the two parent individuals. Through the crossover operation, a diverse group of offspring individuals is generated. Polynomial mutation is performed to adjust the gene value of the individual by introducing a small range of random changes to improve the diversity of the population. After each iteration, the power allocation ratio of the charging pile is constrained according to the generated optimization sequence. The constraints include the non-negativity of the power allocation ratio, the rated capacity limit of the charging pile, and the total power balance constraint. Specifically, it must meet the following requirements: ; in, It is The rated power of each charging pile. Through constraint processing, solutions that do not meet the conditions are eliminated to form a feasible solution set. The feasible solution set is sorted by merit, and the solution with the highest fitness is selected as the output according to the objective function value to obtain a multi-level power allocation scheme. The scheme includes a conventional load allocation mode and a peak load allocation mode, which are respectively suitable for stable and fluctuating load conditions.

[0035] In a specific embodiment, the process of executing step 500 may specifically include the following steps: Based on the multi-level power allocation scheme, the user's charging time and charging demand are scored and calculated, and the charging time score and the charging demand score are linearly weighted combined to obtain the user priority index; The power allocation scheme is graded according to the user priority index, and the charging power of users is limited from high to low priority to obtain the power limit threshold. Based on the power limit threshold, the charging power of non-priority users is adjusted in grades, and the charging power is limited by the step-by-step reduction method to obtain the power limit instruction. The load transfer calculation involved in the power limit instruction is performed, and the load transfer cost function is established based on the physical distance between the charging piles and the remaining capacity to obtain the load transfer plan; According to the load transfer plan, the piecewise linear interpolation method is used to calculate the power distribution ratio between adjacent charging piles, and the transfer power value is determined through closed-loop iterative calculation to obtain the power coordination instruction; Perform execution timing planning for power coordination instructions, set the buffer time and smoothing coefficient of power regulation, obtain the power regulation execution sequence, sample and quantize the power regulation execution sequence according to the control cycle, perform digital conversion on the power limit and transfer power values, and obtain the control quantity sequence; Based on the control quantity sequence, the overload trigger condition and the recovery release condition are set, and the effective state of the control instruction is determined through logical judgment to obtain the emergency power regulation control instruction.

[0036] Specifically, the user's charging time and charging demand are scored and calculated respectively. Assuming that the user's actual charging time is , the maximum charging time is , charging time rating The calculation formula is: ; in, Indicates the proportion of time the user has spent charging. Charging Demand Score Based on the user's current charging needs and maximum demand Calculation, the formula is: ; These two scores reflect the user's time priority and demand priority respectively. The charging time score and charging demand score are linearly weighted to obtain the user priority index. The calculation formula is: ; in, and is the weight coefficient, satisfying , used to balance the importance of charging time and demand. By setting appropriate weights, long-time charging users or high-demand users are given priority. Priority Index The larger the value of , the higher the priority of the user. After calculating the user priority index, the power allocation scheme is graded according to the priority index. The users are sorted from high to low according to the priority index, and the power limit threshold is set according to the classification standard. Assume that the total system power is , the allocation rule of the limit threshold is: ; in, is assigned to The power limit threshold for each user, is the total number of users. For non-priority users, their charging power is adjusted in stages by a step-by-step reduction method. For example, according to a 10% reduction ratio, the power adjusted each time is , until the power meets the restriction condition, and finally generates a power restriction instruction. After the power restriction instruction is generated, the transfer plan is calculated for the load of the restricted user. The load transfer is optimized based on the physical distance and remaining capacity between the charging piles, and the transfer cost function is expressed as: ; in, It is Users to The physical distance between charging stations, is the power transferred to the pile, It is a charging station The remaining capacity of the charging piles is . By optimizing the transfer cost function, a reasonable load transfer plan is generated. Based on the load transfer plan, the piecewise linear interpolation method is used to calculate the power distribution ratio between adjacent charging piles. Assume that the power range between two charging piles is , the allocation ratio is expressed as: ; Adjust through iteration The value of is combined with the closed-loop control algorithm to optimize the transfer power value and finally generate the power coordination instruction. The execution timing of the power coordination instruction is planned to smooth the power adjustment process and avoid impact on the system. The planning includes setting the buffer time of power adjustment and smoothing coefficient , the transition is achieved by gradually adjusting the power. The formula for generating the power adjustment sequence is: ; in, It is The regulated power of the time step is is the target power value. After the sequence is sampled and quantized during the control period, the power limit and transfer power values ​​are converted into digital signals to form a control quantity sequence. Based on the control quantity sequence, the overload trigger condition and the recovery release condition are set, and an effective emergency power regulation control instruction is generated through logical judgment. The overload trigger condition is defined as: ; The conditions for recovery are: ; When the trigger condition is met, the control instruction enters the effective state; when the release condition is met, the instruction becomes invalid. This logic is used to achieve dynamic adjustment and real-time control.

[0037] The above describes the method for dynamic power allocation of charging piles based on load prediction in the embodiment of the present application. The following describes the dynamic power allocation device 10 for charging piles based on load prediction in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a charging pile dynamic power distribution device 10 based on load prediction includes: Identification module 11, used to perform system identification analysis on the real-time operation data of the charging pile and build a small signal model of the charging pile system; A training module 12 is used to train a low-sensitivity load prediction model and a high-sensitivity load prediction model based on a small signal model of a charging pile system, and generate charging load prediction data; The calculation module 13 is used to perform control optimization calculation on the proportional compensation unit and the integral compensation unit according to the deviation value between the charging load prediction data and the actual load data to obtain the dynamic power compensation control parameter; The distribution module 14 is used to perform genetic optimization and multi-level power distribution on the power distribution ratio of the charging pile based on the dynamic power compensation control parameters, and obtain a multi-level power distribution scheme including a conventional load distribution mode and a peak load distribution mode; The processing module 15 is used to perform power limitation by reducing the charging power of non-priority users according to the multi-level power allocation scheme, and to transfer the load by coordinating the power of adjacent charging piles to obtain an emergency power adjustment control instruction.

[0038] Through the coordinated cooperation of the above-mentioned components, by establishing a small signal model of the charging pile system, and combining the system identification method to accurately describe the operating characteristics of the charging pile, the limitations of the traditional modeling method in large-scale charging pile systems are overcome; a dual-model prediction strategy combining the response surface method and the neural network is adopted to perform load prediction for normal working conditions and peak working conditions respectively, which significantly improves the accuracy of load prediction; a dynamic power compensator is designed based on the pole configuration method, and the influence of load prediction deviation on system stability is effectively suppressed by optimizing the proportional integral control parameters; a genetic algorithm is used to perform multi-objective optimization of the charging pile power distribution, and load fluctuations are minimized while ensuring the minimum system loss; an emergency power adjustment mechanism based on user priority is designed, and the system overload problem during peak charging is solved by combining power limitation and load transfer; this application adopts a closed-loop optimization mechanism, and through online parameter optimization and dynamic adjustment of control strategies, the stable operation of the charging pile system under various working conditions is ensured.

[0039] See also Figure 3 , Figure 3 This is a schematic block diagram of the structure of an electronic device 300 provided in an embodiment of the present application. The electronic device 300 includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected via a device bus 303, wherein the memory 302 may include a non-volatile storage medium and an internal memory.

[0040] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 301, the processor 301 can execute any of the above-mentioned charging pile dynamic power allocation methods based on load prediction.

[0041] The processor 301 is used to provide computing and control capabilities to support the operation of the entire electronic device 300 .

[0042] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 301, the processor 301 can execute any of the above-mentioned dynamic power allocation methods for charging piles based on load prediction.

[0043] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the present application scheme, and does not constitute a limitation on the electronic device 300 involved in the present application scheme. The specific electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0044] It should be understood that the processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0045] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the electronic device 300 described above can refer to the corresponding process of the aforementioned charging pile dynamic power allocation method based on load prediction, and will not be repeated here.

[0046] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the dynamic power allocation method for charging piles based on load prediction as provided in an embodiment of the present application.

[0047] The computer-readable storage medium may be an internal storage unit of the electronic device 300 in the aforementioned embodiment, such as a hard disk or memory of the electronic device 300. The computer-readable storage medium may also be an external storage device of the electronic device 300, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the electronic device 300.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0050] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for dynamic power allocation of charging piles based on load prediction, characterized in that: The method comprises: Conduct system identification and analysis on the real-time operation data of charging piles and build a small signal model of the charging pile system; Based on the charging pile system small signal model, a low-sensitivity load prediction model and a high-sensitivity load prediction model are trained to generate charging load prediction data; According to the deviation value between the charging load prediction data and the actual load data, a control optimization calculation is performed on the proportional compensation unit and the integral compensation unit to obtain a dynamic power compensation control parameter; Based on the dynamic power compensation control parameters, the power distribution ratio of the charging pile is genetically optimized and multi-level power distribution is performed to obtain a multi-level power distribution scheme including a conventional load distribution mode and a peak load distribution mode; According to the multi-level power allocation scheme, power limitation is performed by reducing the charging power of non-priority users, and load transfer is performed by coordinating the power of adjacent charging piles to obtain an emergency power adjustment control instruction.

2. The method for dynamic power allocation of charging piles based on load prediction according to claim 1 is characterized in that: The system identification and analysis of the real-time operation data of the charging pile is performed to construct a small signal model of the charging pile system, including: Collecting data on the charging pile output power, local user load data, and charging demand data to obtain real-time operation data of the charging pile, and preprocessing the real-time operation data of the charging pile to obtain a preprocessed operation data sequence; Input the preprocessed operation data sequence into the recursive least squares algorithm model, perform identification calculation on the rated power coefficient and the power regulation coefficient of the charging pile, and obtain a system characteristic parameter set; Based on the system characteristic parameter set, a state space model is used to model and analyze the steady-state operation characteristics of the charging pile under rated conditions to obtain a steady-state operation model; Based on the system characteristic parameter set, a transfer function method is used to model and analyze the power fluctuation response characteristics caused by random charging demand to obtain a dynamic response model; Decoupling the steady-state operation model and the dynamic response model to obtain a frequency domain characteristic equation of the charging pile system, and converting the frequency domain characteristic equation into a time domain state equation by inverse Laplace transform to obtain a small signal mathematical model of the charging pile system; The small signal mathematical model of the charging pile system is subjected to characteristic value analysis and stability criterion calculation to obtain a small signal model of the charging pile system including charging pile rated power parameters, load fluctuation parameters and system response parameters.

3. The method for dynamic power allocation of charging piles based on load prediction according to claim 2 is characterized in that: The low-sensitivity load prediction model and the high-sensitivity load prediction model are trained based on the small signal model of the charging pile system, and charging load prediction data are generated, including: Based on the small signal model of the charging pile system, the operating conditions are classified, and the operating data is divided into normal operating condition data and peak operating condition data according to the load fluctuation amplitude; A response surface polynomial function model is constructed based on the conventional operating condition data, wherein the response surface polynomial function model includes linear terms, square terms and cross terms, and the polynomial coefficients are fitted and calculated using the least squares method to obtain a low-sensitivity load prediction model; The low-sensitivity load prediction model is evaluated by a cross-validation method, and the model accuracy is verified by calculating the root mean square error and the determination coefficient to obtain a low-sensitivity prediction result; A three-layer neural network structure is constructed according to the peak operating condition data, wherein the three-layer neural network structure includes an input layer, a hidden layer and an output layer, wherein the input layer includes a time feature node, a historical load node and a weather factor node, the hidden layer uses a Sigmoid activation function, and the output layer is a predicted load node; The peak operating condition data is divided into a training set, a validation set and a test set in a ratio of 7:2:1, and a back propagation algorithm is used to optimize the neural network weights to obtain a high-sensitivity load prediction model; The Adam optimizer is used to update the parameters of the high-sensitivity load forecasting model, and the mean square error function is selected as the loss function to obtain a high-sensitivity forecasting result; The low-sensitivity prediction result and the high-sensitivity prediction result are weighted combined to obtain charging load prediction data.

4. The method for dynamic power allocation of charging piles based on load prediction according to claim 3 is characterized in that: The response surface polynomial function model is constructed based on the conventional operating condition data. The response surface polynomial function model includes linear terms, square terms and cross terms. The polynomial coefficients are fitted and calculated using the least squares method to obtain a low-sensitivity load prediction model, including: Based on the conventional working condition data, a correlation analysis is performed on the output power of the charging pile, the local user load and the charging demand to obtain a target influencing factor set; Constructing a basic form of a response surface polynomial function based on the target influencing factor set, setting a linear term coefficient, a square term coefficient and a cross term coefficient for each influencing factor, and obtaining an initial polynomial structure; Substituting the initial structure of the polynomial into the least squares calculation formula, constructing the error square sum objective function, obtaining the coefficient equation group to be solved, and performing matrix decomposition operation on the coefficient equation group, using QR decomposition method to solve the linear equation group, and obtaining the optimal solution of the polynomial coefficients; According to the optimal solution of the polynomial coefficients, a significance test is performed on the response surface polynomial function to obtain a simplified polynomial model, and variance analysis is performed on the simplified polynomial model to calculate the regression sum of squares, the residual sum of squares and the total sum of squares to obtain the goodness of fit of the model; The response surface polynomial function is modified based on the goodness of fit, and the complexity of the model is controlled by introducing a penalty term to obtain a modified polynomial function, and the modified polynomial function is used as a low-sensitivity load prediction model.

5. The method for dynamic power allocation of charging piles based on load prediction according to claim 4 is characterized in that: The control optimization calculation is performed on the proportional compensation unit and the integral compensation unit according to the deviation value between the charging load prediction data and the actual load data to obtain the dynamic power compensation control parameters, including: Performing real-time difference calculation on the charging load prediction data and the actual load data to obtain an input deviation signal of the power compensation unit; Based on the input deviation signal, mathematical modeling is performed on the proportional compensation unit, a first-order transfer function including a proportional gain coefficient is constructed, and a transfer function model of the proportional compensation link is obtained; According to the input deviation signal, mathematical modeling is performed on the integral compensation unit, a first-order differential equation including an integral time constant is constructed, and a transfer function model of the integral compensation link is obtained; The transfer function model of the proportional compensation link and the transfer function model of the integral compensation link are combined in parallel to obtain an overall transfer function of the power compensator; Based on the overall transfer function of the power compensator, the expected closed-loop pole position of the system is set, the characteristic equation is established by the pole configuration method, and the parameter optimization target equation is obtained. The parameter optimization target equation is substituted into the Russ-Horwitz stability criterion, the stability constraint condition is determined by coefficient comparison, and the parameter value range is obtained; According to the parameter value range, the proportional gain coefficient is iteratively optimized using the dichotomy method to control the system overshoot within the set range, and the optimal proportional gain coefficient is obtained. Based on the optimal proportional gain coefficient, the integral time constant is scanned and calculated, and the parameter combination with the shortest system response time is selected to obtain the dynamic power compensation control parameter.

6. The method for dynamic power allocation of charging piles based on load prediction according to claim 5 is characterized in that: Based on the dynamic power compensation control parameters, the power distribution ratio of the charging pile is genetically optimized and multi-level power distribution is performed to obtain a multi-level power distribution scheme including a conventional load distribution mode and a peak load distribution mode, including: Classifying the dynamic power compensation control parameters, dividing the dynamic power compensation control parameters into conventional load compensation parameters and peak load compensation parameters, and obtaining a classified compensation parameter set; Based on the classified compensation parameter set, the objective function of the system power loss term and the load fluctuation term is constructed to obtain the optimization objective function of the charging pile power distribution; The power distribution ratio of the charging piles is coded and designed, and the distribution ratio of each charging pile is encoded as a gene fragment to obtain a chromosome coding format, and according to the chromosome coding format, genetic algorithm parameters are set to obtain a genetic algorithm optimization model; Inputting the optimization objective function of the charging pile power distribution into the genetic algorithm optimization model, selecting high-quality individuals to obtain a parent population, and performing a simulated binary crossover operation and a polynomial mutation operation on the parent population to obtain an iterative optimization sequence; According to the iterative optimization sequence, the power allocation ratio of each charging pile is constrained so that the power allocation ratio satisfies the rated capacity of the charging pile and the power balance constraint, a feasible solution set is obtained, and the feasible solution set is sorted by merit to obtain a multi-level power allocation scheme including a conventional load distribution mode and a peak load distribution mode.

7. The method for dynamic power allocation of charging piles based on load prediction according to claim 6 is characterized in that: According to the multi-level power allocation scheme, power limitation is performed by reducing the charging power of non-priority users, and load transfer is performed by coordinating the power of adjacent charging piles to obtain an emergency power adjustment control instruction, including: Based on the multi-level power allocation scheme, the charging time and charging demand of the user are scored and calculated, and the charging time score and the charging demand score are linearly weighted combined to obtain a user priority index; The power allocation scheme is graded according to the user priority index, and the charging power of users is limited from high to low priority to obtain a power limit threshold, and the charging power of non-priority users is adjusted in grades based on the power limit threshold, and the charging power is limited by a step-by-step reduction method to obtain a power limit instruction; Performing transfer calculation on the load involved in the power limit instruction, establishing a load transfer cost function based on the physical distance between charging piles and the remaining capacity, and obtaining a load transfer plan; According to the load transfer scheme, the power distribution ratio between adjacent charging piles is calculated by piecewise linear interpolation method, and the transfer power value is determined by closed-loop iterative operation to obtain the power coordination instruction; Performing execution timing planning for the power coordination instruction, setting a buffer time and a smoothing coefficient for power regulation, obtaining a power regulation execution sequence, and sampling and quantizing the power regulation execution sequence according to a control period, performing digital quantity conversion on a power limit amount and a transfer power value, and obtaining a control amount sequence; Based on the control quantity sequence, an overload trigger condition and a recovery release condition are set, and the effective state of the control instruction is determined through logical judgment to obtain an emergency power regulation control instruction.

8. A charging pile dynamic power distribution device based on load prediction, characterized in that: Used to execute the charging pile dynamic power allocation method based on load prediction according to any one of claims 1 to 7, the charging pile dynamic power allocation device based on load prediction comprises: Identification module, used to perform system identification analysis on the real-time operation data of the charging pile and build a small signal model of the charging pile system; A training module, used for training a low-sensitivity load prediction model and a high-sensitivity load prediction model based on the charging pile system small signal model, and generating charging load prediction data; A calculation module, used to perform control optimization calculation on a proportional compensation unit and an integral compensation unit according to a deviation value between the charging load prediction data and the actual load data, so as to obtain a dynamic power compensation control parameter; A distribution module, used to perform genetic optimization and multi-level power distribution on the power distribution ratio of the charging pile based on the dynamic power compensation control parameters, and obtain a multi-level power distribution scheme including a conventional load distribution mode and a peak load distribution mode; The processing module is used to perform power limitation by reducing the charging power of non-priority users according to the multi-level power allocation scheme, and to transfer the load by coordinating the power of adjacent charging piles to obtain an emergency power regulation control instruction.

9. An electronic device, characterized in that: The electronic device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes the dynamic power allocation method for charging piles based on load prediction as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, a dynamic power allocation method for charging piles based on load prediction is implemented as described in any one of claims 1-7.

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