Dynamic reward method and system for charging pile service chain participant

Abstract

The invention provides a dynamic rewarding method and system for a charging pile service chain participant, and belongs to the technical field of charging operation management.The dynamic rewarding method comprises the steps that the total harmonic distortion degree of a grid-connected point and inverter switch state data are synchronously obtained, and after short-time Fourier transform and state space matrix construction are conducted, a dynamic rewarding result is obtained; and a compensation harmonic component actively emitted by the inverter and an inherent harmonic component of the power grid are accurately separated by using wavelet packet decomposition. A purification contribution degree is generated by analyzing a phase amplitude relationship between the two, the purification contribution degree is input into a self-adaptive PID neural network model to calculate a service performance score, and finally a dynamic reward value is generated according to score matching price steps. The method can accurately quantify the actual technical contribution of the charging pile participating in the power grid harmonic treatment, achieves the fair and dynamic reward distribution based on the active filtering performance, and motivates an operator to improve the power quality treatment level.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of charging operation management, and particularly relates to a dynamic reward method and system for a charging pile service chain participant. BACKGROUND

[0002] With the rapid development of the new energy vehicle industry, large-scale access of charging infrastructure to the power grid has put forward higher requirements for power quality, making vehicle-grid interaction and power auxiliary services become the focus of industry development. By establishing an effective dynamic reward mechanism, the charging pile service chain participant can be guided to actively utilize the equipment capacity to participate in grid regulation, which has broad application prospects for improving the stability of grid operation and promoting energy consumption.

[0003] The reward method for the charging pile service chain in the prior art mainly relies on construction subsidies or simple metering settlement based on charging power and active power response quantity. Although some schemes introduce a demand response mechanism, they usually only perform rough income distribution according to the completion degree of dispatching instructions or the peak-valley price difference, and focus on simply load peak shaving and valley filling.

[0004] However, the existing method often lacks the ability to finely evaluate the effect of power quality governance, and it is difficult to accurately identify and separate whether the harmonics at the grid connection point are caused by the inherent background of the grid side or actively compensated or polluted by the charging equipment. The lack of this evaluation dimension leads to the inability to accurately quantify the actual technical contribution of the operator in auxiliary services such as active filtering, making it difficult to achieve fair and effective incentives. Therefore, the prior art has the technical problem of insufficient dynamic reward due to the inability to accurately reward based on actual power quality governance contribution. SUMMARY

[0005] The purpose of the present application is to provide a dynamic reward method and system for a charging pile service chain participant to solve the problem of insufficient dynamic reward in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a dynamic reward method for a charging pile service chain participant, comprising:

[0007] synchronously acquiring the total harmonic distortion of the charging station and the grid connection point of the public power grid, and the pulse width modulation switching frequency and duty cycle state of the inverter inside the charging pile power module;

[0008] performing short-time Fourier transform on the total harmonic distortion to generate a harmonic feature map, and constructing a switching state space matrix according to the pulse width modulation switching frequency and the duty cycle state;

[0009] Based on the harmonic feature spectrum and the switching state space matrix, the first harmonic component and the second harmonic component are extracted by wavelet packet decomposition, the first harmonic component is a harmonic compensation component actively emitted by the charging pile inverter, and the second harmonic component is an inherent harmonic component of the public power grid;

[0010] By comparing the phase superposition and amplitude of the first harmonic component and the second harmonic component, the contribution degree is generated;

[0011] The contribution degree is input as a feedforward variable into a neural network model based on adaptive PID control, and a preset power quality parameter is used as a target function to calculate a service performance score, which represents the performance level of the charging pile operator participating in the active filtering auxiliary service;

[0012] Based on the service performance score, a target price ladder is determined in a preset performance score and price ladder correspondence relationship, and a target dynamic reward value is generated based on the service performance score and the target price ladder.

[0013] Optionally, the method further comprises:

[0014] The phase and amplitude of the voltage fundamental wave of the public power grid grid-connected point are synchronously acquired, and the phase angle and amplitude modulation ratio of the inverter output voltage fundamental wave are extracted from the switching state space matrix;

[0015] The phase difference between the phase and the phase angle is calculated, and the reactive power compensation amount is calculated based on the phase difference, the amplitude modulation ratio and the amplitude;

[0016] By comparing the phase superposition and amplitude of the first harmonic component and the second harmonic component, the contribution degree is generated, including:

[0017] By comparing the phase superposition and amplitude of the first harmonic component and the second harmonic component, the initial contribution degree is generated, and the contribution degree is determined based on the initial contribution degree and the reactive power compensation amount.

[0018] Optionally, by comparing the phase superposition and amplitude of the first harmonic component and the second harmonic component, the initial contribution degree is generated, and the contribution degree is determined based on the initial contribution degree and the reactive power compensation amount, including:

[0019] The phase difference value and the amplitude ratio value of the first harmonic component and the second harmonic component are calculated, and when the phase difference value is in a preset difference interval and the amplitude ratio value is in a preset compensation interval, the amplitude ratio value is taken as the initial contribution degree;

[0020] The deviation ratio between the reactive power compensation amount and the preset reactive power demand value is calculated, and the reactive power contribution degree is obtained by weighting the deviation ratio using a preset reactive power weight coefficient;

[0021] The initial contribution degree and the reactive power contribution degree are weighted and summed to obtain the contribution degree.

[0022] Optionally, before inputting the contribution degree as a feedforward variable into the neural network model based on adaptive PID control, the method further comprises:

[0023] calculating a real-time phase difference between the first harmonic component and the second harmonic component;

[0024] generating a safety phase margin by calculating an absolute difference value between the real-time phase difference and a preset critical phase value;

[0025] nonlinearly weighting and adjusting the contribution degree using the safety phase margin.

[0026] Optionally, a switching state space matrix is constructed according to the pulse width modulation switching frequency and the duty cycle state, comprising:

[0027] determining the number of pulse cycles per unit time based on the pulse width modulation switching frequency, and extracting the conduction time sequence within each pulse cycle number according to the duty cycle state;

[0028] mapping the conduction time sequence into a discrete state sequence, and continuously intercepting the discrete state sequence using a preset sliding window to obtain a plurality of local state vectors;

[0029] stacking and processing the plurality of local state vectors in time sequence to construct the switching state space matrix.

[0030] Optionally, based on the harmonic feature map and the switching state space matrix, the first harmonic component and the second harmonic component are extracted using wavelet packet decomposition, comprising:

[0031] extracting the frequency spectrum distribution feature by performing frequency domain transformation on the switching state space matrix;

[0032] decomposing the harmonic feature map into a plurality of sub-band sequences of independent frequency bands, and calculating the correlation coefficient of each sub-band sequence and the frequency spectrum distribution feature using covariance operation;

[0033] determining the sub-band sequence with a correlation coefficient greater than a preset correlation threshold as the first sub-band, and determining the sub-band sequence with a correlation coefficient less than or equal to the preset correlation threshold as the second sub-band;

[0034] obtaining the first harmonic component and the second harmonic component by respectively performing wavelet packet inverse transform reconstruction on the first sub-band and the second sub-band.

[0035] Optionally, the contribution degree is input as a feedforward variable into the neural network model based on adaptive PID control, and a service performance score is calculated using a preset power quality parameter as a target function, comprising:

[0036] The contribution degree is input as a feedforward variable into a neural network model based on adaptive PID control, and a preset power quality parameter is set as a target reference trajectory;

[0037] An error between the feedforward variable and the target reference trajectory is calculated by using a back propagation algorithm, and weights of the neural network model are updated by using the error, so as to obtain a convergence residual value and a weight change rate;

[0038] The convergence residual value is weighted by using a preset accuracy weight to obtain a first performance score, and the weight change rate is weighted by using a preset speed weight to obtain a second performance score, and the service performance score is obtained by weighted summation of the first performance score and the second performance score.

[0039] In a second aspect, the application provides a dynamic reward system for a charging pile service chain participant, comprising:

[0040] The acquisition module is configured to synchronously acquire a total harmonic distortion of a charging station and a public power grid interconnection point, and a pulse width modulation switch frequency and a duty cycle state of an inverter inside a charging pile power module;

[0041] The generation module is configured to generate a harmonic feature spectrum by performing short-time Fourier transform on the total harmonic distortion, and construct a switch state space matrix according to the pulse width modulation switch frequency and the duty cycle state;

[0042] The extraction module is configured to extract a first harmonic component and a second harmonic component by using wavelet packet decomposition based on the harmonic feature spectrum and the switch state space matrix, the first harmonic component being a harmonic compensation component actively emitted by the charging pile inverter, and the second harmonic component being an inherent harmonic component of the public power grid;

[0043] The generation module is further configured to generate the contribution degree by performing phase superposition and amplitude comparison on the first harmonic component and the second harmonic component;

[0044] The calculation module is configured to input the contribution degree as a feedforward variable into a neural network model based on adaptive PID control, and calculate a service performance score by taking a preset power quality parameter as a target function, the service performance score representing a performance level of a charging pile operator participating in active filtering auxiliary service;

[0045] The generation module is further configured to determine a target price ladder in a preset performance score-price ladder correspondence relationship based on the service performance score, and generate a target dynamic reward value based on the service performance score and the target price ladder.

[0046] In a third aspect, the application provides an electronic device, comprising:

[0047] The memory is configured to store a computer program;

[0048] The processor is configured to implement the steps of the dynamic reward method of the charging pile service chain participant according to the first aspect.

[0049] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is configured to implement the steps of the dynamic reward method of the charging pile service chain participant according to the first aspect when executed by a processor.

[0050] The dynamic reward method of the charging pile service chain participant provided by the present application first realizes source end decoupling and component separation of complex harmonic signals at the grid connection point by combining the inverter switch state on the device side with the harmonic distortion characteristics on the grid side, and using wavelet packet decomposition technology, so as to accurately identify the compensation component actively emitted by the charging device and the inherent component of the grid.

[0051] Secondly, based on this accurate signal separation result, the actual contribution of the device is quantified by phase superposition analysis, and the adaptive PID neural network model is used to dynamically and intelligently score the performance, so as to convert the abstract technical index into a measurable service performance, effectively solve the problem that the existing technology cannot distinguish the harmonic source and is difficult to quantify the governance effect, and ensure the scientificity and fairness of the reward distribution, thereby significantly improving the enthusiasm of the operator to participate in the power quality governance of the grid. Therefore, the present application realizes accurate and efficient dynamic reward based on the actual power quality governance contribution of the device.

[0052] Further, the present application analyzes and calculates the reactive power compensation amount of the charging pile at the fundamental wave level by deeply mining the fundamental wave control information in the switch state space matrix, and evaluates the reactive power contribution and the harmonic governance contribution. This design breaks through the limitation of single dimension only focusing on harmonic governance, fully utilizes the inverter capacity of the charging device, and realizes comprehensive quantification of the dual auxiliary service value of "purifying the grid" and "supporting voltage".

[0053] This multi-dimensional contribution calculation method not only more comprehensively reflects the actual regulation effect of the charging pile service chain participant on the grid, but also encourages the operator to consider reactive power balance while governing harmonics, thereby further optimizing the overall power quality and stability of the grid. Therefore, the present application effectively expands the evaluation dimension of the dynamic reward, and improves the comprehensiveness and systematicness of the auxiliary service incentive mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0055] Figure 1 A flowchart of a dynamic reward method for a charging pile service chain participant provided by an embodiment of the present application is shown in the figure.

[0056] Figure 2 A flowchart of a harmonic component generation method provided by an embodiment of the present application is shown in the figure.

[0057] Figure 3 A flowchart of a service performance score generation method provided by an embodiment of the present application is shown in the figure.

[0058] Figure 4 A structural diagram of a dynamic reward system for a charging pile service chain participant provided by an embodiment of the present application is shown in the figure.

[0059] Figure 5 A hardware structure diagram of an electronic device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0060] In the dynamic reward mechanism of the charging pile service chain, the prior art generally has the key defects of single evaluation dimension and insufficient refinement: the current incentive mode is mostly based on the simple metering of electric quantity or power, it is difficult to distinguish and attribute the responsibility of power quality fluctuation of the grid point, and it is impossible to accurately identify whether the harmonic is from the inherent background of the grid or the operation of the charging device. The evaluation status of this "black box" leads to the inability to effectively quantify the technical contribution of the operator to actively participate in harmonic control, making it difficult for the participants who really invest resources to improve the quality of the grid to obtain matching benefits.

[0061] In view of the above problems, the present application provides a dynamic reward method for a charging pile service chain participant, which is characterized by constructing a data fusion and analysis mechanism across the grid side and the device side. First, the method synchronously collects the harmonic distortion degree of the grid point and the inverter switching state, uses the wavelet packet decomposition technology combined with the time-frequency domain characteristics, and accurately strips out the compensation component actively emitted by the inverter and the inherent component of the grid. Secondly, through the joint evaluation of phase superposition analysis and adaptive PID neural network model, the actual purification contribution of the device to the grid is dynamically quantified and the performance score is generated, and finally the price ladder is matched to realize accurate reward.

[0062] The application ensures that the reward is strictly linked to the actual technical contribution through the source end decoupling of the harmonic signal and the quantitative calculation of the governance effect, avoids the mis-reward of non-governance behavior, guarantees the reasonable income of the high-quality operator, solves the technical problem of insufficient dynamic reward caused by the inability to accurately and dynamically reward based on the actual power quality governance contribution in the prior art, and improves the enthusiasm and technical efficiency of the charging infrastructure in participating in the power grid auxiliary service.

[0063] In order to enable personnel in the technical field to better understand the scheme of the application, the application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the application.

[0064] In order to solve the problems in the prior art, the embodiments of the application provide a dynamic reward method, device, equipment, computer storage medium and computer program product of a charging pile service chain participant. First, the dynamic reward method of the charging pile service chain participant provided by the embodiments of the application is introduced.

[0065] Figure 1 The flowchart of the dynamic reward method of the charging pile service chain participant provided by an embodiment of the application is shown. As shown in Figure 1 The method comprises:

[0066] S101, synchronously acquiring the total harmonic distortion of the charging station and the public grid interconnection point, and the pulse width modulation switching frequency and duty cycle state of the internal inverter of the charging pile power module.

[0067] The total harmonic distortion refers to the ratio of the effective value of the harmonic component to the effective value of the fundamental component in the voltage waveform of the interconnection point, which is used to quantify the distortion degree of the grid voltage waveform. The pulse width modulation switching frequency refers to the number of turns-on and turns-off of the inverter power device in a unit of time. The duty cycle state refers to the sequence of the ratio of the power device conduction time to the whole cycle in a switching cycle.

[0068] First, a high-precision power quality analysis device is deployed at the interconnection point of the charging station and the public grid. The device acquires the three-phase voltage signal of the grid side in real time at a preset high-frequency sampling rate, and outputs the total harmonic distortion data sequence in real time through the built-in fast calculation module. At the same time, the register data of the internal digital controller of the charging pile is directly read through the data bus, and the pulse width modulation switching frequency value and the real-time duty cycle state sequence of each phase bridge arm being executed by the inverter power module at the corresponding moment are synchronously captured. Finally, the two groups of cross-domain data are time-stamped aligned to form a synchronous data set including the grid side quality characteristics and the equipment side control characteristics.

[0069] For example, assuming that the power quality analysis device collects total harmonic distortion data H = [0.03, 0.032, 0.028] at the point of common coupling of the A charging station at a certain moment, which respectively corresponds to the distortion of the three-phase voltage. At the same time, the pulse width modulation switching frequency of the inverter at the current moment is obtained through the charging pile master control unit F = 10000, and the duty cycle state sequence of the A-phase bridge arm in the next three cycles is D = [0.45, 0.48, 0.52].

[0070] S102, generate a harmonic feature map by short-time Fourier transform of the total harmonic distortion, and construct a switching state space matrix according to the pulse width modulation switching frequency and the duty cycle state.

[0071] The harmonic feature map refers to a data matrix that displays the energy distribution characteristics of the total harmonic distortion in the joint two-dimensional plane of time and frequency, which can include a set of time-frequency pixel points reflecting the amplitude intensity of each harmonic at different times. The switching state space matrix refers to a structured numerical matrix formed by mapping the discrete switching action sequence of the inverter into a multi-dimensional state space, which is used to represent the logical action trajectory of the inverter in the continuous time window.

[0072] First, a time window function with a fixed length is set, which slides on the time axis with a preset step size to intercept local fragments in the total harmonic distortion data sequence. Then, the fast Fourier transform is performed on each intercepted local fragment to calculate the frequency spectrum distribution of the signal in the time window, and the frequency amplitude vector corresponding to the time is obtained. Finally, by arranging and splicing the frequency amplitude vectors calculated by all sliding windows in time sequence on the time axis, a three-dimensional data body including time dimension, frequency dimension and amplitude dimension is generated, which is the harmonic feature map.

[0073] For example, for the input data H, the transformed data can obtain the map matrix M, where the element M(t, f) represents the harmonic component amplitude at time t and frequency f. Secondly, for the data on the inverter side, the basic time reference is determined according to the obtained pulse width modulation switching frequency, and the on-time information reflected by the duty cycle state is combined to abstract the continuous pulse waveform into a discrete "0-1" level logic sequence, and these sequences are stacked in time sequence to construct a switching state space matrix S reflecting the action logic of the device.

[0074] Optionally, the process of constructing the switching state space matrix according to the pulse width modulation switching frequency and the duty cycle state in step S102 can specifically include:

[0075] S1021, determine the number of pulse periods in a unit time based on the pulse width modulation switching frequency, and extract the conduction time sequence in each pulse period according to the duty cycle state.

[0076] The number of pulse periods refers to the number of complete switching periods completed by the inverter in a selected reference time unit, which reflects the time domain density of switching action, and can be an integer count value calculated based on frequency. The conduction time sequence refers to a set of specific duration values of the power switch device in the on state in each consecutive pulse period.

[0077] First, read the previously obtained pulse width modulation switching frequency value 10kHz, select the unit time as 1 second, then calculate the number of pulse periods as 10000, indicating that there are 10000 independent switching periods in 1 second, and the theoretical duration of each period is 1 / 10000 seconds, that is, 100 microseconds. Next, traverse all the duty cycle state data obtained in this second, for each specific pulse period, use the formula to calculate the conduction time in the period, where is the period duration, is the duty cycle at this time. Finally, arrange all the calculated conduction times in chronological order to form the conduction time sequence.

[0078] For example, assuming that the duty cycles of three consecutive periods are 0.5, 0.6 and 0.4 respectively, and the period duration is 100us, then the calculated conduction time sequence segment is Tseq=[50us, 60us, 40us].

[0079] S1022, map the conduction time sequence to a discrete state sequence, and use a pre-set sliding window to continuously intercept the discrete state sequence to obtain multiple local state vectors.

[0080] The discrete state sequence refers to the high-low state sequence of converting continuous analog time quantity to digital logic level, which is used to simulate the physical opening and closing action of the inverter switch tube, and can be a long binary data stream composed of 0 and 1. The local state vector refers to a short sequence segment with fixed length intercepted from the long sequence, which represents the switching action mode in a small time period, and can be a row vector including hundreds of logic state bits.

[0081] First, set a sampling resolution much higher than the switching frequency, for example, 1us, and expand the on-time sequence obtained in the previous step. Second, for each pulse cycle, mark the state as "1" in the time period corresponding to the on-time, and mark it as "0" in the remaining off-time. For example, for a cycle of 50us on and 50us off, a sequence fragment of 50 ones followed by 50 zeros is generated. Concatenate the fragments of all cycles to obtain the complete discrete state sequence. Finally, set a window length . Slide and intercept on the discrete state sequence with a step size . Each slide outputs the data in the window as a local state vector.

[0082] For example, assuming the first 10 bits of the discrete state sequence are 1111100000, and the window length is set to 5 and the step size is 5, the first local state vector obtained by interception is V1 = [1, 1, 1, 1, 1], and the second is V2 = [0, 0, 0, 0, 0].

[0083] S1023, stack the multiple local state vectors in time sequence to construct a switching state space matrix.

[0084] Stacking refers to combining and arranging multiple vectors according to specific spatial geometric rules to increase the dimension of data or construct a matrix structure. It can include row stacking or column stacking.

[0085] First, arrange the multiple local state vectors generated in the previous step as rows of the matrix in the order of their generation time. Assuming that a total of local state vectors are generated, each with a length of , the first vector is placed in the first row of the matrix, the second vector is placed in the second row, and so on until the vector. Finally, a switching state space matrix of dimensions is constructed. For example, assuming there are three local state vectors V1, V2, and V3, the constructed switching state space matrix S is:

[0086] ;

[0087] This matrix S completely retains all the space-time action characteristics of the inverter in a period of time.

[0088] This embodiment realizes the precise alignment of the grid-side spectral characteristics and the device-side control logic in the time-space dimension by generating the time-frequency spectrum of the total harmonic distortion and constructing the inverter switching state space matrix. In particular, by converting the on-time sequence into discrete state vectors and stacking them, the microscopic time sequence rules of inverter switching actions are completely captured, improving the accuracy of signal tracing.

[0089] S103, based on the harmonic feature map and the switching state space matrix, the first harmonic component and the second harmonic component are extracted by wavelet packet decomposition, the first harmonic component is a harmonic compensation component actively emitted by the charging pile inverter, and the second harmonic component is an inherent harmonic component of the public power grid.

[0090] Optionally, the process of extracting the first harmonic component and the second harmonic component based on the harmonic feature map and the switching state space matrix by wavelet packet decomposition in step S103 can specifically include:

[0091] Figure 2 A flowchart of a method for generating harmonic components is shown. As shown in the figure, the method includes: Figure 2

[0092] S1031, the frequency spectrum distribution characteristics are extracted by performing frequency domain transformation on the switching state space matrix.

[0093] The frequency spectrum distribution characteristics refer to the energy density distribution curve formed after mapping the switching action logic in the time domain to the frequency domain, which is used to represent the inherent emission intensity of the inverter switching action at different frequency points, and can be an amplitude vector including the fundamental frequency, the switching frequency and its multiple frequency components.

[0094] For the switching state space matrix constructed before , a two-dimensional fast Fourier transform or column Fourier transform technology along the time axis is applied. Specifically, first, each column vector in the matrix is regarded as an independent discrete time signal, and the frequency spectrum thereof is calculated. Then, the frequency spectrum results of all columns are weighted and averaged or maximum value envelope extraction is performed, so as to obtain a one-dimensional frequency spectrum vector that can cover the entire frequency range, i.e. the frequency spectrum distribution characteristics.

[0095] For example, assuming that the switching state space matrix of the A charging station is After transformation, the extracted frequency spectrum distribution characteristics are a vector of length 1024 In the vector, assuming that there are significant amplitude peaks at the index positions corresponding to the pulse width modulation switching frequency of 10 kHz and its multiple frequency of 20 kHz, and the amplitudes at other frequency bands are close to zero, which describes the current emission spectrum profile of the device.

[0096] S1032, the harmonic feature map is decomposed into a plurality of independent frequency band sub-band sequences, and the correlation coefficients of each sub-band sequence and the frequency spectrum distribution characteristics are calculated by using covariance operation.

[0097] ​The sub-band sequence refers to a set of narrow-band components divided by frequency from high to low by using signal decomposition technology on the harmonic signal of wideband, which can include a low-frequency profile part and different levels of high-frequency detail parts.

[0098] First, the previously generated harmonic feature map is processed by a wavelet packet decomposition algorithm . A wavelet basis function suitable for power signal analysis is selected, and the decomposition level is set to . The harmonic feature map is decomposed into non-overlapping sub-band sequences. Then, each sub-band sequence is transformed into the frequency domain, and compared with the frequency spectrum distribution feature extracted in step S1031 in the corresponding frequency interval. Finally, the matching degree of each sub-band sequence and the inverter switching action feature, i.e. the correlation coefficient, is obtained by calculating the covariance of the two.

[0099] For example, assume that the harmonic feature map is decomposed into sub-band sequences to . Among them, the frequency band covers the range around 10 kHz, and the correlation coefficient between it and the frequency spectrum distribution feature is 0.85, indicating that the energy of this frequency band highly fits the inverter action feature; while the correlation coefficient of the frequency band covering the low-frequency even harmonic is 0.12, indicating that this frequency band is weakly related to the inverter action.

[0100] S1033, determine the sub-band sequence with a correlation coefficient greater than a preset correlation threshold as the first sub-band, and determine the sub-band sequence with a correlation coefficient less than or equal to the preset correlation threshold as the second sub-band.

[0101] The first sub-band refers to a set of signal components determined to be actively emitted by the charging pile inverter, representing the active influence part of the device on the grid point. The second sub-band refers to a set of signal components determined to be unrelated to the charging pile action, representing the inherent background part of the grid side. The preset correlation threshold refers to the judgment threshold for distinguishing the source of the signal. The preset correlation threshold is set according to the complexity of the grid environment, as shown in the following Table 1:

[0102] Table 1: Preset correlation threshold table

[0103]

[0104] Table 1 lists the recommended threshold settings for different power grid ambient noise levels. In low-noise environments, the threshold is set lower to more sensitively detect the subtle emissions from the equipment; while in high-noise environments, the threshold is set higher to prevent background noise from being misinterpreted as equipment emissions.

[0105] First, by statistically analyzing the energy of the second harmonic component in the characteristic frequency band without active compensation, the background noise level of the current power grid is estimated, thereby determining the environmental noise level of the current grid connection point. Table 1 is then consulted to determine the applicable preset correlation threshold. Next, all correlation coefficients calculated in step S1032 are iterated and compared one by one with the threshold. If the correlation coefficient of a sub-frequency band is greater than the threshold, it is classified into the first sub-frequency band list; otherwise, it is classified into the second sub-frequency band list. For example, assuming the current environmental assessment is Level II, the preset correlation threshold is selected according to Table 1. For the previously calculated The coefficient is 0.85, because ,therefore It was designated as the first sub-band; for The coefficient is 0.12, because ,therefore It was designated as the second sub-band.

[0106] S1034. The first harmonic component and the second harmonic component are obtained by reconstructing the first sub-band and the second sub-band by performing wavelet packet inverse transform on them respectively.

[0107] First, a new wavelet packet coefficient node tree is constructed. All coefficients identified as belonging to the first sub-band retain their original values, while all coefficient nodes corresponding to the second sub-band are forcibly set to zero. Then, the inverse transform algorithm is executed, and the output time-domain waveform represents the first harmonic component. Similarly, the first sub-band coefficients are set to zero, only the second sub-band coefficients are retained, and an inverse transform is performed; the output time-domain waveform represents the second harmonic component.

[0108] For example, utilizing the preserved The first harmonic component is reconstructed from the coefficient tree of the equal correlation frequency band. This component clearly demonstrates the high-frequency ripple caused by the inverter switching action; utilizing the preserved The second harmonic component is reconstructed from the coefficient tree of the low correlation band. This component restores the original low-frequency distortion characteristics of the power grid side. Ultimately, it achieves the decomposition of the mixed total harmonic distortion signal into... and The physical process.

[0109] The embodiment extracts the spectral distribution characteristics of the inverter switch state as a reference fingerprint, combines the multi-resolution characteristics of wavelet packet decomposition, and refines the complex power grid harmonic signal into independent sub-bands and performs correlation screening. The first harmonic component highly consistent with the charging pile switch action and the second harmonic component originating from the power grid background can be accurately identified and separated, effectively overcoming the problem of unclear responsibility attribution under mixed signals.

[0110] S104, generating the contribution degree by phase superposition and amplitude comparison of the first harmonic component and the second harmonic component.

[0111] The contribution degree refers to a comprehensive evaluation index quantitatively representing the positive improvement effect or negative deterioration influence of the charging pile inverter on power grid power quality at a certain moment by numerical means.

[0112] Optionally, the method further comprises:

[0113] The phase and amplitude of the voltage fundamental wave of the public power grid point are synchronously acquired, and the phase angle and amplitude modulation ratio of the inverter output voltage fundamental wave are extracted from the switch state space matrix.

[0114] The amplitude modulation ratio refers to the proportional coefficient between the amplitude of the inverter output fundamental wave voltage and the utilization rate of the DC bus voltage in pulse width modulation control. The inverter output voltage fundamental wave refers to the sinusoidal wave component with the same frequency as the power grid power frequency hidden in the high-frequency pulse sequence of the switch state space matrix.

[0115] Firstly, for the public power grid side, the phase value and amplitude value of the fundamental wave are extracted by using phase-locked loop technology or discrete Fourier transform algorithm to analyze the collected grid connection point voltage signal in real time. Secondly, for the charging pile side, the switch state space matrix constructed in step S102 is subjected to low-pass filtering processing or fundamental wave frequency spectrum analysis, and the phase angle of the equivalent fundamental wave voltage actually output by the inverter at the moment and the corresponding amplitude modulation ratio are inversely solved by extracting the fundamental wave component.

[0116] For example, it is assumed that at a certain moment, the grid voltage fundamental wave amplitude is detected to be 311V and the phase is 0 radian; at the same time, the switch state space matrix is analyzed to extract the fundamental wave phase angle of the inverter output voltage as 0.05 radian and the amplitude modulation ratio as 0.95.

[0117] The phase difference between the phase and the phase angle is calculated, and the reactive power compensation amount is calculated based on the phase difference, the amplitude modulation ratio and the amplitude.

[0118] The phase difference refers to the angle deviation of the fundamental wave of the inverter output voltage and the fundamental wave of the grid voltage on the time axis, which determines the direction and nature of power flow. The reactive power compensation quantity refers to the value of the reactive power injected or absorbed by the charging pile inverter to the grid, which is used to measure the support strength of the device to the voltage stability of the grid point.

[0119] Firstly, the difference between the phase angle of the inverter output voltage fundamental wave and the phase of the grid voltage fundamental wave is calculated by subtraction operation to obtain the phase difference. Then, the equivalent fundamental wave voltage amplitude of the inverter output port is estimated by using the power transmission principle of power system, combining the grid-connected inductance parameters of the charging pile, the DC bus voltage value and the amplitude modulation ratio obtained. Finally, according to the reactive power calculation formula of voltage source parallel operation, the current reactive power compensation quantity is calculated by comprehensively considering the grid voltage amplitude, the equivalent voltage amplitude of the inverter and the phase difference. For example, assuming that the phase difference is positive and small, and the equivalent voltage amplitude of the inverter is higher than the grid voltage amplitude, the calculated reactive power compensation quantity is +5kVar, indicating that the charging pile is sending inductive reactive power to the grid to support the voltage.

[0120] Step S104 generates the contribution degree by comparing the phase superposition and amplitude of the first harmonic component and the second harmonic component, including:

[0121] S1041, the initial contribution degree is generated by comparing the phase superposition and amplitude of the first harmonic component and the second harmonic component, and the contribution degree is determined based on the initial contribution degree and the reactive power compensation quantity.

[0122] The initial contribution degree refers to a single technical performance indicator considering only the harmonic control dimension, which represents the degree of cancellation or suppression of the harmonic compensation component emitted by the charging pile to the inherent harmonic component of the grid.

[0123] Firstly, the waveform relationship of the first harmonic component and the second harmonic component is compared. If the superposition of the two can reduce the overall harmonic amplitude, it is determined as positive contribution and quantified as the initial contribution degree; otherwise, it is determined as negative contribution. Then, the calculated reactive power compensation quantity is taken into account, and the initial contribution degree is comprehensively operated to obtain the final contribution degree which covers the double values of harmonic control and reactive support. For example, assuming that the initial contribution degree is calculated as 0.8, and the device provides 5kVar of reactive power compensation, the final contribution degree is calculated by comprehensively considering the two.

[0124] This embodiment analyzes the reactive power compensation quantity of the charging pile by deeply mining the fundamental wave control information in the switch state space matrix, and evaluates the initial contribution degree of harmonic control and reactive contribution. It breaks through the limitation of single attention to harmonic control and realizes the comprehensive quantification of the double auxiliary service values of harmonic elimination and voltage stabilization of the charging pile.

[0125] Optionally, the step S1041 of generating the initial contribution degree by performing phase superposition and amplitude comparison on the first harmonic component and the second harmonic component, and determining the contribution degree based on the initial contribution degree and the reactive power compensation amount can specifically include:

[0126] calculating a phase difference value and an amplitude ratio value of the first harmonic component and the second harmonic component, and taking the amplitude ratio value as the initial contribution degree when the phase difference value is in a preset difference interval and the amplitude ratio value is in a preset compensation interval.

[0127] The preset difference interval refers to a phase angle range determined to be effective for counteracting. The preset compensation interval refers to an amplitude matching range determined to be effective and safe. The preset difference interval is set according to different power grid management modes, as shown in Table 2 below, and the preset compensation interval is as shown in Table 3 below.

[0128] Table 2: Preset difference interval comparison table

[0129]

[0130] Table 2 defines the angle range that the phase difference value of the first harmonic component and the second harmonic component needs to meet under different power grid management modes. Ideally, the phase difference should be 180 degrees to achieve maximum cancellation, but in actual engineering, a certain deviation is allowed.

[0131] Table 3: Preset compensation interval comparison table

[0132]

[0133] Table 3 defines the reasonable range that the amplitude ratio of the first harmonic component relative to the second harmonic component should be controlled in order to avoid overcompensation or undercompensation.

[0134] First, the phase and amplitude of the first harmonic component and the second harmonic component are extracted, and the phase difference value and the amplitude ratio value are calculated. Then, according to the current set management mode, refer to Table 2, and according to the compensation strategy, refer to Table 3, to obtain the corresponding preset difference interval and preset compensation interval. Finally, it is judged whether the calculated and fall within their respective intervals at the same time. If the condition is met, it means that the current compensation is effective and compliant, and at this time the amplitude ratio value is directly assigned as the initial contribution degree . If the condition is not met, it is further judged: if the phase difference value is located in the [0°, 30°] or [330°, 360°] interval, it is determined to be harmful compensation, and the initial contribution degree is -K; otherwise, it is determined to be invalid compensation, and the initial contribution degree is 0.

[0135] For example, assuming that the precision harmonic elimination mode and the full compensation strategy are set. If the measured phase difference value is 175° and the amplitude ratio value is 0.95, it is determined that the compensation is effective, the initial contribution degree .

[0136] The deviation ratio between the reactive compensation amount and the preset reactive demand value is calculated, and the reactive contribution degree is calculated by weighting the deviation ratio using the preset reactive weight coefficient.

[0137] The preset reactive demand value refers to the theoretical value of the reactive power urgently needed by the power grid at the current moment in order to maintain the grid point voltage within the standard range. The deviation ratio refers to the difference between the actual reactive compensation amount and the reactive demand value. The smaller the deviation, the more accurate the response. The reactive contribution degree refers to the quantitative reactive service performance score. The preset reactive demand value is shown in Table 4 as follows:

[0138] Table 4: Preset reactive demand value comparison table

[0139]

[0140] Table 4 lists the classified demand of the power grid for reactive power according to the deviation of the measured voltage at the grid point from the nominal voltage.

[0141] First, according to the real-time voltage deviation of the grid point, refer to Table 4 to obtain the current preset reactive demand value . Then, the deviation ratio is calculated using the formula , and when the denominator is 0, the full score is directly assigned. Finally, the preset reactive weight coefficient =0.3 is obtained, and the reactive contribution degree is calculated. For example, if the measured voltage is 5% lower, refer to Table 4 to obtain the demand value kVar. Assuming that the actual calculated reactive compensation amount kVar, the deviation ratio is . The calculated reactive contribution degree is .

[0142] The initial contribution degree and the reactive contribution degree are weighted and summed to obtain the contribution degree.

[0143] The initial contribution degree and the reactive contribution degree obtained in the foregoing steps are added to obtain the final contribution degree . For example, combining the foregoing data, .

[0144] The embodiment realizes fine and standardized evaluation of harmonic governance and reactive support, ensures that only high-quality counter-phase cancellation behavior is counted as initial contribution, and effectively eliminates invalid or harmful adjustment. At the same time, the response accuracy of reactive support is accurately quantified, and a comprehensive contribution degree index is generated, which takes into account the purification of power quality and voltage stability support.

[0145] Optionally, before the contribution degree is input as a feedforward variable into the neural network model based on adaptive PID control at step S105, the method further comprises:

[0146] The real-time phase difference between the first harmonic component and the second harmonic component is calculated.

[0147] The real-time phase difference refers to the difference between the instantaneous phase angles of the first harmonic component and the second harmonic component at a specific frequency point at any sampling time.

[0148] Firstly, the time-domain waveforms of the first harmonic component and the second harmonic component are analyzed using Hilbert transform or zero-crossing detection algorithm. Secondly, the analytic signals of the two components are calculated respectively, and the instantaneous phase functions and are extracted. Then, the real-time phase difference sequence is obtained by subtraction operation . For example, assuming that for the 5th harmonic component, at t=0.01s, the compensation wave phase of the charging pile is detected to be 175 degrees, and the grid background wave phase is 0 degrees, the calculated real-time phase difference is 175 degrees.

[0149] The safety phase margin is generated by calculating the absolute difference between the real-time phase difference and the preset critical phase value.

[0150] The preset critical phase value refers to the dangerous phase boundary value that causes parallel resonance or leads to voltage instability, which is usually set to an interval near 90 degrees or 270 degrees. The safety phase margin refers to the degree of deviation of the current real-time phase difference from these dangerous boundaries, which is used to quantify the safety of operation. The larger the value, the farther away from the resonance region, the safer.

[0151] Firstly, the critical phase value is set. Secondly, the absolute difference between the real-time phase difference and the nearest critical phase value is calculated, which is the safety phase margin . For example, assuming that the real-time phase difference is 175 degrees, the distance from the 90-degree boundary is 85 degrees, and the distance from the 270-degree boundary is 95 degrees, the minimum value of 85 degrees is taken as the safety phase margin.

[0152] The contribution degree is nonlinearly weighted and adjusted using the safety phase margin.

[0153] Firstly, a penalty function based on the safety phase margin is constructed The function outputs a coefficient close to 1 when the margin is greater than the safety threshold, and the coefficient rapidly decays to 0 when the margin is less than the threshold. Secondly, the contribution degree obtained in the previous step S104 is adjusted. For example, if the safety phase margin is 85 degrees and the penalty coefficient is 1.0, the contribution degree remains unchanged; if the safety phase margin is only 5 degrees and the penalty coefficient is reduced to 0.1, even if the amplitude cancellation effect is good, the final contribution degree will be greatly reduced, thus punishing such risky adjustment behavior.

[0154] The embodiment realizes automatic identification and punishment of high-risk adjustment behavior, and ensures that only effective management within the safe range away from the resonance critical region can obtain full rewards. Not only does it encourage harmonic elimination, but it also pre-emptively avoids the risk of resonance at the grid connection point.

[0155] S105, input the contribution degree as a feedforward variable into a neural network model based on adaptive PID control, and calculate a service performance score with a preset power quality parameter as a target function, where the service performance score represents the performance level of the charging pile operator participating in the active filtering auxiliary service.

[0156] Optionally, the process of inputting the contribution degree as a feedforward variable into a neural network model based on adaptive PID control in step S105, and calculating a service performance score with a preset power quality parameter as a target function, can specifically include:

[0157] Figure 3 A flowchart of a method for generating a service performance score is shown. As shown in Figure 3 the method includes:

[0158] S1051, input the contribution degree as a feedforward variable into a neural network model based on adaptive PID control, and set a preset power quality parameter as a target reference trajectory.

[0159] The feedforward variable refers to an external input signal directly input into a control system to compensate for interference in advance or guide the system response, which in this case is the quantized contribution degree value. The target reference trajectory refers to a curve or set of set values that changes over time and is expected to reach an ideal state, serving as a standard for the control algorithm to strive to approach. The preset power quality parameter refers to the qualified limit or ideal value of the key index of the grid connection point set according to the national standard or grid operation specification. The preset power quality parameter is shown in the following Table 5:

[0160] Table 5: Comparison table of preset power quality parameters

[0161]

[0162] ​Table 5 lists the target control values of total harmonic distortion and power factor for different voltage levels and grid points. These values are used as the full score standard or zero error benchmark for the neural network model.

[0163] First, the contribution generated in step S104 is formatted into an input vector. Then, according to the voltage level actually accessed by the charging station, the corresponding target reference value vector is extracted from Table 5. The two vectors are connected to the input layer and reference layer of the adaptive PID neural network model, respectively. For example, assuming that the total harmonic distortion calculated by the contribution at the current time is 0.06 and the power factor is 0.92, the input vector is [0.06, 0.92]; the corresponding target reference trajectory is set to the constant vector [0.04, 0.95].

[0164] S1052, the error between the feedforward variable and the target reference trajectory is calculated using the backpropagation algorithm, and the weights of the neural network model are updated using the error to obtain the convergence residual value and the weight change rate.

[0165] The convergence residual value refers to the steady-state deviation between the model output and the target reference trajectory after a certain number of iterations or online adjustments. The weight change rate refers to the update amplitude of the connection weights in the neural network per unit time step.

[0166] First, the error function between the input feedforward variable and the set target is calculated using the backpropagation algorithm. Then, according to the gradient descent principle, the partial derivative of the error with respect to the network weights is calculated, and the weighting coefficients of the proportional, integral, and differential units are updated accordingly. Finally, during the model running process, the error value after each iteration is recorded in real time, and when the error changes tend to be flat, the absolute error at this time is recorded as the convergence residual value; at the same time, the Euclidean distance of the weight vectors of the adjacent two iterations is calculated, and divided by the time step to obtain the weight change rate.

[0167] For example, assuming that after 50 iterations, the total harmonic distortion of the model output is 0.042, compared with the target 0.04, the convergence residual value is In this process, the proportional gain in the PID parameter increases rapidly from 0.5 to 0.8, which can be obtained by calculating the average weight change rate of the ratio of the proportional gain change to the total number of iterations.

[0168] S1053, the first performance score is obtained by weighting the convergence residual value using the preset precision weight, and the second performance score is obtained by weighting the weight change rate using the preset speed weight. The service performance score is obtained by weighting and summing the first performance score and the second performance score.

[0169] The first performance score refers to a precision dimension score calculated based on control steady-state error, and the smaller the residual error, the higher the score. The second performance score refers to a response dimension score calculated based on parameter adjustment rate, and the faster the change rate within a reasonable range, the higher the score.

[0170] First, define the service performance score function, which can use an inverse proportional function or an exponential decay function to map the physical indicators to a score of 0-100. Second, for the convergence residual value, use the formula to calculate the first performance score; for the weight change rate, use the formula to calculate the second performance score. Finally, obtain the preset precision weight 0.6 and the preset speed weight 0.4, and weight-sum the two. For example, assume that the first performance score is calculated to be 80 points, and the second performance score is calculated to be 70 points. The final service performance score is calculated to be 80 points.

[0171] This embodiment quantifies the steady-state accuracy and dynamic response speed of device management respectively. This control evaluation method avoids the one-sidedness of single result orientation and realizes the deep profiling and accurate scoring of the comprehensive ability of the operator's device software and hardware.

[0172] S106, based on the service performance score, determine the target price ladder in the preset performance score and price ladder correspondence, and generate a target dynamic reward value based on the service performance score and the target price ladder.

[0173] The preset performance score and price ladder correspondence refers to a lookup table or piecewise function rule that maps technical evaluation scores to economic incentive standards in advance. The target dynamic reward value refers to the actual economic benefit ultimately settled to the charging pile operator. The preset performance score and price ladder correspondence is shown in Table 6 as follows:

[0174] Table 6: Preset performance score and price ladder table

[0175]

[0176] Table 6 divides the service performance score of 0 to 100 points into several level intervals, and sets a corresponding basic reward unit price and adjustment coefficient for each interval. The higher the score, the higher the unit price, and the greater the gain of the adjustment coefficient, thereby non-linearly amplifying the reward intensity for high-level governance services.

[0177] First, read the service performance score output by step S105. Then, match the score with the performance score interval in Table 6 to determine its level, thereby locking the corresponding target price ladder and adjustment coefficient The final reward can be generated by interpolation calculation or linear mapping formula. The specific calculation formula can be: wherein is the equivalent service capacity provided by the device in the period.

[0178] For example, it is assumed that the service performance score calculated in the previous step is 89. Referring to Table 6, the score falls in the interval [80, 90) and belongs to the A level, and the corresponding basic unit price Yuan / kVarh, the adjustment coefficient is 0.5. It is assumed that the cumulative governance capacity provided by the device in the period is kVarh. Then the target dynamic reward value is calculated as Yuan. Finally, the amount will be directly issued to the operator account as a dynamic reward, completing the closed-loop conversion from technical contribution to economic benefit.

[0179] The embodiment realizes the marketization and refinement of auxiliary service pricing, greatly widens the income gap between high-quality services and ordinary services, avoids egalitarianism, and can effectively guide the charging pile operator to continuously optimize the device control strategy and actively pursue higher governance performance to obtain excess income, thereby effectively promoting the macro improvement of power grid power quality at the microeconomic level.

[0180] Figure 4 FIG. 1 is a structural schematic diagram of a specific embodiment of a dynamic reward system for a charging pile service chain participant provided by the embodiment of the application, referring to Figure 4 The system can include:

[0181] 410 an acquisition module, configured to synchronously acquire the total harmonic distortion of the charging station and the public grid interconnection point, and the pulse width modulation switch frequency and duty cycle state of the internal inverter of the charging pile power module;

[0182] 420 a generation module, configured to generate a harmonic feature map by performing short-time Fourier transform on the total harmonic distortion, and construct a switch state space matrix according to the pulse width modulation switch frequency and the duty cycle state;

[0183] 430 an extraction module, configured to extract a first harmonic component and a second harmonic component by using wavelet packet decomposition based on the harmonic feature map and the switch state space matrix, the first harmonic component being a harmonic compensation component actively emitted by the charging pile inverter, and the second harmonic component being an inherent harmonic component of the public grid;

[0184] 420 the generation module is further configured to generate a contribution degree by performing phase superposition and amplitude comparison on the first harmonic component and the second harmonic component;

[0185] 440 a calculation module, configured to input the contribution degree as a feedforward variable into a neural network model based on adaptive PID control, take a preset power quality parameter as a target function, and calculate a service performance score, the service performance score representing a performance level of the charging pile operator participating in the active filtering auxiliary service;

[0186] 420 the generation module is further configured to determine a target price ladder in a preset correspondence between performance scores and price ladders based on the service performance score, and generate a target dynamic reward value based on the service performance score and the target price ladder.

[0187] The dynamic reward system of the charging pile service chain participant according to the embodiments of the present application is used to implement the dynamic reward method of the charging pile service chain participant as described above, and therefore the specific implementation of the dynamic reward system of the charging pile service chain participant can be seen from the embodiment part of the dynamic reward method of the charging pile service chain participant as described above. The specific implementation can be referred to the description of the corresponding embodiment part, and will not be repeated here.

[0188] Figure 5 A hardware structure schematic diagram of an electronic device provided by an embodiment of the present application is shown.

[0189] The electronic device can include a processor 510 and a memory 520 storing computer program instructions.

[0190] Specifically, the processor 510 described above can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or can be configured as one or more integrated circuits that implement one or more embodiments of the present application.

[0191] The memory 520 can include a mass storage for data or instructions. By way of example and not limitation, the memory 520 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 520 can include removable or non-removable (or fixed) media. Where appropriate, the memory 520 can be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, the memory 520 is a non-volatile solid-state memory.

[0192] The memory can include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software that, when executed (e.g., by one or more processors), is operable to perform the operations described with reference to the method according to the first aspect of the present disclosure.

[0193] The processor 510 implements the dynamic reward method of any one of the charging pile service chain participants in the above-described embodiments by reading and executing computer program instructions stored in the memory 520.

[0194] In one example, the electronic device can further include a communication interface 530 and a bus 540. Wherein, as shown in the figure, the processor 510, the memory 520, the communication interface 530 are connected through the bus 540 and complete the communication between each other. Figure 5

[0195] The communication interface 530 is mainly used to realize the communication between each module, device, unit and / or equipment in the embodiments of the present application.

[0196] The bus 540 includes hardware, software or both to couple components of the online data traffic billing device to each other in a known manner. By way of example, and not limitation, the bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand (IB) interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus or a combination of two or more of these. Where appropriate, the bus 540 can include one or more buses. Although the present application describes and illustrates a particular bus, the present application contemplates any suitable bus or interconnect.

[0197] The electronic device can perform the dynamic reward method of the charging pile service chain participant in the embodiments of the present application, so as to realize the dynamic reward method of the charging pile service chain participant described in combination with the figures.

[0198] ​In addition, in combination with the dynamic reward method of the charging pile service chain participant in the above-mentioned embodiments, the embodiments of the application can provide a computer readable storage medium for implementation. The computer readable storage medium has computer program instructions stored thereon; the computer program instructions are executed by a processor to implement any one of the dynamic reward methods of the charging pile service chain participant in the above-mentioned embodiments.

[0199] It should be noted that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of well-known methods are omitted herein. In the above-mentioned embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order of the steps, after understanding the spirit of the present application.

[0200] The functional blocks shown in the structural block diagrams described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the present application are program or code segments used to perform the required tasks. The program or code segments can be stored in a machine readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. The "machine readable medium" can include any medium capable of storing or transmitting information. Examples of machine readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via a computer network such as the Internet, an intranet, etc.

[0201] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-mentioned steps, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from that in the embodiments, or several steps can be performed simultaneously.

[0202] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0203] The above describes in detail the dynamic reward method and system of the charging pile service chain participant provided by the application. The principles and implementation modes of the application are described by applying specific examples in this paper. The above description of the embodiments is only used to help understand the method of the application and its core idea. It should be pointed out that for ordinary skilled persons in the technical field, some improvements and modifications can be made to the application without departing from the principles of the application, and these improvements and modifications also fall within the protection scope of the application.

Claims

1. A dynamic reward method for a charging pile service chain participant, characterized in that, The method comprises: synchronously acquiring total harmonic distortion of a charging station and a public grid interconnection point, and pulse width modulation switch frequency and duty cycle state of an inverter inside a charging pile power module; generating a harmonic feature spectrum by performing short-time Fourier transform on the total harmonic distortion, and constructing a switch state space matrix according to the pulse width modulation switch frequency and the duty cycle state; based on the harmonic feature spectrum and the switch state space matrix, extracting a first harmonic component and a second harmonic component by using wavelet packet decomposition, the first harmonic component being a harmonic compensation component actively emitted by a charging pile inverter, and the second harmonic component being an inherent harmonic component of the public grid; generating a contribution degree by performing phase superposition and amplitude comparison on the first harmonic component and the second harmonic component; inputting the contribution degree as a feedforward variable into a neural network model based on adaptive PID control, taking a preset power quality parameter as a target function, calculating a service performance score, and the service performance score representing a performance level of a charging pile operator participating in active filtering auxiliary services; based on the service performance score, determining a target price ladder in a preset performance score and price ladder corresponding relationship, and generating a target dynamic reward value based on the service performance score and the target price ladder.

2. The method of claim 1, wherein, The method further comprises: synchronously acquiring a phase and amplitude of a voltage fundamental wave of the public grid interconnection point, and extracting a phase angle and amplitude modulation ratio of an inverter output voltage fundamental wave from the switch state space matrix; calculating a phase difference between the phase and the phase angle, and calculating a reactive power compensation amount based on the phase difference, the amplitude modulation ratio and the amplitude; the generating a contribution degree by performing phase superposition and amplitude comparison on the first harmonic component and the second harmonic component comprises: generating an initial contribution degree by performing phase superposition and amplitude comparison on the first harmonic component and the second harmonic component, and determining the contribution degree based on the initial contribution degree and the reactive power compensation amount.

3. The method of claim 2, wherein, the generating an initial contribution degree by performing phase superposition and amplitude comparison on the first harmonic component and the second harmonic component, and determining the contribution degree based on the initial contribution degree and the reactive power compensation amount comprises: calculating a phase difference value and an amplitude ratio value of the first harmonic component and the second harmonic component, and taking the amplitude ratio value as the initial contribution degree when the phase difference value is in a preset difference interval and the amplitude ratio value is in a preset compensation interval; calculating a deviation ratio between the reactive power compensation amount and a preset reactive power demand value, and calculating a reactive power contribution degree by weighting the deviation ratio by using a preset reactive power weight coefficient; weighting and summing the initial contribution degree and the reactive power contribution degree to obtain the contribution degree.

4. The method of claim 3, wherein, Before the contribution degree is inputted as a feedforward variable into the neural network model based on adaptive PID control, the method further comprises: calculating a real-time phase difference between the first harmonic component and the second harmonic component; generating a safe phase margin by calculating an absolute difference value between the real-time phase difference and a preset critical phase value; The contribution degree is nonlinearly weighted and adjusted by using the safe phase margin.

5. The method of claim 1, wherein, The switch state space matrix is constructed according to the pulse width modulation switch frequency and the duty cycle state, and the method comprises the following steps: The number of pulse cycles in a unit time is determined based on the pulse width modulation switch frequency, and the on-time sequence in each pulse cycle is extracted according to the duty cycle state; The on-time sequence is mapped into a discrete state sequence, and the discrete state sequence is continuously intercepted by using a preset sliding window to obtain a plurality of local state vectors; The plurality of local state vectors are arranged in time sequence and stacked to construct the switch state space matrix.

6. The method of claim 1, wherein, The first harmonic component and the second harmonic component are extracted by wavelet packet decomposition based on the harmonic feature spectrum and the switch state space matrix, and the method comprises the following steps: The frequency spectrum distribution feature is extracted by performing frequency domain transformation on the switch state space matrix; The harmonic feature spectrum is decomposed into a plurality of sub-band sequences of independent frequency bands, and the correlation coefficient of each sub-band sequence and the frequency spectrum distribution feature is calculated by using covariance operation; The sub-band sequence with a correlation coefficient greater than a preset correlation threshold is determined as a first sub-band, and the sub-band sequence with a correlation coefficient less than or equal to the preset correlation threshold is determined as a second sub-band; The first harmonic component and the second harmonic component are obtained by respectively performing wavelet packet inverse transformation reconstruction on the first sub-band and the second sub-band.

7. The method of claim 1, wherein, The contribution degree is input as a feedforward variable into a neural network model based on adaptive PID control, a preset power quality parameter is taken as a target function, and a service performance score is calculated, and the method comprises the following steps: The contribution degree is input as a feedforward variable into the neural network model based on adaptive PID control, and the preset power quality parameter is taken as a target reference trajectory; The error between the feedforward variable and the target reference trajectory is calculated by using a back propagation algorithm, and the weights of the neural network model are updated by using the error to obtain a convergence residual value and a weight change rate; A first performance score is obtained by weighting the convergence residual value by using a preset precision weight, and a second performance score is obtained by weighting the weight change rate by using a preset speed weight, and the service performance score is obtained by weighting and summing the first performance score and the second performance score.

8. A dynamic reward system for charging station service chain participants, characterized in that, The method comprises the following steps: An acquisition module is configured to synchronously acquire the total harmonic distortion of a charging station and a public grid grid-connected point, and the pulse width modulation switch frequency and the duty cycle state of an inverter inside a charging pile power module; A generation module is configured to generate a harmonic feature spectrum by performing short-time Fourier transformation on the total harmonic distortion, and construct a switch state space matrix according to the pulse width modulation switch frequency and the duty cycle state; An extraction module is configured to extract a first harmonic component and a second harmonic component by wavelet packet decomposition based on the harmonic feature spectrum and the switch state space matrix, the first harmonic component is a harmonic compensation component actively emitted by a charging pile inverter, and the second harmonic component is an inherent harmonic component of the public grid. The generating module is further configured to generate a contribution degree by performing phase superposition and amplitude comparison on the first harmonic component and the second harmonic component; The calculating module is configured to input the contribution degree as a feedforward variable into a neural network model based on adaptive PID control, take a preset power quality parameter as a target function, and calculate a service performance score, the service performance score representing a performance level of a charging pile operator participating in active filtering auxiliary service; The generating module is further configured to determine a target price ladder in a preset correspondence between performance scores and price ladders based on the service performance score, and generate a target dynamic reward value based on the service performance score and the target price ladder.

9. An electronic device, comprising: Comprise: A memory for storing a computer program; A processor for executing the computer program to implement the steps of the dynamic reward method of the charging pile service chain participant according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the dynamic reward method of the charging pile service chain participant according to any one of claims 1 to 7.

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