Power regulation method, device, equipment and storage medium for photovoltaic energy storage system

Abstract

The present invention relates to the technical field of photovoltaic energy storage systems, and discloses a power regulation method, device, equipment and storage medium for a photovoltaic energy storage system. Among them, the method includes: collecting and performing wavelet decomposition on the voltage and current parameters of a photovoltaic array in real time to obtain power fluctuation components and power trigger signals at different frequencies; performing power vector analysis to obtain an oscillation mode characteristic parameter set and an oscillation suppression strategy; constructing a photovoltaic side feed-forward trigger channel and a energy storage side feedback trigger channel to obtain a coordinated trigger instruction for the photovoltaic side and the energy storage side; performing multi-objective optimization decomposition and slope limit processing on the coordinated trigger instruction to obtain a voltage reference value for a photovoltaic MPPT controller and a current reference value for a battery charge and discharge controller. This method realizes the accurate identification and prediction of power fluctuation components at different frequencies, realizes the coordinated cooperation between the photovoltaic side feed-forward and the energy storage side feedback, and avoids the power oscillation risk caused by the control mode switching.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage systems, and in particular, to a power regulation method, device, equipment and storage medium for a photovoltaic energy storage system. Background Art

[0002] With the rapid deployment and application of distributed photovoltaic energy storage systems, the problem of power stability has become a severe challenge faced by power systems. Due to factors such as light intensity fluctuations and load changes, the output power of photovoltaic systems often exhibits instability. Such power fluctuations can impact the power grid, reduce the power quality, and affect the stable operation of the power grid. Especially in grid-connected applications, unstable power output may cause power grid frequency fluctuations, trigger misoperation of protection devices, and even lead to the collapse of a local power grid.

[0003] Traditional photovoltaic energy storage systems generally adopt a power balance control method based on DC bus voltage feedback to indirectly achieve power balance by stabilizing the DC bus voltage. However, this method often exhibits power oscillation problems under conditions of rapid changes in light intensity or sudden load changes, and it is difficult to quickly and effectively suppress power fluctuations. Especially when the system encounters sudden changes in light caused by extreme weather conditions or sudden load changes caused by power grid faults, the lag and singularity of traditional control methods will result in untimely power regulation and even cause system instability. Summary of the Invention

[0004] The present invention provides a power regulation method, device, equipment and storage medium for a photovoltaic energy storage system. The present invention realizes the accurate identification and prediction of power fluctuation components with different frequencies, realizes the coordinated cooperation of photovoltaic-side feedforward and energy storage-side feedback, and avoids the risk of power oscillation caused by control mode switching.

[0005] In a first aspect, the present invention provides a power regulation method for a photovoltaic energy storage system, and the power regulation method for the photovoltaic energy storage system includes:

[0006] Real-time collect and perform wavelet decomposition on the voltage and current parameters of a photovoltaic array to obtain power fluctuation components with different frequencies and a power trigger signal;

[0007] Perform power vector analysis according to the power fluctuation components with different frequencies and the power trigger signal to obtain a set of oscillation mode characteristic parameters and an oscillation suppression strategy;

[0008] Construct a photovoltaic-side feedforward trigger channel and an energy storage-side feedback trigger channel based on the set of oscillation mode characteristic parameters and the oscillation suppression strategy to obtain a coordinated trigger instruction for the photovoltaic side and the energy storage side;

[0009] Perform multi-objective optimization decomposition and slope limit processing on the coordinated trigger instruction to obtain the voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller.

[0010] In a second aspect, the present invention provides a power regulation device for a photovoltaic energy storage system, and the power regulation device for the photovoltaic energy storage system includes:

[0011] A real-time acquisition module, configured to perform real-time acquisition and wavelet decomposition on the voltage and current parameters of the photovoltaic array to obtain power fluctuation components and power trigger signals at different frequencies;

[0012] A power vector analysis module, configured to perform power vector analysis according to the power fluctuation components at different frequencies and the power trigger signal to obtain an oscillation mode characteristic parameter set and an oscillation suppression strategy;

[0013] A feedback trigger module, configured to construct a photovoltaic-side feedforward trigger channel and an energy storage-side feedback trigger channel based on the oscillation mode characteristic parameter set and the oscillation suppression strategy to obtain a coordinated trigger instruction for the photovoltaic side and the energy storage side;

[0014] A processing module, configured to perform multi-objective optimization decomposition and slope limit processing on the coordinated trigger instruction to obtain the voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller.

[0015] In a third aspect of the present invention, a power regulation device for a photovoltaic energy storage system is provided, including: 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 to enable the power regulation device for the photovoltaic energy storage system to execute the above-mentioned power regulation method for the photovoltaic energy storage system.

[0016] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is enabled to execute the above-mentioned power regulation method for the photovoltaic energy storage system.

[0017] In the technical solution provided by the present invention, through multi-order polynomial fitting and wavelet decomposition techniques, the precise identification and prediction of power fluctuation components with different frequencies are realized. Based on the quantitative analysis method of the power vector analysis model, the influence mechanism of each frequency component on the system stability is clarified. The design of the dual-channel power trigger mechanism realizes the coordinated cooperation of photovoltaic-side feedforward and energy storage-side feedback, and can effectively suppress power fluctuations without calculating complex power compensation values on the photovoltaic side; the power distribution strategy of multi-objective optimization takes into account both the response speed and the energy utilization efficiency, and realizes the optimal allocation of system resources; the differential processing strategy is adopted for power fluctuations with different frequency characteristics, avoiding the risk of power oscillation caused by control mode switching; the introduction of the system operation state evaluation and control strategy optimization mechanism ensures that the system maintains the best performance state during long-term operation, and significantly improves the power stability and energy utilization efficiency of the photovoltaic energy storage system under extreme conditions such as sharp changes in light or sudden changes in load.

[0018] Other features and advantages of the present invention will be described in the following specification, and partly will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims and drawings.

[0019] To make the above objectives, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0020] Figure 1 It is a schematic diagram of an embodiment of the power regulation method of the photovoltaic energy storage system in the embodiment of the present invention;

[0021] Figure 2 It is a schematic diagram of an embodiment of the power regulation device of the photovoltaic energy storage system in the embodiment of the present invention;

[0022] Figure 3 It is a schematic diagram of an embodiment of the power regulation equipment of the photovoltaic energy storage system in the embodiment of the present invention. Detailed Embodiments

[0023] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0024] As used in the embodiments of the present invention, the terms "comprising", "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device end that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or device ends.

[0025] For ease of understanding of this embodiment, first, a power regulation method for a photovoltaic energy storage system disclosed in the embodiments of the present invention will be introduced in detail. As Figure 1 shown, the method includes the following steps:

[0026] 101. Real-time collect and perform wavelet decomposition on the voltage and current parameters of the photovoltaic array to obtain power fluctuation components and power trigger signals at different frequencies;

[0027] It can be understood that the execution entity of the present invention can be a power regulation device of a photovoltaic energy storage system, or a terminal or a server. Specifically, it is not limited here. In the embodiments of the present invention, the server is taken as an example of the execution entity for illustration.

[0028] Specifically, a voltage sensor and a current sensor are installed at the output end of the photovoltaic array to collect the output voltage and current of the photovoltaic array. After the collected voltage and current data are processed by signal processing, a multiplication operation is performed to calculate the output power of the photovoltaic array. At the same time, a voltage sensor is installed at the DC bus to measure the voltage value of the DC bus in real time. The voltage value is calculated with the preset DC bus voltage reference value to obtain the voltage deviation of the DC bus, reflecting the stability of the DC bus voltage. If the voltage deviation is large, it indicates that there is a large mismatch between the output power of the photovoltaic array and the load power, and the power distribution strategy needs to be adjusted to maintain the stable operation of the system. The DC bus voltage deviation is subjected to adaptive band-pass filtering to extract the power fluctuation characteristic signal. The center frequency of this band-pass filter is dynamically adjusted according to the real-time operating state of the system to adapt to different power fluctuation frequencies. Through this filter, power fluctuations in different frequency ranges are effectively identified, and short-period fluctuations and long-term trend changes are distinguished. The adaptive filtering automatically adjusts the filtering parameters according to the dynamic changes of the photovoltaic system, enabling accurate extraction of components with different power fluctuation frequencies. After the adaptive band-pass filtering process, a signal characterizing the power fluctuation characteristics is obtained, and this signal is used to determine whether there are obvious power fluctuations in the system and their influence range. A multi-order polynomial fitting is performed on the photovoltaic output power and the power fluctuation characteristic signal. The polynomial fitting method is used to predict the short-term power trend. By using the historical data of the photovoltaic output power, the power change trend in the future short time is fitted to improve the response ability of power regulation. At the same time, this fitting method can analyze the instantaneous trend of power change, thereby judging whether there are sudden power changes and optimizing the dynamic response strategy of the energy storage system. After completing the multi-order polynomial fitting, a wavelet decomposition method is used to decompose the photovoltaic output power and the power fluctuation characteristic signal to extract power fluctuation components of different frequencies. The wavelet decomposition method disassembles the signal into multiple components of different frequencies to distinguish short-period fluctuations and long-term trend changes. Through this method, the power fluctuation signal is decomposed into high-frequency components, medium-frequency components, and low-frequency components. Among them, the high-frequency components mainly reflect short-term power fluctuations, such as changes caused by load mutations or environmental disturbances; the medium-frequency components correspond to periodic fluctuations, such as short-period power fluctuations caused by changes in illumination; the low-frequency components characterize the long-term power change trend of the system, such as the overall impact of weather changes on photovoltaic power generation. Based on the power fluctuation components of different frequencies obtained by wavelet decomposition, it is judged whether there are abnormal power fluctuations and corresponding power regulation strategies are triggered. Power trigger signals are set to identify significant power fluctuations. When the fluctuation amplitude of the high-frequency component, medium-frequency component, or low-frequency component exceeds the set threshold, the corresponding power trigger signal is triggered to further adjust the power distribution strategy.

[0029] Perform a fifth-order polynomial fitting on the historical data sequence of photovoltaic output power to obtain the predicted value of photovoltaic output power for the next sampling period. To ensure the accuracy of the fitting result, the recursive least squares method is used to dynamically update the coefficients of the polynomial, enabling it to continuously optimize according to the latest data, thereby enhancing the reliability and real-time performance of the prediction. Since the output power of the photovoltaic system is affected by light intensity, ambient temperature, and load changes, through the real-time adjustment of the recursive least squares method, it can more accurately track the changing trend of photovoltaic output power and provide a reasonable estimate of future power levels. Conduct a weighted moving average and exponential smoothing process on the predicted value of photovoltaic output power for the next sampling period to reduce the interference of random fluctuations on the power regulation strategy. Among them, the weighted moving average method effectively filters out the influence of short-term power fluctuations, making the prediction result more stable, while the exponential smoothing method dynamically adjusts the weights according to historical trends, making the contribution of the latest data to the predicted value greater, thus improving the sensitivity of short-term prediction. To ensure the smoothness and stability of the prediction, a comprehensive weighted calculation is performed on the results of the weighted moving average and exponential smoothing, and by reasonably allocating the weight coefficients, it meets the requirements of balancing short-term changes and long-term trends to obtain the medium-term power prediction value. According to the medium-term power prediction value, perform wavelet decomposition on the power fluctuation characteristic signal to extract the power fluctuation components in different frequency ranges. The wavelet decomposition method splits the original signal into multiple components of different scales, thereby distinguishing short-period fluctuations from long-term trend changes. In this process, according to the collected power fluctuation characteristic signal, the wavelet transform method is used for decomposition, and the high-frequency component, intermediate-frequency component, and low-frequency component are extracted respectively. Among them, the high-frequency component reflects the sudden power fluctuations in a short period, such as the impact of rapidly changing load demands or environmental disturbances; the intermediate-frequency component corresponds to periodic power fluctuations, such as the short-period changes in sunlight intensity; while the low-frequency component is used to characterize the long-term power change trend of the system, such as climate change or the fluctuations of long-term load demands. Through wavelet decomposition, identify the power fluctuation conditions in different frequency ranges and provide support for the formulation of power regulation strategies. Based on the amplitudes of the power fluctuation components of different frequencies, assign adaptive weight coefficients to the power fluctuation components of each frequency and perform weighted summation to determine the compensation demand. The compensation weight of the high-frequency component is relatively high to ensure a rapid response to sudden power fluctuations, while the compensation weight of the low-frequency component is relatively low to avoid affecting the long-term stability of the system due to excessive adjustment. The adaptive weight coefficients are dynamically adjusted according to the current power fluctuation situation. For example, when the power fluctuation characteristic signal indicates that the high-frequency component dominates, the system correspondingly increases the weight of high-frequency compensation to enhance the suppression effect of short-period power fluctuations; when the influence of the intermediate-frequency or low-frequency component is more significant, the system then adjusts the weight allocation accordingly to optimize the overall power regulation strategy. In this way, accurately calculate the power compensation demand according to the power fluctuation characteristics of different frequencies.Calculate according to the ratio of the power compensation demand to the system rated power to obtain the power stability index, which is the ratio of the power compensation demand to the system rated power and is used to measure the impact of the current power fluctuation on the system stability. When the power compensation demand is small, the power stability index is high, indicating that the system is in a relatively stable operating state; when the power compensation demand is large, the power stability index is low, indicating that there are large-amplitude power fluctuations in the system and appropriate adjustment measures need to be taken to restore the system stability. Compare the power stability index with a preset threshold to determine whether power adjustment measures are required. When the power stability index is lower than the preset threshold, it means that the power fluctuation of the system exceeds the allowable range and affects the safe operation of the system. Therefore, a power trigger signal is generated to initiate the power adjustment process.

[0030] 102. Perform power vector analysis according to the power fluctuation components of different frequencies and the power trigger signal to obtain the oscillation mode characteristic parameter set and the oscillation suppression strategy;

[0031] Specifically, the power of the system is represented in a vectorized form to more intuitively reflect the dynamic characteristics of the power on the photovoltaic side, battery side, and load side. The system power is represented in complex form, where the real part corresponds to the active power and the imaginary part corresponds to the reactive power. To obtain the precise power vector distribution, based on the phase difference information of the photovoltaic output power, battery power, and load power, the power vectors on the photovoltaic side, battery side, and load side are calculated respectively. The power vector on the photovoltaic side is calculated through the relationship between the photovoltaic output power and the phase angle, while the power vector on the battery side is vectorized based on the current, voltage, and charge / discharge state of the battery. At the same time, the calculation of the power vector on the load side is based on the power demand characteristics of the load. Through this step, the power vector distribution is obtained, which describes the power flow relationship between different power sources and loads. To evaluate the impact of system power oscillation, the power angle of the system and its derivative are calculated, and based on this information, the damping power component and the synchronous power component are extracted. The damping power component is used to measure the ability of the system to suppress power disturbances, while the synchronous power component is used to describe the synchronous characteristics of the system under different power states. The damping power is calculated by taking the time derivative of the power angle, and the synchronous power is calculated by combining the power angle deviation, quantifying the impact of power dynamic changes. At the same time, to evaluate the power oscillation risk of the system, the ratio of the damping power component to the synchronous power component is analyzed, and based on this ratio, it is judged whether the system is in a potential power oscillation state. When the damping power is small, it means that the power disturbances of the system cannot be effectively suppressed, resulting in the occurrence of power oscillation. When the synchronous power is too high, the system will generate large-amplitude power oscillations, affecting its stability. Through this evaluation method, the risk level of power oscillation is determined based on real-time monitoring data, and corresponding adjustment strategies are formulated accordingly. After completing the evaluation of the power oscillation risk, to quantify the power dynamic characteristics of the system, a state space model is constructed. This model includes state variables such as the DC bus voltage, battery current, photovoltaic power, and power angle, and takes the photovoltaic reference voltage and battery reference current as input variables to form a complete mathematical description of the system. The DC bus voltage is used to reflect the voltage stability of the entire system, while the battery current is directly related to the charge / discharge state of the energy storage system. At the same time, the photovoltaic power is the main energy input source of the system, and the power angle is used to describe the interaction between different power components. On this basis, these state variables are constructed into a vector, and combined with the input variable vector, a state space model is established to obtain the system matrix and the input matrix. The system matrix is used to describe the dynamic relationship between state variables, while the input matrix is used to characterize the impact of the photovoltaic reference voltage and battery reference current on the system state. The eigenvalues of the system matrix are calculated, and based on the eigenvalues, the stability of the system is judged. The calculation process of the eigenvalues involves decomposing the system matrix and extracting its real part and imaginary part to analyze the dynamic characteristics of the system under different operating states.The real part of the eigenvalue determines the stability of the system. When the real part is positive, the system is in an unstable state, while when the real part is negative, the system tends to be stable. At the same time, the imaginary part of the eigenvalue reflects the oscillation characteristics of the system. The larger its absolute value, the higher the oscillation frequency of the system. By calculating the real part and the imaginary part of the eigenvalue, it is judged whether the current power state is stable and the occurring power oscillation is predicted. On the basis of obtaining the eigenvalue, a power oscillation evaluation index is calculated. This index is calculated by the ratio of the maximum value of the real part of the eigenvalue to the minimum value of the absolute value of the imaginary part, so as to quantify the intensity of the power oscillation. The power oscillation evaluation index can provide a measurement standard to indicate the current oscillation severity of the system. At the same time, in order to analyze the influence of power fluctuations at different frequencies on the system oscillation, the contribution values of high-frequency, medium-frequency and low-frequency power fluctuation components to the power oscillation evaluation index are calculated respectively to obtain the oscillation contribution value set. The high-frequency component corresponds to rapidly changing power disturbances, while the medium-frequency component mainly reflects periodic power oscillations, and the low-frequency component reflects the long-term stability of the system. Through this analysis method, the main frequency components causing the power oscillation are accurately located, and targeted oscillation suppression strategies are formulated accordingly. Determine the oscillation dominant frequency component according to the high-frequency, medium-frequency and low-frequency oscillation contribution value sets, and select the appropriate oscillation suppression strategy based on this dominant frequency. When the high-frequency component is dominant, a fast-response power compensation strategy is adopted to suppress the power disturbance within a short time, while when the medium-frequency component is dominant, a smooth-transition power distribution strategy is adopted to reduce the influence of periodic power fluctuations. When the low-frequency component dominates the oscillation, a power filtering strategy with a long time constant is adopted to optimize the long-term stability of the system. Output the oscillation mode characteristic parameter set, including the oscillation mode, oscillation frequency and oscillation phase.

[0032] Normalize the sets of contribution values for high-frequency, medium-frequency, and low-frequency oscillations to eliminate the influence of data at different scales and enable comparison within the same numerical range. After normalization, compare the contribution values of oscillations at different frequencies and determine the frequency component corresponding to the maximum contribution value as the current dominant oscillation frequency component. If the high-frequency oscillation contribution value is the largest, it indicates that the power fluctuations of the system are mainly caused by sudden disturbances within a short period. If the medium-frequency oscillation contribution value is the largest, it means that the power fluctuations of the system exhibit periodic oscillation characteristics and are affected by external periodic loads or light changes. If the low-frequency oscillation contribution value is the largest, it shows that the main source of the system's power fluctuations is long-term power imbalance or the slow regulation response of the energy storage system. Through this process, determine the frequency components of the dominant oscillation. Based on the dominant oscillation frequency component, perform strategy matching to select the most suitable oscillation suppression method. If the high-frequency component is dominant, it indicates that the system requires a rapid response to quickly suppress short-term power fluctuations. Therefore, select a power compensation strategy with a rapid response and weaken the impact of high-frequency power disturbances by quickly adjusting the charge and discharge power of the energy storage system. When the medium-frequency component is dominant, it shows that the power fluctuations of the system mainly exhibit periodic changes. Select a power distribution strategy with a smooth transition to make the power regulation process smoother and avoid the impact of drastic adjustments on the system stability. When the low-frequency component is dominant, it indicates that the power fluctuations of the system are of a long-term trend nature. At this time, adopt a power filtering strategy with a long time constant to slowly adjust the power distribution of the system, thereby optimizing the power balance and ensuring the long-term stability of the system. Through the strategy matching method based on the dominant frequency component, select the most suitable oscillation suppression strategy for different types of oscillation characteristics to maximize the effect of power regulation. Perform singular value decomposition on the power fluctuation time-domain signal corresponding to the dominant frequency component to obtain oscillation mode eigenvalues. The singular value decomposition method can effectively extract the main mode information in the power fluctuation signal and remove irrelevant noise, enabling the oscillation mode eigenvalues to accurately represent the main power oscillation modes of the system. To analyze the frequency characteristics of the oscillation, perform a fast Fourier transform on the power fluctuation signal of the dominant frequency component to obtain oscillation frequency eigenvalues. Convert the time-domain signal to the frequency domain and analyze the main frequency components of the power fluctuations to quantify the oscillation frequency characteristics of the system. Calculate the phase difference between the dominant frequency component and adjacent frequency components to obtain oscillation phase eigenvalues. The calculation of the phase difference can reflect the relative timing relationship between different frequency components and thus reveal the propagation characteristics of power oscillations in the time domain. If the phase difference between adjacent frequency components is large, it indicates that the power oscillations exhibit strong phase change characteristics in the system, and it is necessary to further optimize the power regulation strategy to reduce the impact of phase shift on the system stability. On the contrary, if the phase difference is small, it indicates that the power fluctuations of the system are relatively synchronous, and appropriate compensation strategies can be used to enhance the overall stability of the system.Combine the oscillation mode eigenvalue, the oscillation frequency eigenvalue, and the oscillation phase eigenvalue into an oscillation mode feature parameter set.

[0033] 103. Construct a photovoltaic-side feedforward trigger channel and an energy storage-side feedback trigger channel based on the oscillation mode feature parameter set and the oscillation suppression strategy to obtain coordinated trigger commands for the photovoltaic side and the energy storage side;

[0034] Specifically, calculate the difference between the predicted value of the photovoltaic output power and the current photovoltaic output power. This difference reflects the deviation between the current photovoltaic power generation and the predicted power, and is thus used to calculate the feed-forward compensation amount. The calculation of the feed-forward compensation amount needs to consider the dynamic change characteristics of the system. Therefore, a feed-forward gain is set to ensure the rapidity and accuracy of the compensation effect. Since the power oscillation characteristics of the system will affect the effectiveness of the feed-forward compensation, the feed-forward gain is dynamically adjusted according to the high-frequency oscillation contribution value in the oscillation mode characteristic parameter set. If the high-frequency oscillation contribution value is large, it means that the power fluctuation of the system is mainly affected by short-term disturbances. In this case, the feed-forward gain needs to be increased accordingly to enhance the system's response ability to sudden power changes and ensure that the power regulation on the photovoltaic side can quickly follow the power demand changes. On the contrary, if the high-frequency oscillation contribution value is low, it indicates that the power fluctuation of the system is mainly dominated by medium- and low-frequency components. At this time, the feed-forward gain should be appropriately reduced to prevent over-regulation from causing system instability. Through the dynamic adjustment mechanism, the feed-forward trigger channel on the photovoltaic side can effectively adapt to different types of power fluctuation situations and improve the dynamic response ability of the system. At the same time, the construction of the feedback trigger channel on the energy storage side is based on the DC bus voltage deviation and its rate of change to calculate the feedback compensation amount. The DC bus voltage deviation reflects the current power balance state of the system, and its rate of change can describe the dynamic characteristics of the power fluctuation. Calculating the feedback compensation amount based on these two parameters can more accurately characterize the power demand situation of the system. During the calculation of the feedback compensation amount, a proportional gain and a derivative gain are set to ensure that the calculation of the compensation amount can accurately reflect the dynamic changes of the system. Since the medium- and low-frequency components of the power oscillation usually affect the long-term stability of the system, the proportional gain and the derivative gain need to be dynamically adjusted according to the medium- and low-frequency oscillation contribution values in the oscillation mode characteristic parameter set. When the medium-frequency oscillation contribution value is large, it means that the power fluctuation of the system is mainly affected by periodic factors. At this time, the proportional gain is enhanced to improve the ability to suppress medium-period power fluctuations, while the derivative gain should be appropriately reduced to avoid the short-term rapid changes affecting the stability of the system. When the low-frequency oscillation contribution value is large, it indicates that the power fluctuation of the system is mainly caused by long-term power imbalance. At this time, the role of the proportional gain should be appropriately reduced, while the derivative gain should be enhanced to ensure that the system can more smoothly adapt to the long-term power trend changes, thereby optimizing the charge and discharge strategy of the energy storage system. Through the dynamic adjustment mechanism, the feedback trigger channel on the energy storage side can effectively compensate for the long-term power fluctuations of the system and maintain the stability of the system operation. After completing the construction of the feed-forward trigger channel on the photovoltaic side and the feedback trigger channel on the energy storage side, trigger commands for the photovoltaic side and the energy storage side are generated respectively according to the changes in the feed-forward compensation amount on the photovoltaic side and the feedback compensation amount on the energy storage side. For the generation of the trigger command on the photovoltaic side, judge whether the absolute value of the feed-forward compensation amount exceeds the set feed-forward threshold or whether its rate of change exceeds the rate-of-change threshold.If the absolute value of the feedforward compensation amount exceeds the feedforward threshold, it indicates that the current adjustment requirement for photovoltaic power is large. At this time, the power adjustment on the photovoltaic side is triggered to quickly respond to the power fluctuation requirement. Similarly, if the change rate of the feedforward compensation amount exceeds the change rate threshold, it means that the change trend of the photovoltaic power is fast. At this time, the power adjustment command on the photovoltaic side is triggered to prevent the rapid change of power from affecting the stability of the system. Through this judgment mechanism, the photovoltaic side trigger channel can quickly respond to different types of power fluctuations and provide corresponding compensation strategies. During the generation process of the energy storage side trigger command, it is judged according to the comparison result of the absolute value of the feedback compensation amount and the feedback threshold, and whether the DC bus voltage deviation exceeds the safe range. If the absolute value of the feedback compensation amount exceeds the set feedback threshold, it indicates that the current energy storage system needs a large amount of charge and discharge adjustment to maintain the stability of the DC bus voltage. Therefore, the power adjustment command on the energy storage side is triggered to increase the response ability of the energy storage system. If the DC bus voltage deviation exceeds the safe range, it indicates that there is a large imbalance in the power distribution of the current system. At this time, the energy storage side needs to make an immediate adjustment to avoid voltage fluctuations affecting the normal operation of the system. Through this trigger mechanism, the energy storage side trigger channel can provide accurate adjustment commands under different power fluctuation conditions to maintain the balance of the system power. After the trigger commands on the photovoltaic side and the energy storage side are generated, these two trigger commands are combined, and the coordination coefficient is dynamically adjusted according to the oscillation suppression strategy to obtain the final coordinated trigger command. The adjustment of the coordination coefficient needs to consider the oscillation characteristics of the current system. When the contribution value of high-frequency oscillation is large, the weight of the photovoltaic side is increased to enhance the response ability to high-frequency power fluctuations. When the contribution values of medium-frequency and low-frequency oscillations are large, the weight of the energy storage side is increased to enhance the compensation ability of the system to long-term power fluctuations. Through reasonable coordinated trigger commands, the system can dynamically allocate power adjustment tasks between the photovoltaic side and the energy storage side to optimize the stability and operation efficiency of the system.

[0035] 104. Perform multi-objective optimization decomposition and slope limit processing on the coordinated trigger command to obtain the voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller.

[0036] Specifically, multiply the coordination trigger instruction by the power ratio factor to complete the conversion of power demand and obtain the total power regulation demand. The setting of the power ratio factor depends on the rated power of the system and the current operating state to ensure that the calculated total power regulation demand can accurately reflect the current power distribution demand. On this basis, an optimization objective function is constructed to optimize the power regulation process while satisfying the system operation constraints. To ensure that the power regulation can not only quickly respond to the power change demand of the system but also minimize the energy loss to the greatest extent, the optimization objective function considers both the response speed objective and the energy loss objective. In terms of response speed, reach the new power balance state as soon as possible to avoid the impact of power fluctuations on the system stability, so the objective function includes a term that can minimize the regulation time. In terms of energy loss, reduce the conversion loss between photovoltaic power generation and the energy storage system, so the objective function includes a term that can minimize the energy loss. In this way, the optimization objective function can comprehensively consider the dynamic performance and energy efficiency level of the system, thus realizing an efficient power regulation strategy. Based on the optimization objective function, optimize and solve it under certain constraint conditions to ensure that the calculated power regulation scheme conforms to the actual operation limits of the system. Among them, the photovoltaic output power is restricted by the photovoltaic power generation capacity, so the photovoltaic-side power regulation instruction must be within the maximum output power range allowed by the photovoltaic system. The charge and discharge capacity of the energy storage system is restricted by the state of charge of the battery, so the energy storage-side power regulation instruction needs to ensure that it will not cause the state of charge of the battery to exceed the safe range. Based on these constraint conditions, use the optimization solution algorithm to calculate the optimal photovoltaic-side power regulation instruction and energy storage-side power regulation instruction. According to the photovoltaic output characteristic curve, calculate the voltage reference value of the photovoltaic MPPT controller. To ensure that the photovoltaic system always operates near the optimal power point, search the photovoltaic output characteristic curve to determine the maximum power point voltage value under the current conditions. Since the power regulation instruction causes the adjustment of the photovoltaic power, calculate a voltage offset to ensure that the photovoltaic output power can meet the new power regulation demand. Add the maximum power point voltage value to the calculated offset to obtain the first reference value of the photovoltaic MPPT controller, so as to ensure that the photovoltaic system can operate stably under the new power demand. At the same time, according to the energy storage-side power regulation instruction, calculate the current reference value of the battery charge and discharge controller. Since the charge and discharge power of the battery is related to the battery voltage, divide the energy storage-side power regulation instruction by the current battery voltage value to calculate the actual charge and discharge current demand. Through this calculation method, ensure that the charge and discharge process of the battery matches the power regulation instruction, thus optimizing the operation state of the energy storage system and improving the overall energy utilization efficiency of the system. To prevent too rapid voltage or current changes during the power regulation process from affecting the system stability, perform slope limit processing on the calculated voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller.Set the maximum voltage change rate to ensure that the voltage reference value of the PV MPPT controller does not change suddenly, thus avoiding unstable power output of the PV system due to too fast voltage change. Similarly, set the maximum current change rate to ensure that the current reference value of the battery charge and discharge controller is smoothly adjusted within a reasonable range, thus avoiding the decrease of battery life or system oscillation of the energy storage system due to too fast current change. Through slope limit processing, ensure the smoothness of the power regulation process and improve the operation stability and safety of the entire PV energy storage system.

[0037] In the embodiment of the present invention, through multi-order polynomial fitting and wavelet decomposition techniques, accurate identification and prediction of power fluctuation components with different frequencies are realized. Based on the quantitative analysis method of the power vector analysis model, the influence mechanism of each frequency component on the system stability is clarified. The design of the dual-channel power trigger mechanism realizes the coordinated cooperation of PV side feedforward and energy storage side feedback, and can effectively suppress power fluctuations without calculating complex power compensation values on the PV side; The power distribution strategy of multi-objective optimization takes into account both the response speed and the energy utilization efficiency, and realizes the optimal allocation of system resources; Adopt a differential processing strategy for power fluctuations with different frequency characteristics to avoid the risk of power oscillation caused by control mode switching; The introduction of the system operation state evaluation and control strategy optimization mechanism ensures that the system maintains the best performance state during long-term operation, and significantly improves the power stability and energy utilization efficiency of the PV energy storage system under extreme conditions such as sudden changes in light or load mutations.

[0038] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0039] Collect the output voltage value and output current value of the PV array through the voltage sensor and current sensor set at the output end of the PV array, and perform a product operation on the output voltage value and output current value to obtain the PV output power;

[0040] Collect the DC bus voltage value through the voltage sensor set on the DC bus, and calculate the difference between the DC bus voltage value and the preset DC bus voltage reference value to obtain the DC bus voltage deviation;

[0041] Perform adaptive band-pass filtering on the DC bus voltage deviation to obtain a power fluctuation characteristic signal;

[0042] Based on the PV output power and the power fluctuation characteristic signal, perform multi-order polynomial fitting and wavelet decomposition to obtain power fluctuation components and power trigger signals with different frequencies.

[0043] Specifically, voltage sensors and current sensors are installed at the output end of the photovoltaic array to collect the output voltage and current values of the photovoltaic array in real time. These sensors convert the voltage and current information of the photovoltaic power generation unit into digital signals through a high-precision data acquisition module and transmit them to the control system. After receiving the data, the system calculates the photovoltaic output power by multiplying the collected voltage value by the current value. This power value can accurately represent the power generation capacity of the photovoltaic system under the current environmental conditions. At the same time, a voltage sensor is installed at the DC bus to obtain the real-time DC bus voltage. The DC bus voltage is a key parameter of the entire system and directly affects the power balance among the photovoltaic power generation unit, the energy storage unit, and the load. By measuring the voltage value of the DC bus and comparing it with the preset DC bus voltage reference value, the DC bus voltage deviation is calculated to reflect the current power balance situation of the system. If the deviation is too large, it indicates a significant mismatch among photovoltaic power generation, energy storage charging and discharging, and load power, and a reasonable power regulation strategy is needed to optimize the system operation. The DC bus voltage deviation is subjected to adaptive band-pass filtering to remove irrelevant low-frequency drift and high-frequency noise, and at the same time, the power fluctuation characteristic signal that has a significant impact on the system operation state is extracted. Adaptive band-pass filtering can dynamically adjust the filtering parameters according to the real-time operation state of the system to adapt to different power fluctuation characteristics. When the system is in a stable state, the center frequency of the filter is relatively low to capture the slowly changing power fluctuation trend, while when the system detects a rapid power change, the center frequency of the filter will automatically increase to improve the response ability to high-frequency power fluctuations. Multistage polynomial fitting is performed on the photovoltaic output power and the power fluctuation characteristic signal to predict the power change trend in a short period. Multistage polynomial fitting establishes a mathematical model based on historical data to predict the change of future power levels. By continuously updating the fitting parameters to adapt to different weather conditions and load changes, the accuracy of power prediction is improved. In order to decompose the different frequency components of the power fluctuation and extract the key power disturbance signal, wavelet decomposition is performed on the fitted power signal. The original power signal is split into multiple components of different scales to distinguish short-period fluctuations and long-term trend changes, and the power fluctuation components and power trigger signals of different frequencies are obtained.

[0044] In a specific embodiment, the process of performing multistage polynomial fitting and wavelet decomposition based on the photovoltaic output power and the power fluctuation characteristic signal to obtain the power fluctuation components and power trigger signals of different frequencies may specifically include the following steps:

[0045] Perform fifth-order polynomial fitting on the historical data sequence of the photovoltaic output power, and update the polynomial coefficients by the recursive least squares method to obtain the predicted value of the photovoltaic output power in the next sampling period;

[0046] The predicted value of the photovoltaic output power for the next sampling period is subjected to weighted moving average and exponential smoothing processing, and weight coefficients are assigned for weighted calculation to obtain the medium-term power prediction value;

[0047] According to the medium-term power prediction value, the power fluctuation characteristic signal is subjected to wavelet decomposition processing, and the high-frequency component, intermediate-frequency component and low-frequency component are respectively extracted to obtain power fluctuation components of different frequencies;

[0048] Based on the amplitudes of the power fluctuation components of different frequencies, adaptive weight coefficients are assigned to the power fluctuation components of each frequency and weighted summation is performed to obtain the power compensation demand;

[0049] Calculation is performed according to the ratio of the power compensation demand to the rated power of the system to obtain the power stability index;

[0050] The power stability index is compared with a preset threshold. When the power stability index is lower than the preset threshold, a power trigger signal is generated.

[0051] Specifically, analyze the historical data sequence of photovoltaic output power and establish a mathematical model using the method of fifth-order polynomial fitting to predict the photovoltaic output power in the next sampling period. In this process, continuously collect the output power data of the photovoltaic array and store the historical power data sequence within a certain period as the basis for fitting. The fifth-order polynomial fitting can better describe the trend of photovoltaic power changing with time and can adapt to complex non-linear fluctuations, thus providing relatively accurate power prediction values. To improve the real-time performance and accuracy of fitting, the recursive least squares method is used to update the polynomial coefficients. The recursive least squares method is a dynamic update algorithm that adjusts the existing polynomial fitting results according to new measurement data, so as to ensure that the fitting model can respond quickly when the photovoltaic output power changes. Smooth the predicted value of the photovoltaic output power in the next sampling period to eliminate the influence of short-term random fluctuations on power regulation. A method combining weighted moving average and exponential smoothing is used to optimize the predicted data. In the process of weighted moving average processing, different weights are assigned to multiple historical sampling points, so that the newer data has a greater impact on the final prediction result, while the older data has a relatively smaller impact. At the same time, the exponential smoothing method assigns exponentially decaying weights to new data to enhance the system's sensitivity to recent data changes and reduce the cumulative error of historical data. This weighted calculation method enables the power prediction result to not only reflect short-term trend changes but also maintain a certain stability when the data fluctuates greatly, thereby improving the reliability of power regulation. After calculating the medium-term power prediction value, use this prediction value to perform wavelet decomposition on the power fluctuation characteristic signal to extract the power fluctuation components in different frequency ranges. The wavelet decomposition method can decompose the power fluctuation signal into components of multiple scales, so as to distinguish short-term severe fluctuations and long-term trend changes. Extract the high-frequency component, medium-frequency component and low-frequency component respectively. The high-frequency component mainly reflects the power mutation in a short time, such as the power change caused by load mutation or environmental disturbance; the medium-frequency component corresponds to periodic fluctuations, such as the power oscillation caused by weather changes or grid fluctuations; while the low-frequency component mainly reflects the long-term power change trend of the system, such as the impact of seasonal sunshine changes on photovoltaic power generation. Based on the amplitudes of the power fluctuation components of different frequencies, perform weighted summation on them to calculate the overall power compensation demand. To ensure that the power compensation strategy accurately adapts to different types of power fluctuations, assign adaptive weight coefficients to the power fluctuation components of each frequency and dynamically adjust these weights according to the power fluctuation characteristics. For example, when the system detects that the amplitude of the high-frequency component is large, it indicates that the power change in a short time is relatively severe. At this time, increase the weight of the high-frequency power fluctuation component to enhance the compensation ability for short-term power fluctuations; when the amplitude of the medium-frequency or low-frequency component is large, it indicates that the main source of power fluctuation is periodic oscillation or long-term trend change. At this time, increase the weight of medium-frequency or low-frequency compensation accordingly to optimize the power balance strategy.After calculating the power compensation demand, evaluate the current power stability and calculate the power stability index, which represents the ratio of the power compensation demand to the rated power of the system, thereby quantifying the impact of the current power fluctuation on the system stability. When the power stability index is high, it indicates that the power fluctuation of the system is small and the overall operation is relatively stable. While when the power stability index is low, it indicates that the power fluctuation of the system is large and corresponding adjustment measures need to be taken to restore the power balance. Compare the power stability index with a preset threshold to determine whether power adjustment measures are needed. If the power stability index is lower than the preset threshold, it indicates that the current power fluctuation of the system has exceeded the safe range and affects the normal operation of the system, generating a power trigger signal to initiate the power adjustment process.

[0052] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0053] Express the system power in complex form and calculate the power vectors on the photovoltaic side, battery side, and load side based on the phase difference information to obtain the system power vector distribution;

[0054] Calculate the damping power component and synchronous power component according to the system power angle and its derivative, and evaluate the ratio of the damping power component to the synchronous power component to obtain the system power oscillation risk determination result;

[0055] Establish a system state space model including a state variable vector of the DC bus voltage, battery current, photovoltaic power, and power angle and an input variable vector of the photovoltaic reference voltage and battery reference current based on the system power vector distribution and the system power oscillation risk determination result to obtain the system matrix and input matrix;

[0056] Calculate the eigenvalues of the system matrix, and extract the real part and imaginary part from the eigenvalues to obtain the system stability criterion;

[0057] Calculate the power oscillation evaluation index based on the ratio of the maximum value of the real part of the eigenvalue to the minimum absolute value of the imaginary part, and calculate the contribution value of each frequency power fluctuation component to the power oscillation evaluation index respectively to obtain the high-frequency, medium-frequency, and low-frequency oscillation contribution value sets;

[0058] Determine the oscillation dominant frequency component according to the high-frequency, medium-frequency, and low-frequency oscillation contribution value sets, select the corresponding oscillation suppression strategy based on the oscillation dominant frequency component, and output the oscillation mode characteristic parameter set at the same time.

[0059] Specifically, the system power is represented in complex form to more intuitively describe the power distribution on the photovoltaic side, battery side, and load side. By using the complex representation method of active power and reactive power, power analysis takes into account both amplitude and phase information simultaneously. The calculation of the power vector on the photovoltaic side is based on the phase angle between the photovoltaic output power and voltage and current, while the power vector on the battery side is calculated by combining the charge and discharge power of the battery and the dynamic characteristics of the energy storage system. At the same time, the calculation of the power vector on the load side depends on the power demand of the load and the dynamic changes of the load. Through this method, the power flow relationship among photovoltaic power generation, energy storage units, and loads is obtained, thereby constructing a power vector distribution diagram. After obtaining the power vector distribution of the system, the power angle and its derivative are calculated to analyze the power oscillation characteristics of the system. The power angle reflects the relative phase between different power components, and the rate of change of the power angle is used to measure the intensity of power oscillation. To evaluate the power oscillation risk of the system, the damping power component and synchronous power component are calculated. The damping power component describes how the system dissipates power disturbances, while the synchronous power component reflects the response characteristics of the system under power fluctuations. To determine the power oscillation risk, the ratio of the damping power component to the synchronous power component is calculated, and based on this, the current power stability is judged. When the damping power component is large, the system can effectively suppress power oscillation, while when the synchronous power component is large, the system is prone to unstable power oscillation under the influence of external disturbances. Through this analysis method, the severity of power oscillation is monitored in real time, and it is determined whether further adjustment measures need to be taken. Based on the above power vector distribution and power oscillation risk determination results, a state-space model is established to describe the dynamic characteristics of the entire photovoltaic energy storage system. In this model, the state variable vector of the system includes key parameters such as the DC bus voltage, battery current, photovoltaic power, and power angle, while the input variable vector includes the photovoltaic reference voltage and battery reference current. The state variable vector is used to describe the dynamic state of the system, while the input variable vector is used to control the power output of the system and the charge and discharge strategy of the energy storage unit. To construct a complete state-space model, the state equation is defined to describe the relationship between state variables over time, and the input equation is defined to characterize the influence of input variables on the system state. After constructing the state-space model, the eigenvalues of the system matrix are calculated, and the real part and imaginary part are extracted from the eigenvalues to judge the stability of the system. The real part of the eigenvalue determines the stability of the system. When the real part is positive, the system is in an unstable state, while when the real part is negative, the system tends to be stable. At the same time, the imaginary part of the eigenvalue reflects the oscillation characteristics of the system, and the larger its absolute value, the higher the oscillation frequency of the system. To quantify the intensity of power oscillation, based on the ratio of the maximum value of the real part of the eigenvalue to the minimum value of the absolute value of the imaginary part, a power oscillation evaluation index is calculated, which can provide an intuitive measure to indicate the current severity of system oscillation. The contribution values of high-frequency, medium-frequency, and low-frequency power fluctuation components are calculated respectively, and an oscillation contribution value set is constructed.The contribution value of the high-frequency component reflects the power disturbance situation in a short period of time. The contribution value of the medium-frequency component reflects the periodic oscillation of the system, while the contribution value of the low-frequency component reflects the influence of long-term power imbalance. Through this step, the power oscillation components in different frequency ranges are accurately identified, so as to determine the dominant frequency component of the oscillation. Based on the set of oscillation contribution values, the current dominant oscillation frequency component is determined, and an appropriate oscillation suppression strategy is selected according to this component. When the high-frequency component is dominant, a fast-response power compensation strategy is adopted to suppress the power disturbance in a short period of time. When the medium-frequency component is dominant, a smooth-transition power distribution strategy is adopted to reduce the influence of periodic power fluctuations. When the low-frequency component dominates the oscillation, a power filtering strategy with a long time constant is adopted to optimize the long-term stability of the system. The oscillation mode characteristic parameter set is output, including oscillation mode characteristics, oscillation frequency characteristics, and oscillation phase characteristics.

[0060] In a specific embodiment, the process of performing the step of determining the dominant oscillation frequency component according to the sets of high-frequency, medium-frequency, and low-frequency oscillation contribution values, selecting the corresponding oscillation suppression strategy according to the dominant oscillation frequency component, and outputting the oscillation mode characteristic parameter set may specifically include the following steps:

[0061] Compare the magnitudes and perform normalization processing on the sets of high-frequency, medium-frequency, and low-frequency oscillation contribution values, and determine the frequency component corresponding to the maximum contribution value as the dominant oscillation frequency component;

[0062] Perform strategy matching based on the dominant oscillation frequency component. When the high-frequency component is dominant, select a fast-response power compensation strategy. When the medium-frequency component is dominant, select a smooth-transition power distribution strategy. When the low-frequency component is dominant, select a power filtering strategy with a long time constant to obtain the oscillation suppression strategy;

[0063] Perform singular value decomposition on the power fluctuation time-domain signal corresponding to the dominant frequency component to obtain the oscillation mode eigenvalue; perform fast Fourier transform on the power fluctuation signal of the dominant frequency component to obtain the oscillation frequency eigenvalue; calculate the phase difference between the dominant frequency component and the adjacent frequency component to obtain the oscillation phase eigenvalue;

[0064] Combine the oscillation mode eigenvalue, oscillation frequency eigenvalue, and oscillation phase eigenvalue into an oscillation mode characteristic parameter set.

[0065] Specifically, analyze the contribution value sets of high-frequency, medium-frequency, and low-frequency oscillations, and determine the proportion of each frequency component in the overall oscillation. Since the power fluctuations of different frequencies have different degrees of influence on the system stability, during the oscillation analysis, normalize the contribution values of high-frequency, medium-frequency, and low-frequency oscillations to eliminate the differences in numerical scales, ensuring that each frequency component can be compared under the same standard. The normalized data can more accurately reflect the relative influence degrees of each oscillation component and avoid misjudgment caused by different data magnitudes. After the normalization process, compare the contribution values of high-frequency, medium-frequency, and low-frequency components, and determine the frequency component corresponding to the maximum contribution value as the current dominant oscillation frequency component. If the contribution value of the high-frequency component is the largest, it indicates that the power fluctuations of the current system are mainly caused by rapid disturbances within a short period. If the contribution value of the medium-frequency component is the largest, it means that the power fluctuations of the system have a certain periodicity and are affected by external periodic loads or weather changes. If the contribution value of the low-frequency component is the largest, it implies that the power fluctuations of the system mainly stem from long-term power imbalance or the slow regulation response of the energy storage system. Based on the dominant frequency component, perform strategy matching to select the most suitable oscillation suppression method. If the high-frequency component is dominant, it indicates that the system needs to respond quickly to rapidly suppress short-term power fluctuations. Select a fast-response power compensation strategy and weaken the impact of high-frequency power disturbances by quickly adjusting the charge and discharge power of the energy storage system. For example, increase the short-time discharge power of the energy storage system to compensate for the power gap and reduce the fluctuation amplitude of the DC bus voltage. When the medium-frequency component is dominant, it shows that the power fluctuations of the system mainly exhibit periodic changes. At this time, direct rapid compensation will lead to system instability. Therefore, select a power distribution strategy with smooth transition to make the power regulation process smoother. For example, adopt a method of slowly adjusting the power to reduce the sensitivity of the system to periodic disturbances and reduce the power oscillations caused during the regulation process. When the low-frequency component is dominant, it means that the power fluctuations of the system are of a long-term trend. Adopt a power filtering strategy with a long time constant to slowly adjust the power distribution of the system, thereby optimizing the power balance and ensuring the long-term stability of the system. For example, adopt a long-term power balance strategy, gradually increase the discharge amount of the energy storage system, and at the same time optimize the energy distribution strategy on the load side to adapt to the long-term change trend of the photovoltaic output. Analyze the power fluctuation characteristics corresponding to the dominant frequency component to extract the oscillation mode characteristic parameter set. To obtain the modal characteristics of the oscillation, perform singular value decomposition on the power fluctuation time-domain signal corresponding to the dominant frequency component to obtain the oscillation mode eigenvalues. The singular value decomposition method can effectively extract the main modal information in the power fluctuation signal and remove irrelevant noise, enabling the oscillation mode eigenvalues to accurately represent the main power oscillation modes of the system. To analyze the frequency characteristics of the oscillation, perform a fast Fourier transform on the power fluctuation signal of the dominant frequency component to obtain the oscillation frequency eigenvalues.The fast Fourier transform converts the time-domain signal to the frequency domain and resolves the main frequency components of the power fluctuation, thereby quantifying the oscillation frequency characteristics of the system. To analyze the phase characteristics of the power fluctuation, the phase difference between the dominant frequency component and the adjacent frequency component is calculated to obtain the oscillation phase eigenvalue. The calculation of the phase difference can reflect the relative timing relationship between different frequency components, thereby revealing the propagation characteristics of the power oscillation in the time domain. For example, in a certain off-grid photovoltaic system, it is detected that there is a large phase shift in the power fluctuations of different frequency components, which indicates that the power regulation strategy of the system needs to be optimized to reduce the delay response of the energy storage system to the change of photovoltaic power, thereby improving the overall stability of the system. If the phase difference between adjacent frequency components is large, it means that the power oscillation exhibits strong phase change characteristics in the system, and the power regulation strategy needs to be further optimized to reduce the impact of the phase shift on the system stability. If the phase difference is small, it indicates that the power fluctuations of the system are relatively synchronous, and the overall stability of the system can be enhanced through appropriate compensation strategies. The oscillation mode eigenvalue, the oscillation frequency eigenvalue, and the oscillation phase eigenvalue are combined into a complete set of oscillation mode characteristic parameters.

[0066] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0067] Calculate the feedforward compensation amount according to the difference between the predicted value of the photovoltaic output power and the current photovoltaic output power, and set a feedforward gain for the feedforward compensation amount, where the feedforward gain is dynamically adjusted according to the high-frequency oscillation contribution value in the oscillation mode characteristic parameter set, and construct a feedforward trigger channel on the photovoltaic side;

[0068] Calculate the feedback compensation amount based on the DC bus voltage deviation and its rate of change, and set a proportional gain and a derivative gain for the feedback compensation amount, where the proportional gain and the derivative gain are dynamically adjusted according to the medium-frequency and low-frequency oscillation contribution values in the oscillation mode characteristic parameter set, and construct a feedback trigger channel on the energy storage side;

[0069] Adopt the feedforward trigger channel on the photovoltaic side, and generate a photovoltaic-side trigger command according to the comparison result of the absolute value of the feedforward compensation amount with the feedforward threshold or the comparison result of its rate of change with the rate-of-change threshold;

[0070] Adopt the feedback trigger channel on the energy storage side, and generate an energy storage-side trigger command according to the comparison result of the absolute value of the feedback compensation amount with the feedback threshold or the judgment result that the DC bus voltage deviation exceeds the safe range;

[0071] Combine the photovoltaic-side trigger command and the energy storage-side trigger command, and dynamically adjust the coordination coefficient according to the oscillation suppression strategy to obtain a coordinated trigger command.

[0072] Specifically, a feed-forward trigger channel on the PV side is constructed to calculate the feed-forward compensation amount based on the difference between the predicted PV output power and the current PV output power. The predicted value of the PV output power is obtained by performing multi-order polynomial fitting on historical power data and dynamically updating it in combination with the recursive least squares method, which can reflect the power trend in the short term in the future. When there is a significant difference between the predicted value and the current PV output power, it indicates that the power balance of the system has changed and the power output on the PV side needs to be adjusted. The feed-forward compensation amount is determined by this power deviation and directly affects the regulation ability of the PV system. Therefore, a feed-forward gain is set to adjust the compensation intensity according to different power fluctuation conditions. The magnitude of the feed-forward gain is not fixed but is dynamically adjusted according to the high-frequency oscillation contribution value in the oscillation mode characteristic parameter set. If the high-frequency oscillation contribution value is large, it indicates that the power fluctuation of the system is mainly caused by short-term disturbances. At this time, the feed-forward gain should be appropriately increased to enhance the response ability to high-frequency power fluctuations, enabling the PV power to be adjusted quickly, thereby suppressing the impact of short-term power disturbances. If the high-frequency oscillation contribution value is low, it indicates that the power fluctuation of the system is mainly affected by medium and low-frequency components. At this time, the feed-forward gain should be appropriately reduced to avoid system instability caused by overcompensation. At the same time, to optimize power regulation, a feedback trigger channel on the energy storage side is constructed, and this channel calculates the feedback compensation amount based on the DC bus voltage deviation and its rate of change. The DC bus voltage deviation can reflect the current power balance of the system, and the rate of change of the voltage deviation further describes the dynamic characteristics of power regulation. When the bus voltage deviation is large, it indicates that there is a large deviation in the power matching among photovoltaic power generation, energy storage units, and loads, and compensation is carried out through the charge and discharge of the energy storage system. To ensure that the compensation amount can adapt to different power fluctuation conditions, a proportional gain and a derivative gain are set for the feedback compensation amount. Among them, the role of the proportional gain is to directly adjust the power output of the energy storage system according to the magnitude of the voltage deviation, while the derivative gain can predict the future power trend based on the rate of change of the voltage deviation and make an early response. To optimize the regulation effect of the feedback channel, the magnitudes of the proportional gain and the derivative gain are dynamically adjusted according to the medium-frequency and low-frequency oscillation contribution values in the oscillation mode characteristic parameter set. When the medium-frequency oscillation contribution value is large, it indicates that the power fluctuation of the system mainly shows periodic oscillation. At this time, the proportional gain should be appropriately increased to enhance the regulation ability for periodic power fluctuations, while the derivative gain should be appropriately reduced to avoid the energy storage system over-following the power fluctuation, resulting in oscillation during the regulation process. When the low-frequency oscillation contribution value is large, it indicates that the power fluctuation of the system is mainly caused by long-term power imbalance. At this time, the derivative gain should be appropriately increased to enhance the system's adaptability to long-term power changes, while the proportional gain should be relatively reduced to ensure a smoother power regulation process. The feed-forward trigger channel on the PV side and the feedback trigger channel on the energy storage side are used to generate trigger commands for the PV side and the energy storage side respectively.For the generation of the photovoltaic side trigger instruction, it is judged whether the absolute value of the feed-forward compensation amount exceeds the set feed-forward threshold or whether its change rate exceeds the change rate threshold. If the absolute value of the feed-forward compensation amount exceeds the feed-forward threshold, it indicates that the adjustment requirement for the current photovoltaic power is large. At this time, the power adjustment instruction on the photovoltaic side is triggered to make the photovoltaic output power quickly respond to the change of the power demand. Similarly, if the change rate of the feed-forward compensation amount exceeds the change rate threshold, it means that the change trend of the photovoltaic power is fast. At this time, the power adjustment instruction on the photovoltaic side is triggered to prevent the power change from being too fast and affecting the stability of the system. For example, in a certain photovoltaic energy storage system, when sudden cloud shading causes the photovoltaic output power to drop within a short period of time while the power prediction value of the system still remains at a relatively high level, the absolute value of the feed-forward compensation amount will increase rapidly and exceed the set feed-forward threshold. At this time, the photovoltaic side trigger channel will immediately issue an instruction to adjust the reference voltage of the photovoltaic MPPT controller to make the photovoltaic output power adjust to the new steady state. In the process of generating the energy storage side trigger instruction, it is judged according to the comparison result of the absolute value of the feedback compensation amount and the feedback threshold, and whether the DC bus voltage deviation exceeds the safe range. If the absolute value of the feedback compensation amount exceeds the set feedback threshold, it means that the current energy storage system needs a large amount of charge and discharge adjustment to maintain the stability of the DC bus voltage. Therefore, the energy storage side power adjustment instruction is triggered to increase the response ability of the energy storage system. If the DC bus voltage deviation exceeds the safe range, it indicates that there is a large imbalance in the power distribution of the current system. At this time, the energy storage side needs to make an immediate adjustment to avoid voltage fluctuations affecting the normal operation of the system. For example, when the DC bus voltage changes beyond the safe range due to a sudden change in the load, the energy storage side feedback channel will immediately detect this change and trigger the discharge instruction of the energy storage system to compensate for the voltage fluctuation of the bus and make the system return to stability again. After the photovoltaic side and energy storage side trigger instructions are generated, these two trigger instructions are combined, and the coordination coefficient is dynamically adjusted according to the oscillation suppression strategy to obtain the final coordinated trigger instruction. The adjustment of the coordination coefficient needs to consider the oscillation characteristics of the current system. When the contribution value of high-frequency oscillation is large, the system increases the weight of the photovoltaic side to enhance the response ability to high-frequency power fluctuations, while when the contribution values of medium-frequency and low-frequency oscillations are large, the system increases the weight of the energy storage side to enhance the compensation ability of the system to long-term power fluctuations.

[0073] In a specific embodiment, the process of executing step 104 may specifically include the following steps:

[0074] Multiply the coordinated trigger instruction by the power ratio factor for conversion to obtain the total power adjustment requirement;

[0075] Based on the total power adjustment requirement, an optimization objective function considering the response speed objective function and the energy loss objective function is established;

[0076] Under the constraints of photovoltaic output power and battery state of charge, the optimization objective function is optimized to obtain the power regulation command on the photovoltaic side and the power regulation command on the energy storage side;

[0077] According to the power regulation command on the photovoltaic side and the photovoltaic output characteristic curve, look-up table mapping is performed to calculate the maximum power point voltage and the offset, and the maximum power point voltage is added to the offset to obtain the first reference value of the photovoltaic MPPT controller;

[0078] The power regulation command on the energy storage side is divided by the battery voltage value to calculate the second reference value of the battery charge and discharge controller;

[0079] The slope limit processing is performed on the first reference value and the second reference value. The change rate of the first reference value is limited within the maximum voltage change rate range, and the change rate of the second reference value is limited within the maximum current change rate range to obtain the voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller.

[0080] Specifically, the coordinated trigger command is converted to determine the total power regulation demand. According to the real-time operating states of the photovoltaic side and the energy storage side, the coordinated trigger command is multiplied by the power ratio factor to ensure that the power regulation demand adapts to different load conditions and power generation environments. The setting of the power ratio factor needs to consider the rated power ranges of the photovoltaic system and the energy storage system, as well as the current operating state of the system to avoid power fluctuations caused by over-regulation or voltage deviation caused by under-regulation. Through this conversion process, the total power regulation demand is obtained. Based on the total power regulation demand, an optimization objective function considering the response speed objective function and the energy loss objective function is established to optimize the power regulation efficiency while satisfying the system operation constraints. The optimization objective function comprehensively considers two key factors, namely, response speed and energy loss. The response speed is a measure of the time required for the system to adjust to a new stable state when a power fluctuation occurs. To improve the dynamic response performance of the system, the optimization objective function includes a term that can minimize the regulation time. The energy loss is a measure of the additional loss generated by the system during the power regulation process. To improve the overall energy efficiency, the objective function includes a term that minimizes the energy loss. Based on these two objectives, the optimization objective function is constructed to optimally allocate the power regulation tasks on the photovoltaic side and the energy storage side on the premise of ensuring system stability, thereby reducing unnecessary energy loss while ensuring the regulation effect. The formula is as follows:

[0081]

[0082] Among them, is the optimization objective function, represents the power regulation amount of the system, represents the energy loss amount, and are the weight coefficients for balancing the response speed and energy loss, respectively, while It is to adjust the time range. The solution of the optimization objective function needs to be carried out under certain constraints to ensure that the calculated power regulation scheme conforms to the actual operation limits of the system. When solving the optimization objective function, the constraint conditions of the photovoltaic output power are considered. The output power of the photovoltaic system is affected by light intensity, temperature and device characteristics. Therefore, the power regulation command on the photovoltaic side cannot exceed the maximum allowable output power range of the photovoltaic array, otherwise it will cause the failure of MPPT (maximum power point tracking) control or the overloaded operation of the photovoltaic power generation equipment. At the same time, the charge and discharge capacity of the energy storage system is limited by the state of charge of the battery. Therefore, the power regulation command on the energy storage side needs to ensure that the state of charge of the battery will not exceed the safe range. If the state of charge of the battery is already close to the upper limit, the system should reduce the charging power to prevent overcharging and damaging the battery. If the state of charge is too low, the discharge power should be reduced to ensure that the battery can maintain normal operation. Under the condition of meeting these constraint conditions, the optimal solution algorithm is used to calculate the optimal power regulation command on the photovoltaic side and the power regulation command on the energy storage side, so as to ensure that the system can achieve the best energy efficiency and stability during the power regulation process. After obtaining the power regulation command on the photovoltaic side, according to the photovoltaic output characteristic curve, the voltage reference value of the photovoltaic MPPT controller is calculated. Since the maximum power point voltage of the photovoltaic system is not fixed but affected by the changes of light intensity and temperature, a look-up table mapping is performed to obtain the maximum power point voltage value under the current environmental conditions. At the same time, considering that the photovoltaic power regulation command may cause the adjustment of the photovoltaic output power, a voltage offset is calculated to ensure that the photovoltaic system can maintain the operating state near the maximum power point while adjusting the power output. The maximum power point voltage value is added to the calculated offset to obtain the first reference value of the photovoltaic MPPT controller, so as to ensure that the photovoltaic system can operate according to the new power regulation requirements. At the same time, according to the power regulation command on the energy storage side, the current reference value of the battery charge and discharge controller is calculated. Since the charge and discharge power of the battery is directly related to the battery voltage, the power regulation command on the energy storage side is divided by the current battery voltage value to calculate the actual charge and discharge current demand. Through this calculation method, it is ensured that the charge and discharge process of the battery matches the power regulation command, so as to optimize the operation state of the energy storage system and improve the overall energy utilization efficiency of the system. For example, when the system detects the photovoltaic power fluctuation, it will calculate the charge and discharge demand of the energy storage system and obtain the corresponding charge and discharge current through the battery voltage, so as to adjust the working state of the energy storage system to ensure the stability of the DC bus voltage. In order to prevent too fast voltage or current changes during the power regulation process from affecting the stability of the system, slope limit processing is performed on the calculated voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller. The maximum voltage change rate is set to ensure that the voltage reference value of the photovoltaic MPPT controller will not mutate, thus avoiding the unstable power output of the photovoltaic system due to too fast voltage changes.Similarly, a maximum current rate of change is set to ensure that the current reference value of the battery charge and discharge controller is adjusted smoothly within a reasonable range, thereby preventing the energy storage system from experiencing battery life loss or system oscillations due to excessively rapid current changes. This slope limiting process ensures the smoothness of the power regulation process and improves the operational stability and safety of the entire photovoltaic energy storage system.

[0083] Among them, the optimization objective function is optimized under the photovoltaic output power constraint and the battery state of charge constraint to obtain the photovoltaic side power regulation instruction and the energy storage side power regulation instruction, including: converting the optimization objective function into a distributed form and establishing a directed network topology, in which the photovoltaic side nodes and the energy storage side nodes are used as resource allocation units, and initializing the state of each node to obtain an initial power allocation state vector; constructing a nonlinear distributed update law that converges in a predefined time based on the initial power allocation state vector, wherein the update law contains a nonhomogeneous function with an exponential term to meet the user's preset convergence time requirements, and obtain a time-varying distributed update function; iteratively updating the state of each node according to the time-varying distributed update function, calculating the candidate value of the power regulation instruction in each round of iteration, and comparing the convergence error before and after the iteration to obtain the convergence performance evaluation result; analyzing the convergence performance evaluation result, dynamically adjusting the convergence time parameter based on the current system state, and ensuring the convergence time of the photovoltaic and energy storage systems. The convergence process is optimized while meeting the response time requirements of the energy system to obtain optimized convergence parameters; an auxiliary correction system is designed to monitor the satisfaction of the global power balance constraints in real time. When a constraint deviation occurs, the correction amount is calculated and distributed to each node to obtain a correction value that meets the global balance constraint; the correction value is applied to the candidate value of the power adjustment instruction, and the communication topology between nodes is dynamically reconstructed according to the actual operating status of the system to adapt to the rapid changes in light intensity and load demand, thereby obtaining an optimized power distribution structure; a predefined time consistency analysis is applied to the power distribution structure to ensure that each node reaches the optimal consistent state within the user-specified time, avoid the incoordination between photovoltaic output and energy storage response, and obtain a power distribution result with time determinism; the final power adjustment ratio of the photovoltaic side and the energy storage side is determined based on the power distribution result, and the ratio is quantified and converted into a discrete control signal to obtain the final photovoltaic side power adjustment instruction and the energy storage side power adjustment instruction.

[0084] The above describes the power regulation method of the photovoltaic energy storage system in the embodiment of the present invention. The following describes the power regulation device of the photovoltaic energy storage system in the embodiment of the present invention. Figure 2 , an embodiment of a power regulation device of a photovoltaic energy storage system in an embodiment of the present invention includes:

[0085] The real-time acquisition module 201 is used to collect and perform wavelet decomposition on the voltage and current parameters of the photovoltaic array in real time to obtain power fluctuation components of different frequencies and power trigger signals;

[0086] A power vector analysis module 202 is configured to perform power vector analysis based on power fluctuation components and power trigger signals at different frequencies, so as to obtain an oscillation mode characteristic parameter set and an oscillation suppression strategy.

[0087] A feedback trigger module 203 is configured to construct a photovoltaic-side feedforward trigger channel and a energy storage-side feedback trigger channel based on the oscillation mode characteristic parameter set and the oscillation suppression strategy, so as to obtain a coordinated trigger instruction for the photovoltaic side and the energy storage side.

[0088] A processing module 204 is configured to perform multi-objective optimization decomposition and slope limit processing on the coordinated trigger instruction, so as to obtain a voltage reference value for a photovoltaic MPPT controller and a current reference value for a battery charge and discharge controller.

[0089] Through the collaborative cooperation of the above-mentioned various components, through multi-order polynomial fitting and wavelet decomposition techniques, the precise identification and prediction of power fluctuation components at different frequencies are realized. Based on the quantitative analysis method of the power vector analysis model, the influence mechanism of each frequency component on the system stability is clarified. The design of the dual-channel power trigger mechanism realizes the coordinated cooperation between the photovoltaic-side feedforward and the energy storage-side feedback, and can effectively suppress power fluctuations without calculating complex power compensation values on the photovoltaic side; The power distribution strategy of multi-objective optimization takes into account both the response speed and the energy utilization efficiency, and realizes the optimal allocation of system resources; The differential processing strategy for power fluctuations with different frequency characteristics avoids the power oscillation risk caused by control mode switching; The introduction of the system operation state evaluation and control strategy optimization mechanism ensures that the system maintains the best performance state during long-term operation, and significantly improves the power stability and energy utilization efficiency of the photovoltaic energy storage system under extreme conditions such as rapid changes in illumination or sudden changes in load. [[ID=1,3]]

[0090] The above Figure 2 The power regulation device of the photovoltaic energy storage system in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the power regulation equipment of the photovoltaic energy storage system in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0091] Figure 3FIG. 0 is a schematic structural diagram of a power conditioning device of a photovoltaic energy storage system provided by an embodiment of the present invention. The power conditioning device 300 of the photovoltaic energy storage system may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 for storing application programs 333 or data 332 (for example, one or more mass storage device terminals). Among them, the memory 320 and the storage media 330 may be transient storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the power conditioning device 300 of the photovoltaic energy storage system. Further, the processor 310 may be configured to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the power conditioning device 300 of the photovoltaic energy storage system to implement the steps of the power conditioning method of the above photovoltaic energy storage system.

[0092] The power conditioning device 300 of the photovoltaic energy storage system may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 3 the shown structural diagram of the power conditioning device of the photovoltaic energy storage system does not constitute a limitation on the power conditioning device of the photovoltaic energy storage system provided by the present invention, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0093] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the power conditioning method of the photovoltaic energy storage system.

[0094] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units may refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0095] When 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 invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0096] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A power regulation method for a photovoltaic energy storage system, characterized in that, The method includes: Real-time collecting and wavelet decomposing the voltage and current parameters of the photovoltaic array to obtain power fluctuation components and power trigger signals at different frequencies; Performing power vector analysis according to the power fluctuation components at different frequencies and the power trigger signals to obtain an oscillation mode characteristic parameter set and an oscillation suppression strategy; specifically including: representing the system power in complex form and calculating the power vectors on the photovoltaic side, battery side, and load side according to the phase difference information to obtain the system power vector distribution; calculating the damping power component and synchronous power component according to the system power angle and its derivative, and evaluating the ratio of the damping power component to the synchronous power component to obtain the system power oscillation risk determination result; establishing a system state space model including a state variable vector of the DC bus voltage, battery current, photovoltaic power, and power angle and an input variable vector of the photovoltaic reference voltage and battery reference current according to the system power vector distribution and the system power oscillation risk determination result to obtain a system matrix and an input matrix; calculating the eigenvalues of the system matrix, and extracting the real part and imaginary part from the eigenvalues to obtain a system stability criterion; calculating a power oscillation evaluation index based on the ratio of the maximum real part of the eigenvalues to the minimum absolute value of the imaginary part, and respectively calculating the contribution values of the power fluctuation components at each frequency to the power oscillation evaluation index to obtain a set of high-frequency, medium-frequency, and low-frequency oscillation contribution values; determining the oscillation dominant frequency component according to the set of high-frequency, medium-frequency, and low-frequency oscillation contribution values, and selecting the corresponding oscillation suppression strategy based on the oscillation dominant frequency component, and simultaneously outputting the oscillation mode characteristic parameter set; Constructing a photovoltaic-side feedforward trigger channel and an energy storage-side feedback trigger channel based on the oscillation mode characteristic parameter set and the oscillation suppression strategy to obtain coordinated trigger commands for the photovoltaic side and the energy storage side; Performing multi-objective optimization decomposition and slope limit processing on the coordinated trigger commands to obtain the voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller.

2. The power regulation method of the photovoltaic energy storage system according to claim 1, wherein The real-time collecting and wavelet decomposing the voltage and current parameters of the photovoltaic array to obtain power fluctuation components and power trigger signals at different frequencies includes: Collecting the output voltage value and output current value of the photovoltaic array through a voltage sensor and a current sensor arranged at the output end of the photovoltaic array, and performing a product operation on the output voltage value and output current value to obtain the photovoltaic output power; Collecting the DC bus voltage value through a voltage sensor arranged on the DC bus, and calculating the difference between the DC bus voltage value and a preset DC bus voltage reference value to obtain the DC bus voltage deviation; Performing adaptive band-pass filtering on the DC bus voltage deviation to obtain a power fluctuation characteristic signal; Performing multi-order polynomial fitting and wavelet decomposition based on the photovoltaic output power and the power fluctuation characteristic signal to obtain power fluctuation components and power trigger signals at different frequencies.

3. The power regulation method of the photovoltaic energy storage system according to claim 2, characterized in that, The performing multi-order polynomial fitting and wavelet decomposition based on the photovoltaic output power and the power fluctuation characteristic signal to obtain power fluctuation components and power trigger signals at different frequencies includes: Perform a fifth-order polynomial fitting on the historical data sequence of the photovoltaic output power, and update the polynomial coefficients by the recursive least squares method to obtain the predicted value of the photovoltaic output power for the next sampling period; Perform weighted moving average and exponential smoothing on the predicted value of the photovoltaic output power for the next sampling period, and assign weight coefficients for weighted calculation to obtain the medium-term power prediction value; According to the medium-term power prediction value, perform wavelet decomposition on the power fluctuation characteristic signal, and extract the high-frequency component, intermediate-frequency component and low-frequency component respectively to obtain the power fluctuation components of different frequencies; Based on the amplitudes of the power fluctuation components of different frequencies, assign adaptive weight coefficients to the power fluctuation components of each frequency and perform weighted summation to obtain the power compensation demand; Calculate according to the ratio of the power compensation demand to the rated power of the system to obtain the power stability index; Compare and judge the power stability index with a preset threshold, and generate a power trigger signal when the power stability index is lower than the preset threshold.

4. The power regulation method of the photovoltaic energy storage system according to claim 1, wherein Determine the dominant oscillation frequency component according to the high-frequency, intermediate-frequency and low-frequency oscillation contribution value sets, and select the corresponding oscillation suppression strategy based on the dominant oscillation frequency component, and output the oscillation mode characteristic parameter set at the same time, including: Compare the magnitudes of the high-frequency, intermediate-frequency and low-frequency oscillation contribution value sets and perform normalization processing, and determine the frequency component corresponding to the maximum contribution value as the dominant oscillation frequency component; Perform strategy matching based on the dominant oscillation frequency component. When the high-frequency component is dominant, select a fast-response power compensation strategy. When the intermediate-frequency component is dominant, select a smooth-transition power distribution strategy. When the low-frequency component is dominant, select a long-time constant power filtering strategy to obtain the oscillation suppression strategy; Perform singular value decomposition on the power fluctuation time-domain signal corresponding to the dominant frequency component to obtain the oscillation mode eigenvalue; perform fast Fourier transform on the power fluctuation signal of the dominant frequency component to obtain the oscillation frequency eigenvalue; calculate the phase difference between the dominant frequency component and the adjacent frequency component to obtain the oscillation phase eigenvalue; Combine the oscillation mode eigenvalue, the oscillation frequency eigenvalue and the oscillation phase eigenvalue into an oscillation mode characteristic parameter set.

5. The power regulation method of the photovoltaic energy storage system according to claim 1, wherein Construct a photovoltaic-side feedforward trigger channel and a storage-side feedback trigger channel based on the oscillation mode characteristic parameter set and the oscillation suppression strategy to obtain the coordinated trigger instructions for the photovoltaic side and the storage side, including: Calculate the feedforward compensation amount according to the difference between the predicted value of the photovoltaic output power and the current photovoltaic output power, and set a feedforward gain for the feedforward compensation amount, where the feedforward gain is dynamically adjusted according to the high-frequency oscillation contribution value in the oscillation mode characteristic parameter set, and construct a photovoltaic-side feedforward trigger channel; Calculate the feedback compensation amount based on the DC bus voltage deviation and its change rate, and set a proportional gain and a derivative gain for the feedback compensation amount, where the proportional gain and the derivative gain are dynamically adjusted according to the intermediate-frequency and low-frequency oscillation contribution values in the oscillation mode characteristic parameter set, and construct a storage-side feedback trigger channel; Using the photovoltaic side feed-forward trigger channel, a photovoltaic side trigger command is generated according to the comparison result of the absolute value of the feed-forward compensation amount with the feed-forward threshold or the comparison result of its change rate with the change rate threshold; Using the energy storage side feedback trigger channel, an energy storage side trigger command is generated according to the comparison result of the absolute value of the feedback compensation amount with the feedback threshold or the judgment result that the DC bus voltage deviation exceeds the safe range; Combining the photovoltaic side trigger command and the energy storage side trigger command, and dynamically adjusting the coordination coefficient according to the oscillation suppression strategy to obtain a coordinated trigger command.

6. The power regulation method of the photovoltaic energy storage system according to claim 1, wherein The multi-objective optimization decomposition and slope limit processing of the coordinated trigger command to obtain the voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller includes: Multiplying the coordinated trigger command by a power ratio factor for conversion to obtain the total power regulation requirement; Based on the total power regulation requirement, an optimization objective function considering the response speed objective function and the energy loss objective function is established; Under the photovoltaic output power constraint condition and the battery state of charge constraint condition, the optimization objective function is optimized to obtain the photovoltaic side power regulation command and the energy storage side power regulation command; According to the photovoltaic side power regulation command and the photovoltaic output characteristic curve, look-up table mapping is performed to calculate the maximum power point voltage and the offset, and the maximum power point voltage is added to the offset to obtain the first reference value of the photovoltaic MPPT controller; Dividing the energy storage side power regulation command by the battery voltage value to calculate the second reference value of the battery charge and discharge controller; Performing slope limit processing on the first reference value and the second reference value, limiting the change rate of the first reference value within the maximum voltage change rate range, and limiting the change rate of the second reference value within the maximum current change rate range to obtain the voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller.

7. A power regulation device for a photovoltaic energy storage system, characterized in that, For implementing the power regulation method of the photovoltaic energy storage system according to any one of claims 1-6, the power regulation device of the photovoltaic energy storage system includes: A real-time acquisition module for real-time acquisition and wavelet decomposition of the voltage and current parameters of the photovoltaic array to obtain power fluctuation components and power trigger signals of different frequencies; A power vector analysis module for performing power vector analysis according to the power fluctuation components of different frequencies and the power trigger signals to obtain an oscillation mode characteristic parameter set and an oscillation suppression strategy; A feedback trigger module for constructing a photovoltaic side feed-forward trigger channel and an energy storage side feedback trigger channel based on the oscillation mode characteristic parameter set and the oscillation suppression strategy to obtain a coordinated trigger command for the photovoltaic side and the energy storage side; A processing module for performing multi-objective optimization decomposition and slope limit processing on the coordinated trigger command to obtain the voltage reference value of the photovoltaic MPPT controller and the current reference value of the battery charge and discharge controller.

8. A power regulation device for a photovoltaic energy storage system, characterized in that, The power regulation device of the photovoltaic energy storage system includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor invokes the instructions in the memory to cause a power conditioning device of the photovoltaic energy storage system to execute the power conditioning method of the photovoltaic energy storage system according to any one of claims 1-6.

9. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are executed by the processor, the power conditioning method of the photovoltaic energy storage system according to any one of claims 1-6 is implemented.

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