Photovoltaic energy storage inverter power coordination control method based on adaptive algorithm

Abstract

The invention provides a photovoltaic energy storage inverter power coordination control method based on an adaptive algorithm, and relates to the technical field of data processing, and the method comprises the steps: collecting the output power data of a photovoltaic module, the state data of an energy storage device, and the load demand data of a grid connection point, and carrying out the normalization processing of the data; calculating to obtain an initial power distribution parameter of the current period; respectively calculating a load change trend factor and a power fluctuation trend factor based on the load demand data and the output power data of the historical period, and coupling the load change trend factor and the power fluctuation trend factor to form a trend coupling factor; carrying out self-adaptive repair through a trend coupling factor; combining real-time grid-connected point load demand data with energy storage device state data to generate a target power distribution instruction and an energy storage charging and discharging adjustment instruction, and respectively issuing the instructions to an inverter control unit and an energy storage device control unit; according to the invention, the accuracy of the photovoltaic energy storage inverter power coordination control method is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a power coordination control method for a photovoltaic energy storage inverter based on an adaptive algorithm. Background Art

[0002] Currently, mainstream power coordination control methods for PV-energy-storage inverters typically employ fixed-weight allocation, fuzzy control, or prediction-based scheduling strategies. These methods dynamically adjust the inverter's output power by real-time monitoring of PV output, energy storage status, and load demand, combined with pre-defined control models, to ensure grid-connected stability and power quality. These methods can achieve relatively stable power distribution when light and load fluctuations are relatively small.

[0003] However, in scenarios with large distributed photovoltaic installations and multiple parallel energy storage systems, the controlled parameters are numerous and fluctuate dramatically. For example, in predictive control, when peak loads in an industrial park coincide with sudden cloud cover, existing control methods are prone to energy storage response delays due to parameter update lags and insufficient model generalization. This can cause the inverter output to momentarily deviate from the setpoint and even trigger a protective inverter shutdown. Summary of the Invention

[0004] The purpose of the present invention is to provide a photovoltaic energy storage inverter power coordination control method based on an adaptive algorithm, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows: A photovoltaic energy storage inverter power coordination control method based on an adaptive algorithm, the method comprising: Collect the output power data of photovoltaic modules, the status data of energy storage devices and the load demand data of grid connection points, and normalize them to obtain the basic data for distribution parameter calculation; Based on the basic data, calculate and obtain the preliminary power allocation parameters of the current cycle; Based on the load demand data and output power data of the historical period, the load change trend factor and the power fluctuation trend factor are calculated respectively, and the two are coupled to form a trend coupling factor; Adaptively modify the preliminary power allocation parameters through the trend coupling factor to generate modified target power allocation parameters; According to the target power allocation parameters, combined with the real-time grid-connected point load demand data and energy storage device status data, the target power allocation instructions and energy storage charge and discharge adjustment instructions are generated and sent to the inverter control unit and energy storage device control unit respectively.

[0006] Preferably, calculating and obtaining preliminary power allocation parameters for the current cycle based on basic data includes: Based on the basic data, each data item is differentiated from the mean value of the corresponding data item in the previous period to obtain a differential value data set; According to the differential value data set, the differential value of each data item is weighted and superimposed according to the preset data allocation weight coefficient to obtain a weighted processing value data set; The weighted processed values ​​are added to form a composite parameter, and the theoretical load demand is inferred from the composite parameter, and the difference between the theoretical load demand and the actual load demand is calculated to obtain the power distribution deviation; According to the power allocation deviation, the total power to be allocated is allocated to each power source in proportion to the absolute value of the power allocation deviation, and the preliminary power allocation parameters of each power source are obtained.

[0007] Preferably, based on the load demand data and output power data of the historical period, the load change trend factor and the power fluctuation trend factor are calculated respectively, and the two are coupled to form a trend coupling factor, including: Perform time series analysis on a set number of historical period load demand data, calculate the rate of change between every two adjacent period load demand data, and average all the change rates to obtain the mean change rate; Perform polynomial fitting on the historical period load demand data, extract the slope of the fitting curve, and weight the slope value and the mean value of the rate of change according to the preset trend factor synthesis weight coefficient to form a load change trend factor; The historical period output power data is processed step by step using a sliding window. The variance and range of the output power are calculated in each window and multiplied by the preset variance weight coefficient and range weight coefficient respectively. The weighted variance value and range value are added together to obtain the initial value of the power fluctuation trend factor. Directional analysis is performed on the initial value of the power fluctuation trend factor, and the duration of the fluctuation increase or decrease is counted. Based on the correlation between the duration period and the initial value of the power fluctuation trend factor, a preset duration period weight coefficient and an initial value weight coefficient are assigned respectively, and the two are weighted and superimposed to form the power fluctuation trend factor; The load change trend factor and the power fluctuation trend factor are weighted and summed according to the preset coupling weight coefficient. If the two are in the same direction, the coupling weight is increased, and if the directions are opposite, the coupling weight is reduced to generate a trend coupling factor.

[0008] Preferably, the preliminary power allocation parameters are adaptively corrected by the trend coupling factor to generate corrected target power allocation parameters, including: Determining whether the magnitude of the trend coupling factor exceeds a preset trend coupling threshold; When it exceeds, the trend coupling factor is multiplied by the preliminary power allocation parameter item by item to obtain the first correction value of each power source allocation parameter; Based on the first correction value, combined with the current cycle load demand change and the available capacity of the energy storage device, the two are weighted and superimposed on each first correction value according to a preset feedback adjustment weight coefficient to obtain a second correction value; All second correction values ​​are summed and then normalized to ensure that the sum of the allocation parameters is equal to the current allocable total power, thereby generating target power allocation parameters after multi-level adaptive correction and normalization.

[0009] Preferably, according to the target power allocation parameters, combined with the real-time grid connection point load demand data and the energy storage device status data, the target power allocation instructions and the energy storage charge and discharge adjustment instructions are generated, including: Obtain the grid connection point load demand data of the current cycle in real time, calculate the difference between the target power allocation parameter and the load demand data, and distribute the difference to each power source according to the allocation ratio of the allocation parameter to form the target power allocation instruction; At the same time, the system monitors the energy storage device's current remaining capacity, charge and discharge power upper limit, and temperature safety range. Combining the target power allocation parameters with the load demand margin, it determines whether the energy storage device needs to be charged or discharged in the current cycle, sets the charge and discharge power values ​​accordingly, and generates energy storage charge and discharge adjustment instructions. Send the target power allocation instruction to the inverter control unit, and the energy storage charge and discharge adjustment instruction to the energy storage device control unit, driving the relevant equipment to work in coordination according to their respective instructions; Monitor the inverter output power and the actual charging and discharging status of the energy storage device, and compare them with the target instructions in real time. When deviation occurs, automatic feedback is given and the initial power allocation parameters are automatically returned to recalculate to achieve continuous closed-loop adaptive optimization.

[0010] Preferably, a polynomial fitting is performed on the historical period load demand data to extract the slope of the fitting curve, and the slope value and the mean value of the change rate are weighted and superimposed according to a preset trend factor synthesis weight coefficient to form a load change trend factor, including: According to the historical period load demand data, each data item is arranged in chronological order to form a time series sample; Apply a polynomial fitting algorithm to the time series samples to obtain a curve reflecting the load change trend, and extract the slope value of the curve in the latest cycle; According to the preset trend factor synthesis weight coefficient, the slope value and the average value of the change rate are multiplied by their respective weight coefficients, and the two products are accumulated to generate the load change trend factor of the current cycle.

[0011] Preferably, a directional analysis is performed on the initial value of the power fluctuation trend factor, and the duration period of the fluctuation increase or decrease is counted. According to the correlation between the duration period and the initial value of the power fluctuation trend factor, a preset duration period weight coefficient and an initial value weight coefficient are assigned respectively, and the two are weighted and superimposed to form the power fluctuation trend factor, including: Based on the initial value of the power fluctuation trend factor, the output power data of the historical period is compared cycle by cycle to identify and count the number of cycles with continuous same-direction rise or fall changes to obtain the continuous cycle length; According to the duration period length and the initial value of the power fluctuation trend factor, and based on their influence in previous adjustments, the duration period weight coefficient and the initial value weight coefficient are set; The power fluctuation trend factor of the current cycle is obtained by multiplying the duration period length by the duration period weight coefficient, and multiplying the initial value of the power fluctuation trend factor by the initial value weight coefficient, and adding the two weighted results.

[0012] Preferably, according to the first correction value, combined with the current cycle load demand change and the available capacity of the energy storage device, the two are weighted and superimposed on each first correction value according to a preset feedback adjustment weight coefficient to obtain the second correction value, including: Obtain the current cycle load demand change and the available capacity of the energy storage device, and assign feedback adjustment weight coefficients to each of them. The feedback adjustment weight coefficients are dynamically adjusted based on the historical contribution of each parameter to power distribution stability; Multiply the load demand change by its feedback adjustment weight coefficient, and multiply the available capacity of the energy storage device by its feedback adjustment weight coefficient to obtain weighted feedback values ​​respectively; Each first correction value is added to the corresponding weighted feedback value to obtain a second correction value of each power source in the current cycle.

[0013] Preferably, the load demand data of the grid connection point in the current cycle is obtained in real time, the difference between the target power allocation parameter and the load demand data is calculated, and the difference is allocated to each power source according to the allocation ratio of the allocation parameter to form a target power allocation instruction, including: Obtain the grid connection point load demand data of the current cycle in real time, compare it with the target power allocation parameters of the current cycle item by item, calculate the difference between the theoretical output power of each power source and the actual load demand, and obtain the power difference that needs to be adjusted for each power source; The power difference of each power source is added together to obtain the total power difference; According to the proportion of each power source in the target power allocation parameter, the total power difference is proportionally distributed to each power source to form their respective allocation adjustment amounts; Combine the output history data and dynamic response capability of each power source in the previous cycle to determine the correction coefficient of each power source allocation adjustment, and add the corrected allocation adjustment to its target power allocation parameter to obtain the final target output value; The final target output value is safety checked. If it exceeds the maximum or minimum power range allowed by the device, it is automatically corrected to the boundary value allowed by the device; The final target output value after safety verification is used as the target power allocation instruction for the current cycle.

[0014] Preferably, the current remaining power of the energy storage device, the upper limit of the charge and discharge power, and the temperature safety range are monitored simultaneously. In combination with the target power allocation parameters and the load demand margin, it is determined whether the energy storage device needs to be charged or discharged in the current cycle, and the charge and discharge power values ​​are set accordingly to generate energy storage charge and discharge adjustment instructions, including: Real-time monitoring of the remaining power, charge and discharge power limits, and actual temperature of the energy storage device during the current cycle to form an energy storage status data input set; Comprehensively analyzing the energy storage state data input set with the target power allocation parameters of the current cycle and the load demand margin of the current cycle to determine whether the energy storage device should adopt the charging mode or the discharging mode in the current cycle, thereby obtaining an operation mode determination result; Based on the result of the operation mode determination, the charging or discharging direction of the energy storage device in the current cycle is determined, and the actual executable charging and discharging power values ​​are set based on the remaining power, the upper power limit of the equipment, and the temperature safety range. The safety and rationality of the charge and discharge power values, target power allocation parameters, and actual load requirements are checked. If the value exceeds the equipment limit or poses a safety hazard, it is automatically adjusted to the maximum or minimum value within the equipment's allowable range to generate a corrected charge and discharge power value. According to the corrected charge and discharge power values, the energy storage charge and discharge adjustment instructions for the current cycle are generated.

[0015] The above solution of the present invention includes at least the following beneficial effects: Through an adaptive algorithm, real-time normalization of PV module output power data, energy storage device status data, and grid-connection point load demand data dynamically reflects the current operating status of each power source and provides a unified, standardized calculation basis for power allocation. Compared to existing solutions that use fixed weights or static control models, this invention not only adapts to the complex variations of multi-source data under different operating conditions, but also enhances the algorithm's sensitivity and responsiveness to extreme loads or fluctuating light conditions.

[0016] Combining the trend factor analysis of historical period load demand and output power, the present invention uses adaptive correction and trend coupling mechanisms to enable the target power allocation parameters to actively track the actual load and power fluctuations, thereby achieving dynamic optimization and adjustment of the output of each power source. In the case of large-scale access to distributed photovoltaics, multi-point parallel connection of energy storage, and rapid changes in grid load, it can effectively alleviate the energy storage response delay problem caused by model lag and insufficient generalization ability, and reduce the risk of protective shutdown of the inverter due to power deviation from the set value. For example, when the energy consumption in an industrial park is at its peak and there is a sudden cloud cover, the power allocation strategy can be corrected in time to ensure the smooth output of the inverter and significantly improve the safety and reliability of the grid operation.

[0017] Furthermore, the adaptive multi-level closed-loop adjustment mechanism employed by this invention adjusts power allocation instructions and energy storage charge and discharge control instructions in real time based on current operational feedback, achieving dynamic adaptation and closed-loop optimization throughout the entire process. This mechanism not only improves power allocation accuracy in complex scenarios but also significantly enhances the PV energy storage inverter's resilience and stable operation in the face of extreme disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flowchart of a photovoltaic energy storage inverter power coordination control method based on an adaptive algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0020] like Figure 1 As shown, an embodiment of the present invention proposes a photovoltaic energy storage inverter power coordination control method based on an adaptive algorithm, the method comprising: Collect the output power data of photovoltaic modules, the status data of energy storage devices and the load demand data of grid connection points, and normalize them to obtain the basic data for distribution parameter calculation; Based on the basic data, calculate and obtain the preliminary power allocation parameters of the current cycle; Based on the load demand data and output power data of the historical period, the load change trend factor and the power fluctuation trend factor are calculated respectively, and the two are coupled to form a trend coupling factor; Adaptively modify the preliminary power allocation parameters through the trend coupling factor to generate modified target power allocation parameters; According to the target power allocation parameters, combined with the real-time grid-connected point load demand data and energy storage device status data, the target power allocation instructions and energy storage charge and discharge adjustment instructions are generated and sent to the inverter control unit and energy storage device control unit respectively.

[0021] In this embodiment of the present invention, by collecting and normalizing PV module output power data, energy storage device status data, and grid-connected point load demand data, a unified dimensional basis is provided for subsequent power allocation calculations, eliminating the uncertainty caused by differences in units and amplitudes between different data sources and ensuring that all types of data can be efficiently integrated and processed within the same computing system. The system utilizes this basic data, combined with historical cycle load demand and output power data, to extract load change trend factors and power fluctuation trend factors, respectively. Through a trend coupling mechanism, it dynamically reflects the combined impact of load changes and power fluctuations within the current cycle, effectively enhancing its perception and adaptability to complex grid conditions.

[0022] Adaptively modifying the initial power allocation parameters using trend coupling factors enables dynamic adjustment of the coordinated output of multiple power sources. This provides greater flexibility and responsiveness to target power allocation parameters in scenarios such as large-scale distributed photovoltaic access, multi-point parallel energy storage systems, and frequent load fluctuations. For example, in situations such as sudden load increases or sudden drops in sunlight in industrial parks, the system can automatically adjust the coordinated output ratio between the inverter and energy storage devices, significantly reducing the risk of output deviation and protective shutdowns, thereby improving system operational safety and reliability.

[0023] Furthermore, by combining target power allocation parameters with real-time grid-connected load demand data and energy storage device status data, target power allocation instructions and energy storage charge and discharge adjustment instructions are generated and issued, enabling closed-loop dynamic optimization of the entire process. Through real-time monitoring and adaptive correction, the entire system continuously optimizes the output of each power source, significantly improving the accuracy of power allocation and enhancing the comprehensive utilization efficiency and anti-disturbance capabilities of the new energy system.

[0024] The output power data of photovoltaic modules, the status data of energy storage devices and the load demand data of grid connection points are collected and normalized to obtain the basic data for distribution parameter calculation, including: In actual system operation, the output power data of the photovoltaic components in each sampling period is obtained through power acquisition sensors, the status data of the energy storage device such as the remaining power, charging and discharging status is obtained through the status monitoring unit, and the real-time load demand data of the grid connection point is obtained through the power consumption monitoring device. For data of different types and different physical magnitudes, in order to facilitate subsequent unified modeling and analysis, all collected data are first normalized according to preset maximum values, minimum values ​​or historical average values, so that each data item is converted into a dimensionless standard value. For example, the method of subtracting the minimum historical value of each collected value from the data item and then dividing it by the range of its interval can be used to normalize all data to between 0 and 1. After normalization, a basic data set containing multi-source normalized values ​​such as photovoltaic components, energy storage devices and grid connection point loads is obtained, providing a unified and comparable input data basis for subsequent power allocation parameter calculations.

[0025] The inverter control unit specifically includes: The inverter control unit (ICU) receives target power allocation instructions from the system host computer or dispatch center and adjusts the inverter's output power in real time based on the instructions. This unit comprises a digital signal processing module, an inverter main control board, and a communication interface. In actual implementation, the ICU first receives the target power allocation instruction via a communication interface (such as CAN, Modbus, or Ethernet). Upon receiving the instruction, the ICU automatically adjusts the conduction state of the inverter bridge arms and the PWM control signals based on the specified target output power and currently collected grid parameters (such as voltage, current, and frequency), thereby achieving dynamic energy flow between the DC and AC sides of the inverter.

[0026] To ensure the accuracy and safety of command execution, the inverter control unit also monitors the synchronization status of output power and the grid in real time. If a deviation between actual output power and command is detected, or if system operating parameters exceed safety thresholds, the inverter automatically adjusts the output strategy through a feedback loop or issues an alarm to the system. The entire control process requires no human intervention, ensuring the inverter's rapid, stable, and closed-loop response to system dispatch commands.

[0027] The energy storage device control unit specifically includes: The energy storage device control unit is used to receive energy storage charge and discharge adjustment instructions and precisely control the charging and discharging processes and power of the energy storage device based on the instructions. This unit consists of a battery management system (BMS), a power regulation module, and a communication interface. During implementation, the energy storage device control unit receives charging and discharging power and operating mode (charging or discharging) instructions from the dispatch center via wired or wireless communication. After receiving the instructions, the BMS module first determines the current energy storage unit's power level, temperature, safety range, and other status information to confirm that the device is within the adjustable range. If all conditions meet safety conditions, the power regulation module is controlled to switch the energy storage device to the corresponding charging or discharging mode according to the instructions, and accurately adjusts the actual charging and discharging power to ensure that the maximum power and maximum / minimum SOC limits allowed by the device are not exceeded.

[0028] During operation, the energy storage device control unit continuously monitors the battery pack's current, voltage, temperature, and other parameters. If it detects a significant deviation between the actual charge and discharge process and the control instructions, or if a parameter exceeds the safe range, the system automatically triggers a protection mechanism, adjusts or terminates the charge and discharge process, and feeds back the abnormal information to the upper system, achieving dynamic closed-loop management and multi-level protection for the energy storage device's charge and discharge process.

[0029] In a preferred embodiment of the present invention, calculating and obtaining preliminary power allocation parameters for the current cycle based on basic data includes: Based on the basic data, each data item is differentiated from the mean value of the corresponding data item in the previous period to obtain a differential value data set; According to the differential value data set, the differential value of each data item is weighted and superimposed according to the preset data allocation weight coefficient to obtain a weighted processing value data set; The weighted processed values ​​are added to form a composite parameter, and the theoretical load demand is inferred from the composite parameter, and the difference between the theoretical load demand and the actual load demand is calculated to obtain the power distribution deviation; According to the power allocation deviation, the total power to be allocated is allocated to each power source in proportion to the absolute value of the power allocation deviation, and the preliminary power allocation parameters of each power source are obtained.

[0030] In an embodiment of the present invention, preliminary power allocation parameters for the current cycle are calculated based on basic data, which can achieve dynamic allocation optimization of different power sources. By performing differential processing on the basic data and the mean value of the data in the previous cycle, the changing trend of each power source can be reflected in real time, and then the differential result is weighted in combination with the preset data allocation weight coefficient, which fully considers the power allocation contribution of various parameters in the historical cycle and obtains a more scientific weighted processing value. These weighted processing values ​​are used to reversely infer the theoretical load demand and compare it with the actual load demand to obtain a power allocation deviation that is more in line with the actual working conditions. Furthermore, the total power is allocated according to the absolute value ratio of the power allocation deviation, so that the preliminary allocation parameters of each power source can flexibly reflect the current supply and demand relationship of the system. For example, if the status data of the energy storage device in a certain cycle is relatively superior, its allocation parameters will be automatically increased to improve the power supply elasticity of the entire system.

[0031] The preset data allocation weight coefficients specifically include: To reasonably reflect the influence of various data items (such as photovoltaic output power, energy storage status, and grid-connected load) in power allocation, the system assigns a weight coefficient to each data item. This weight coefficient can be initially set during the initial system deployment based on historical data analysis, system structure design, or actual operating experience. For example, for energy storage units with fast response speed and high energy density, their allocation weight can be set to a higher value; for photovoltaic output with strong volatility and susceptibility to external influences, a lower weight is assigned. In subsequent operation, the weight coefficient can also be dynamically adjusted based on indicators such as historical power allocation errors and the difference between target and actual response, so that each weight can better reflect the contribution of the current system operating status to the allocation accuracy. Ultimately, each weight coefficient is used to weight each data item in the differential value data set to ensure that the allocation result takes into account the actual capabilities and current status of each power source.

[0032] The theoretical load demand is inferred from the synthesized parameters, and the difference between the theoretical load demand and the actual load demand is calculated to obtain the power distribution deviation, which includes: After weighting and superimposing each normalized data item, the system obtains the synthetic parameter of the current cycle, which can be understood as the theoretical comprehensive output capacity of each power source after weighting. Subsequently, the system compares the theoretical load demand corresponding to the synthetic parameter with the grid connection point load demand data actually monitored in this cycle by reverse deduction. Specifically, the synthetic parameter is restored to the physical magnitude of the theoretical load demand by normalized reverse transformation, and then the actual load demand value is differenced with the theoretical value to obtain the power distribution deviation reflecting the difference between actual demand and theoretical supply. This deviation is used to guide the subsequent allocation priority and adjustment range of the total power among the power sources, so as to dynamically balance the degree of matching between the output of each source and the actual energy consumption of the system.

[0033] The total power to be allocated specifically includes: The total power to be allocated refers to the maximum effective power that the system can deploy or allocate during the current cycle, based on the load demand and the actual available capacity of each power source. In specific implementation, the total power budget available to the system for this cycle is first calculated based on the actual load demand and the maximum output power ceiling of each power source (such as photovoltaics and energy storage). It is important to note that the total power to be allocated cannot exceed the total load demand of the system or the physical limits of the output capacity of each power source. For example, when the load demand is low and there is sufficient energy storage, the total power to be allocated is determined by the load; when the load is peak and some power sources are current-limited, the total power to be allocated is determined by the power source capacity ceiling, ensuring allocation safety and system stability.

[0034] In a preferred embodiment of the present invention, based on the load demand data and output power data of the historical period, the load change trend factor and the power fluctuation trend factor are calculated respectively, and the two are coupled to form a trend coupling factor, including: Perform time series analysis on a set number of historical period load demand data, calculate the rate of change between every two adjacent period load demand data, and average all the change rates to obtain the mean change rate; Perform polynomial fitting on the historical period load demand data, extract the slope of the fitting curve, and weight the slope value and the mean value of the rate of change according to the preset trend factor synthesis weight coefficient to form a load change trend factor; The historical period output power data is processed step by step using a sliding window. The variance and range of the output power are calculated in each window and multiplied by the preset variance weight coefficient and range weight coefficient respectively. The weighted variance value and range value are added together to obtain the initial value of the power fluctuation trend factor. Directional analysis is performed on the initial value of the power fluctuation trend factor, and the duration of the fluctuation increase or decrease is counted. Based on the correlation between the duration period and the initial value of the power fluctuation trend factor, a preset duration period weight coefficient and an initial value weight coefficient are assigned respectively, and the two are weighted and superimposed to form the power fluctuation trend factor; The load change trend factor and the power fluctuation trend factor are weighted and summed according to the preset coupling weight coefficient. If the two are in the same direction, the coupling weight is increased, and if the directions are opposite, the coupling weight is reduced to generate a trend coupling factor.

[0035] In an embodiment of the present invention, based on the load demand data and output power data of the historical period, the load change trend factor and the power fluctuation trend factor are calculated respectively, and the two are coupled to form a trend coupling factor, which can significantly enhance the system's adaptive perception and response capabilities to load changes and power fluctuations. Specifically, by using historical data time series analysis and polynomial fitting to extract the load change trend factor, the overall change direction and rate of the load can be captured in a timely manner. At the same time, the power fluctuation trend factor is extracted through statistical methods such as sliding windows to effectively reflect the short-term fluctuation characteristics of the output power. After the two are weighted and coupled, a trend coupling factor that takes into account both global and local changes is formed, which can serve as an important reference for subsequent adaptive corrections. For example, within a certain period, if the historical data shows a trend of continuous increase in load and intensified power fluctuations, the trend coupling factor will amplify the correction weight, making the allocation plan more robust and effectively reducing the actual risks such as inverter overload and frequent switching of energy storage devices.

[0036] The time series analysis is performed on a set number of historical period load demand data to calculate the rate of change between two adjacent period load demand data, specifically including: First, set a historical analysis window in the system and determine the number of historical cycles that need to be counted, such as the last ten sampling cycles. Arrange the load demand data within these cycles in chronological order to obtain a set of continuous load demand data points. Then, starting from the second data point, subtract the data of the previous cycle from the data of the current cycle, and then perform ratio processing on the difference with the data of the previous cycle to obtain the rate of change between each pair of adjacent cycle load demand data. Finally, all the change rates are counted as a sequence to provide basic data for subsequent trend analysis and factor extraction. For example, if the load data of the last five cycles are A, B, C, D, and E, then when calculating the rate of change, use B minus A divided by A, C minus B divided by B, and so on, to obtain four sets of change rate results.

[0037] Among them, the preset trend factor synthesis weight coefficients specifically include: The trend factor synthesis weight coefficient is used to weight and superimpose trend indicators from different sources (such as the slope of the fitting curve, the mean rate of change, etc.) according to their importance in the overall trend judgment. When the system is first deployed, the weight coefficient of each trend indicator can be empirically set based on historical operating conditions and data fluctuation characteristics. For example, in scenarios where the load changes are relatively stable over the long term and there are occasional large fluctuations, a higher weight can be set for the slope of the fitting curve, while a lower weight can be set for the mean rate of change. During operation, these weight coefficients can also be dynamically adjusted based on the actual prediction error, response effect, and other conditions of the system to more accurately reflect the contribution of the trend factor to power distribution optimization. Ultimately, the role of the trend factor synthesis weight coefficient is to reasonably integrate the influence of each individual trend parameter into an overall trend judgment result.

[0038] Among them, the preset variance weight coefficient and range weight coefficient specifically include: The variance weight coefficient and the range weight coefficient are mainly used for multi-dimensional evaluation of the fluctuation characteristics of the output power data of the historical period. The system first performs sliding window processing on the output power data of the historical period within the set window, and calculates the variance (reflecting the degree of dispersion of the fluctuation) and the range (reflecting the fluctuation range of the maximum and minimum values) of the output power in each window. The variance weight coefficient is used to measure the contribution of data dispersion in the judgment of power fluctuation trends; the range weight coefficient measures the influence of the maximum fluctuation amplitude. The two weights can be configured by the system administrator or the algorithm in an adaptive manner based on the system's sensitivity to stability or extreme fluctuations. By multiplying the variance and range of each window by the corresponding weight and superimposing them, a more comprehensive and objective basic data of the power fluctuation trend factor is formed. For example, if the system is particularly concerned about sudden and large fluctuations, a higher value can be assigned to the range weight.

[0039] The preset coupling weight coefficients specifically include: The coupling weight coefficient is used to weight the load change trend factor and the power fluctuation trend factor to form a more comprehensive trend coupling factor. During the system setting stage, the coupling weight coefficient can be set to a basic value based on actual operating experience and the performance of each factor in the system power distribution optimization. For example, in scenarios with large fluctuations but slow load changes, a higher coupling weight can be assigned to the power fluctuation trend factor. The coupling weight coefficient can also change dynamically with the system operating status. For example, when the change directions of the two trend factors are consistent, the coupling weight is automatically increased to highlight the system's rapid response to drastic changes; when the two trends are in opposite directions, the coupling weight is appropriately reduced to prevent over-correction. Through this dynamic adjustment, it is ensured that the system can respond quickly to drastic changes while avoiding unnecessary adjustment interference, thereby improving the stability and reliability of the overall power distribution.

[0040] In a preferred embodiment of the present invention, the preliminary power allocation parameters are adaptively modified by the trend coupling factor to generate the modified target power allocation parameters, including: Determining whether the magnitude of the trend coupling factor exceeds a preset trend coupling threshold; When it exceeds, the trend coupling factor is multiplied by the preliminary power allocation parameter item by item to obtain the first correction value of each power source allocation parameter; Based on the first correction value, combined with the current cycle load demand change and the available capacity of the energy storage device, the two are weighted and superimposed on each first correction value according to a preset feedback adjustment weight coefficient to obtain a second correction value; All second correction values ​​are summed and then normalized to ensure that the sum of the allocation parameters is equal to the current allocable total power, thereby generating target power allocation parameters after multi-level adaptive correction and normalization.

[0041] In an embodiment of the present invention, the preliminary power allocation parameters are adaptively corrected by the trend coupling factor, which enables the power allocation process to dynamically adapt to the complex fluctuations of the grid load and multiple input sources such as photovoltaics and energy storage, thereby achieving more refined and robust power coordination control. When the trend coupling factor exceeds the preset threshold, the system can automatically identify significant changes in the current cycle without manual intervention and promptly start the adaptive correction process. After the power source allocation parameters are adjusted by the trend coupling factor, they are multi-weighted in combination with the current cycle load demand change and the available capacity of the energy storage device, and finally normalized to ensure that the total power output is reasonable. In this way, the response speed and stability of the inverter in high-frequency fluctuation scenarios can be significantly improved. For example, in the scenario of sudden increase in load and sudden change in light intensity, this embodiment can timely adjust the output of each power source to prevent a single source from causing system instability due to excessive fluctuations.

[0042] The preset trend coupling thresholds include: In actual engineering applications, before the system performs adaptive power allocation adjustments, it must first set a trend coupling threshold. This threshold determines whether the trend coupling factor in the current cycle has reached the conditions that require adaptive adjustments. The trend coupling threshold can be set based on historical operating data, system adjustment experience, or preset according to the system operating environment. For example, the typical fluctuation range of the trend coupling factor over a period of time can be statistically analyzed, and the mean or percentile can be selected as the initial threshold. Alternatively, a maximum allowable fluctuation value can be manually set based on the safety tolerance of actual power fluctuations. During operation, the system can also dynamically fine-tune the threshold based on the actual device response to ensure that it consistently matches the device adjustment capabilities and system safety requirements. During actual operation, when the absolute value of the trend coupling factor is greater than or equal to the threshold, the system automatically triggers the subsequent adaptive adjustment steps. If it is less than the threshold, the current allocation strategy remains unchanged, avoiding frequent adjustments to minor changes.

[0043] The trend coupling factor is multiplied by the preliminary power allocation parameter item by item to obtain the first correction value of each power source allocation parameter, specifically including: When the system determines that the trend coupling factor reaches or exceeds the preset threshold, it enters the adaptive correction phase. At this point, the system takes the preliminary power allocation parameters of each power source as input and multiplies them one by one with the trend coupling factor of the same period. That is, for each power source, its preliminary allocation parameters are directly multiplied by the numerical value of the trend coupling factor to obtain the first correction value for that power source. This way, the changing trend of the trend coupling factor (such as positive amplification or negative weakening) is directly reflected in the allocation correction amplitude of each power source, achieving adaptive adjustment of power allocation. For example, if the trend coupling factor is greater than 1, the correction value of the corresponding power source will increase accordingly; if it is less than 1, the correction value will decrease; when the trend coupling factor is negative, the allocation direction is switched. All first correction values ​​serve as the basis for subsequent multiple corrections and normalization processes, ensuring that the power allocation results closely follow the system operating trend and achieving more efficient and flexible multi-source coordinated adjustment.

[0044] In a preferred embodiment of the present invention, based on the target power allocation parameters, combined with real-time grid-connected point load demand data and energy storage device status data, target power allocation instructions and energy storage charge and discharge adjustment instructions are generated, including: Obtain the grid connection point load demand data of the current cycle in real time, calculate the difference between the target power allocation parameter and the load demand data, and distribute the difference to each power source according to the allocation ratio of the allocation parameter to form the target power allocation instruction; At the same time, the system monitors the energy storage device's current remaining capacity, charge and discharge power upper limit, and temperature safety range. Combining the target power allocation parameters with the load demand margin, it determines whether the energy storage device needs to be charged or discharged in the current cycle, sets the charge and discharge power values ​​accordingly, and generates energy storage charge and discharge adjustment instructions. Send the target power allocation instruction to the inverter control unit, and the energy storage charge and discharge adjustment instruction to the energy storage device control unit, driving the relevant equipment to work in coordination according to their respective instructions; Monitor the inverter output power and the actual charging and discharging status of the energy storage device, and compare them with the target instructions in real time. When deviation occurs, automatic feedback is given and the initial power allocation parameters are automatically returned to recalculate to achieve continuous closed-loop adaptive optimization.

[0045] In an embodiment of the present invention, by performing polynomial fitting on the load demand data of the historical period, extracting the slope of the fitting curve, and weightedly superimposing the slope value and the mean value of the rate of change according to the preset trend factor synthesis weight coefficient, a load change trend factor is formed, which can more accurately reflect the dynamic evolution law of the overall load of the system. Polynomial fitting can make full use of the continuity of historical data changes, effectively combine short-term fluctuations with long-term trends, and extract the slope to further quantify the rate of load growth or decrease. Then, through weighted synthesis with the mean value of the rate of change, a more comprehensive evaluation of the current load change trend is formed. For example, when the grid load shows a slow increase but occasional sudden changes, the fitting slope can reflect the main trend, and the mean value of the rate of change can capture sudden fluctuations. The synthesis of the two can provide more forward-looking parameter support for subsequent power distribution corrections.

[0046] The inverter output power and the actual charge and discharge status of the energy storage device are monitored and compared with the target instructions in real time. If there is a deviation, the system automatically returns to the initial power allocation parameter for recalculation after automatic feedback to achieve continuous closed-loop adaptive optimization. Specifically, it includes: During system operation, a real-time monitoring module continuously collects the actual output power data of the inverter and the actual charging and discharging status of the energy storage device. This collected data is compared item by item with the target power allocation instructions and energy storage charge and discharge adjustment instructions issued by the system during the current cycle, determining the difference between each actual output or operating status and the instruction requirements. If the actual output of a power source or the charge / discharge status of an energy storage device deviates from the target instruction beyond the set tolerance range, the system immediately inputs this deviation information as a feedback signal to the dispatch control center. Upon receiving the deviation feedback, the dispatch center automatically triggers the recalculation process of the allocation algorithm, directly returning to the calculation of the initial power allocation parameters and re-completing the entire power allocation process based on the latest collected real-time data, status, and system feedback. This design implements continuous closed-loop adaptive optimization of the system, ensuring that the power allocation results can continuously self-correct according to the actual operating status, thereby improving the operational stability and responsiveness of the entire photovoltaic energy storage inverter system in complex dynamic environments.

[0047] In a preferred embodiment of the present invention, a polynomial fitting is performed on the historical period load demand data to extract the slope of the fitting curve. The slope value and the mean value of the rate of change are weighted and superimposed according to a preset trend factor synthesis weight coefficient to form a load change trend factor, including: According to the historical period load demand data, each data item is arranged in chronological order to form a time series sample; Apply a polynomial fitting algorithm to the time series samples to obtain a curve reflecting the load change trend, and extract the slope value of the curve in the latest cycle; According to the preset trend factor synthesis weight coefficient, the slope value and the average value of the change rate are multiplied by their respective weight coefficients, and the two products are accumulated to generate the load change trend factor of the current cycle.

[0048] In an embodiment of the present invention, by performing a directional analysis on the initial value of the power fluctuation trend factor, the duration period of the fluctuation increase or decrease is counted, and according to the correlation between the duration period and the initial value of the power fluctuation trend factor, a preset duration period weight coefficient and an initial value weight coefficient are assigned respectively, and the two are weighted and superimposed to form a power fluctuation trend factor, which can effectively enhance the system's ability to identify short-term fluctuation trends and continuous fluctuation characteristics. Directional analysis not only takes into account the amplitude of power fluctuations, but also focuses on the impact of the duration of fluctuations. Through the weight adjustment mechanism, the sensitivity of power allocation under complex fluctuation conditions is improved. For example, when the output power of a certain stage shows a same-direction growth for multiple consecutive cycles, the system can promptly increase the adjustment amplitude of the corresponding power source; and if the fluctuation direction switches frequently, the weight is adjusted to reduce excessive response to a single fluctuation signal, thereby enhancing the smoothness and robustness of the overall operation.

[0049] The polynomial fitting algorithm is applied to the time series samples to obtain a curve reflecting the load change trend, including: When processing historical periodic load demand data, the system first arranges a set number of historical periodic load demand data in chronological order to form a complete time series sample set. The system then selects a fitting order (such as a linear, quadratic, or higher-order polynomial) and uses the time series data as the independent variable and the load demand value as the dependent variable to model this data using a polynomial function using a minimum error fitting method. During the fitting process, the system adjusts the polynomial coefficients of each order to ensure that the fitted curve fits all historical data points as closely as possible. Once the fitting is complete, a continuous, smooth mathematical curve is obtained that accurately reflects the overall trend and local variations of historical load demand. The system then further extracts parameters such as the slope or local rate of change of this curve at the latest period point for subsequent calculation of load trend factors. In this way, polynomial fitting not only improves the ability to capture the evolution of load demand but also provides a solid data foundation and trend judgment for subsequent dynamic optimization of power allocation.

[0050] In a preferred embodiment of the present invention, a directional analysis is performed on the initial value of the power fluctuation trend factor, and the duration period of the fluctuation increase or decrease is counted. Based on the correlation between the duration period and the initial value of the power fluctuation trend factor, a preset duration period weight coefficient and an initial value weight coefficient are assigned respectively. The two are weighted and superimposed to form the power fluctuation trend factor, including: Based on the initial value of the power fluctuation trend factor, the output power data of the historical period is compared cycle by cycle to identify and count the number of cycles with continuous same-direction rise or fall changes to obtain the continuous cycle length; According to the duration period length and the initial value of the power fluctuation trend factor, and based on their influence in previous adjustments, the duration period weight coefficient and the initial value weight coefficient are set; The power fluctuation trend factor of the current cycle is obtained by multiplying the duration period length by the duration period weight coefficient, and multiplying the initial value of the power fluctuation trend factor by the initial value weight coefficient, and adding the two weighted results.

[0051] In an embodiment of the present invention, by performing a directional analysis on the initial value of the power fluctuation trend factor, the duration period of the fluctuation increase or decrease is counted, and according to the correlation between the duration period and the initial value of the power fluctuation trend factor, a preset duration period weight coefficient and an initial value weight coefficient are assigned respectively, and the two are weighted and superimposed to form a power fluctuation trend factor, which can improve the ability to identify short-term fluctuations and continuous changes in power output. The statistics of the duration period enable the system to not only pay attention to the amplitude of the fluctuation, but also to promptly reflect the consistency and periodicity of power changes, which is of great significance for dealing with working conditions with continuous abnormal load or lighting conditions. In actual applications, when it is detected that the output power rises in the same direction for multiple consecutive cycles, the system can enhance the response speed to the trend by increasing the duration period weight coefficient, thereby realizing forward-looking correction of energy storage charging and discharging or inverter adjustment, thereby reducing frequent small adjustments and resource waste, and improving the overall energy efficiency and operating life of the system.

[0052] Among them, according to the duration period length and the initial value of the power fluctuation trend factor, and based on their influence in previous adjustments, the duration period weight coefficient and the initial value weight coefficient are set, specifically including: When calculating the power fluctuation trend factor, the system first counts the number of consecutive periods of upward or downward fluctuations (i.e., the duration of the period) and the initial value of the power fluctuation trend factor for the current period. To determine the importance of each factor in comprehensively determining the trend, the system reviews and analyzes historical data to assess the impact of the duration of the period and the initial value on system stability and regulation accuracy during actual allocation and regulation. For example, if historical operating results indicate that the persistence of power fluctuations significantly impacts allocation accuracy, the duration weight coefficient is set higher. If the initial value of the power fluctuation trend factor effectively reflects the fluctuation amplitude, the initial weight coefficient is increased. This evaluation can be performed by statistically analyzing indicators such as allocation error and system fluctuation frequency corresponding to different coefficient settings under historical operating conditions, or by manually setting the initial value through experience and then adaptively adjusting it based on the results. Ultimately, the duration weight coefficient and the initial value weight coefficient are used as parameters in the weighted calculation of the fluctuation trend factor for the current period. This weighted result reflects both the impact of fluctuation persistence and the reference value of the fluctuation amplitude within a single period, thereby improving the accuracy of power allocation trend determination.

[0053] In a preferred embodiment of the present invention, based on the first correction value, combined with the current cycle load demand change and the available capacity of the energy storage device, the two are weighted and superimposed on each first correction value according to a preset feedback adjustment weight coefficient to obtain a second correction value, including: Obtain the current cycle load demand change and the available capacity of the energy storage device, and assign feedback adjustment weight coefficients to each of them. The feedback adjustment weight coefficients are dynamically adjusted based on the historical contribution of each parameter to power distribution stability; Multiply the load demand change by its feedback adjustment weight coefficient, and multiply the available capacity of the energy storage device by its feedback adjustment weight coefficient to obtain weighted feedback values ​​respectively; Each first correction value is added to the corresponding weighted feedback value to obtain a second correction value of each power source in the current cycle.

[0054] In an embodiment of the present invention, by combining the current cycle load demand variation and the available capacity of the energy storage device according to the first correction value, and weighting and superimposing the two to each first correction value according to a preset feedback adjustment weight coefficient, a second correction value is obtained, which enables the power allocation correction process to fully integrate real-time demand and energy storage capacity, and improve the level of dynamic adaptability. Specifically, the load demand variation can reflect the fluctuation characteristics of actual energy consumption on the user side, while the available capacity of the energy storage device reflects the response redundancy of the current energy storage system. The two jointly affect the final correction result through weighted superposition. In this way, in situations with high demand and sufficient energy storage capacity, the allocation parameters are automatically relaxed, which helps to improve system output; when the energy storage is close to the limit, the parameters are tightened to prioritize equipment safety and operational reliability. For example, during peak power consumption, the system automatically tilts more power to the load side, and prioritizes energy storage during low periods, effectively responding to variable working conditions and extreme events.

[0055] The load demand change and available capacity of the energy storage device in the current cycle are obtained, and feedback adjustment weight coefficients are assigned to the two respectively, including: During the current cycle, the system collects and calculates the load demand variation in real time, that is, the variation between the load demand value of this cycle and the previous cycle or the set benchmark value. At the same time, the available capacity of the energy storage device is monitored, including its available power and the dispatchable charge and discharge power margin. For these two key parameters, the system assigns a feedback adjustment weight coefficient to each of them. The initial setting of the weight coefficient can be based on equipment characteristics, operating conditions and historical operating data experience. For example, if the load demand fluctuates greatly and has a greater impact on system operation, the load variation weight coefficient is set to a high value; if the capacity of the energy storage device is tight or plays a key role in system stability, its weight coefficient is appropriately increased. In this way, the role of each input parameter in participating in the subsequent power allocation correction can be flexibly adjusted to better adapt to the current actual operating conditions of the system.

[0056] The feedback adjustment weight coefficient is dynamically adjusted according to the historical contribution of each parameter to the power distribution stability, specifically including: In order to achieve more accurate and adaptive power distribution optimization, the system not only adjusts the weight coefficients for the load demand fluctuation and the available capacity of the energy storage device during the initial setting stage, but also dynamically optimizes the weight settings according to the historical contribution of each parameter in improving distribution stability during the long-term operation of the system. Specifically, the system regularly retrospectively analyzes historical operating data to evaluate the impact of fluctuations in each parameter on key indicators such as power distribution error, output stability, and the number of equipment protection triggers. For example, if historical data shows that the system distribution error is significantly reduced when the weight coefficient is increased, the weight of the parameter is maintained or further increased; conversely, if the increase in the weight of a certain parameter does not bring about an improvement in the distribution effect, the weight value is appropriately lowered. This dynamic adjustment can be achieved based on rolling evaluation algorithms, feedback calibration logic, or manual intervention to ensure that the weights of the parameters involved in the adjustment in each cycle always reflect their actual contribution to system stability, thereby achieving closed-loop adaptive optimization.

[0057] In a preferred embodiment of the present invention, the load demand data of the grid connection point in the current period is obtained in real time, the difference between the target power allocation parameter and the load demand data is calculated, and the difference is allocated to each power source according to the allocation ratio of the allocation parameter to form a target power allocation instruction, including: Obtain the grid connection point load demand data of the current cycle in real time, compare it with the target power allocation parameters of the current cycle item by item, calculate the difference between the theoretical output power of each power source and the actual load demand, and obtain the power difference that needs to be adjusted for each power source; The power difference of each power source is added together to obtain the total power difference; According to the proportion of each power source in the target power allocation parameter, the total power difference is proportionally distributed to each power source to form their respective allocation adjustment amounts; Combine the output history data and dynamic response capability of each power source in the previous cycle to determine the correction coefficient of each power source allocation adjustment, and add the corrected allocation adjustment to its target power allocation parameter to obtain the final target output value; The final target output value is safety checked. If it exceeds the maximum or minimum power range allowed by the device, it is automatically corrected to the boundary value allowed by the device; The final target output value after safety verification is used as the target power allocation instruction for the current cycle.

[0058] In an embodiment of the present invention, by acquiring the grid connection point load demand data for the current cycle in real time and comparing it item by item with the target power allocation parameters, the real-time difference between the theoretical output and actual load of each power source can be accurately reflected, effectively detecting system deviations. The difference values ​​of each power source are summarized and allocated to each power source as needed according to the allocation ratio of the target allocation parameters, making the power adjustment of the entire system more holistic and coordinated. Furthermore, by combining the output historical data and dynamic response capabilities of each power source in the previous cycle, a correction coefficient for each power source's allocation adjustment is set, achieving dynamic adaptive optimization of the allocation adjustment process. This correction coefficient can flexibly adjust the adjustment amplitude of each power source based on factors such as the actual response of the equipment and historical fluctuation characteristics, effectively avoiding persistent deviations or output oscillations caused by lag or inertia of a particular power source. Subsequently, the corrected allocation adjustment value is added to the target power allocation parameters to obtain the final target output value, which is then subjected to safety verification and boundary correction to ensure that the output of all power sources is within the equipment's safe allowable range. Ultimately, the present invention enables each power source to respond quickly, accurately, and dynamically to changes in grid load, significantly improving the system's adjustment flexibility, safety, and operational stability. For example, in actual power grid operation, if a power source has a delayed response on a cycle basis, the system will automatically reduce the increase in the adjustment amount allocated to it in this cycle, thereby avoiding output overshoot and improving the overall energy efficiency of the system and equipment life.

[0059] The correction coefficient of each power source allocation adjustment amount is determined by combining the output historical data and dynamic response capability of each power source in the previous cycle, specifically including: During actual operation, the system continuously records historical output data for each power source (such as photovoltaics and energy storage), including the actual output power during the previous cycle and its deviation from the target output value. Furthermore, based on factory device parameters or operational evaluation results, the system also obtains the dynamic response capabilities of each power source, such as maximum regulation speed, allowable output variation, and inertial response.

[0060] To determine the correction coefficient for the allocated adjustment for the current cycle, the system first analyzes the deviation between the actual output and the target output in the previous cycle. If a power source exhibited a delayed response and large deviation in the previous cycle, the adjustment allocated for the current cycle should be appropriately reduced to prevent continued error accumulation and system oscillation. Conversely, if the response is good and the output is stable, the adjustment can be maintained or appropriately increased to improve the overall system dynamics. Furthermore, the dynamic response capability of the device should be considered. If the device itself has a slow adjustment speed or a limited range of variation, even if the algorithm allocates a large adjustment, it should be limited by a correction coefficient to ensure that the resulting allocation does not exceed the device's tolerance. In practice, the correction coefficient for each power source can be calculated by combining output history and response capability using methods such as preset rules, rolling mean statistics, and weight mapping. This coefficient is applied to the allocated adjustment for the current cycle, ensuring that all adjustments have both historical correction logic and physical enforceability, effectively ensuring the safety and stability of system operation.

[0061] The final target output value is subjected to a security check, specifically including: After all the power sources' final target output values ​​have been corrected and combined, the system performs a comprehensive safety check on each target output value to prevent the risk of over-limit operation, misoperation, or failure. The first step in this safety check is to compare each power source's final target output value with the maximum and minimum power ranges allowed by the device to check for any violations of the safety range specified by the device's technical parameters. If a power source's target output exceeds the maximum power limit, the output value is automatically corrected to the device's maximum allowable value. If it falls below the minimum safe output, the value is adjusted to the minimum allowable value. The system then verifies the slope of the target output change to ensure that the power fluctuation per unit time does not exceed the device's maximum regulation rate to prevent any impact on the device. Furthermore, the system allocates the total output power appropriately based on the overall system operation strategy and grid stability requirements to prevent the overload of a single power source from impacting the entire system. Only after all safety checks have passed are these target output values ​​issued as actual power allocation instructions, ensuring efficient system operation while safeguarding the safety and stability of both the device and the grid under various extreme operating conditions.

[0062] In a preferred embodiment of the present invention, the current remaining power of the energy storage device, the upper limit of the charge and discharge power, and the temperature safety range are monitored simultaneously. In combination with the target power allocation parameter and the load demand margin, it is determined whether the energy storage device needs to be charged or discharged in the current cycle, and the charge and discharge power values ​​are set accordingly to generate energy storage charge and discharge adjustment instructions, including: Real-time monitoring of the remaining power, charge and discharge power limits, and actual temperature of the energy storage device during the current cycle to form an energy storage status data input set; Comprehensively analyzing the energy storage state data input set with the target power allocation parameters of the current cycle and the load demand margin of the current cycle to determine whether the energy storage device should adopt the charging mode or the discharging mode in the current cycle, thereby obtaining an operation mode determination result; Based on the result of the operation mode determination, the charging or discharging direction of the energy storage device in the current cycle is determined, and the actual executable charging and discharging power values ​​are set based on the remaining power, the upper power limit of the equipment, and the temperature safety range. The safety and rationality of the charge and discharge power values, target power allocation parameters, and actual load requirements are checked. If the value exceeds the equipment limit or poses a safety hazard, it is automatically adjusted to the maximum or minimum value within the equipment's allowable range to generate a corrected charge and discharge power value. According to the corrected charge and discharge power values, the energy storage charge and discharge adjustment instructions for the current cycle are generated.

[0063] In an embodiment of the present invention, by simultaneously monitoring the energy storage device's current remaining capacity, charge and discharge power limits, and temperature safety range, and combining target power allocation parameters with load demand margin, the system determines whether the energy storage device needs to be charged or discharged in the current cycle. The system then sets the charge and discharge power values ​​accordingly, generating energy storage charge and discharge adjustment instructions. This enables the energy storage system to sensitively respond to and proactively adjust to grid fluctuations and energy consumption changes. Real-time collection of energy storage status data ensures that each cycle's charge and discharge control of the energy storage device is based on the most accurate device status and environmental constraints. Through comprehensive analysis with target power allocation parameters and actual load margin, the optimal strategy for prioritizing charge or discharge of the energy storage device can be dynamically determined, and appropriate charge and discharge power can be allocated accordingly to avoid risks such as overcharging, overdischarging, or overheating of the energy storage device. For example, when the grid-connected load drops significantly and the energy storage device's capacity is low, the system automatically enters charging mode and allocates the appropriate charging power. Conversely, when the load suddenly increases and the energy storage device is in a high capacity range, the system automatically switches to discharge mode to appropriately compensate for grid shortfalls. All power settings are dynamically verified against device limits and safety constraints, ensuring that energy storage charging and discharging behavior supports stable grid operation while maximizing the safety and lifespan of the energy storage system itself. Ultimately, the energy storage device receives regulation instructions based on device status, demand changes, and safety constraints, enabling precise and efficient energy management and distribution.

[0064] The actual executable charge and discharge power values ​​are set based on the remaining power, the upper power limit of the device, and the temperature safety range. Specifically, the following are included: During each cycle, the system first obtains real-time information about the remaining charge in the energy storage device to determine the specific amount of available energy. It then queries the upper limits for charge and discharge power specified by the energy storage device manufacturer or in the system configuration file to determine the device's maximum allowable charge and discharge power under the current operating conditions. Simultaneously, the system monitors the operating temperature of the energy storage device using temperature sensors or the device's built-in status monitoring function to ensure it remains within the specified range for safe operation. If the temperature exceeds the safe range, the system automatically derates the charge and discharge power or suspends operation.

[0065] After considering the three constraints above, the system adjusts the theoretical charge or discharge power calculated for the current cycle: If the power exceeds the charge capacity allowed by the current remaining power or the device's power limit, the power value is limited to the maximum allowable value; if the temperature approaches the limit, the power value is also derated according to a predetermined ratio. The resulting actual executable charge and discharge power value not only meets the current scheduling needs but also prevents equipment overcharging, over-discharging, battery life damage, or safety accidents, thus ensuring safe and reliable energy storage participation in energy regulation.

[0066] Among them, the safety and rationality of the charging and discharging power values, target power allocation parameters, and actual load requirements are checked, including: After the system determines the actual charge and discharge power value that can be executed in this cycle, it further compares this value with the target power allocation parameters item by item to confirm whether it is consistent with the overall system scheduling target. For example, if the target allocation parameters require charging operations in this cycle, but the actual calculated safety power value is negative or zero, the system will prioritize equipment safety and automatically adjust the actual power to the minimum allowable safety value or suspend charging operations. At the same time, the system must also synchronize and verify the actual load demand to ensure that the charging and discharging behavior of the energy storage device does not cause excessive fluctuations in parameters such as voltage and current in the local power grid, affecting the normal operation of the power user or other equipment.

[0067] This process also includes determining whether the charging and discharging behavior may trigger protective actions for other linked devices, such as inverter overcurrent or bus voltage exceeding a certain limit. After all verification steps are completed, if the actual power value passes all safety and rationality checks, it will be directly used as the energy storage charge and discharge adjustment command for the current cycle. If it fails, the power value will be further adjusted to be within all safety constraints, or the charge and discharge tasks for the current cycle will be temporarily suspended.

[0068] This series of rigorous safety and rationality checks ensures that the energy storage device always participates in grid energy regulation within a controllable and predictable range, maximizing the system's operational safety and economic benefits.

[0069] An embodiment of the present invention further provides a photovoltaic energy storage inverter power coordination control system based on an adaptive algorithm, the system comprising: The acquisition module is used to collect the output power data of the photovoltaic modules, the status data of the energy storage device and the load demand data of the grid connection point, and normalize them to obtain the basic data for the calculation of the distribution parameters; The allocation parameter calculation module is used to calculate the preliminary power allocation parameters of the current cycle based on the basic data; The trend analysis module is used to calculate the load change trend factor and the power fluctuation trend factor based on the load demand data and output power data of the historical period, and couple the two to form a trend coupling factor; An adaptive correction module is used to adaptively correct the preliminary power allocation parameters through the trend coupling factor to generate corrected target power allocation parameters; The instruction generation and issuance module is used to generate target power allocation instructions and energy storage charge and discharge adjustment instructions based on the target power allocation parameters, combined with real-time grid-connected point load demand data and energy storage device status data, and issue them to the inverter control unit and energy storage device control unit respectively.

[0070] It should be noted that this system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0071] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A power coordination control method for photovoltaic energy storage inverters based on an adaptive algorithm, characterized in that: The method comprises: Collect the output power data of photovoltaic modules, the status data of energy storage devices and the load demand data of grid connection points, and normalize them to obtain the basic data for distribution parameter calculation; Based on the basic data, calculate and obtain the preliminary power allocation parameters of the current cycle; Based on the load demand data and output power data of the historical period, the load change trend factor and the power fluctuation trend factor are calculated respectively, and the two are coupled to form a trend coupling factor; Adaptively modify the preliminary power allocation parameters through the trend coupling factor to generate modified target power allocation parameters; According to the target power allocation parameters, combined with the real-time grid-connected point load demand data and energy storage device status data, the target power allocation instructions and energy storage charge and discharge adjustment instructions are generated and sent to the inverter control unit and energy storage device control unit respectively.

2. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 1 is characterized in that: Based on the basic data, the preliminary power allocation parameters for the current cycle are calculated, including: Based on the basic data, each data item is differentiated from the mean value of the corresponding data item in the previous period to obtain a differential value data set; According to the differential value data set, the differential value of each data item is weighted and superimposed according to the preset data allocation weight coefficient to obtain a weighted processing value data set; The weighted processed values ​​are added to form a composite parameter, and the theoretical load demand is inferred from the composite parameter, and the difference between the theoretical load demand and the actual load demand is calculated to obtain the power distribution deviation; According to the power allocation deviation, the total power to be allocated is allocated to each power source in proportion to the absolute value of the power allocation deviation, and the preliminary power allocation parameters of each power source are obtained.

3. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 1 is characterized in that: Based on the load demand data and output power data of the historical period, the load change trend factor and the power fluctuation trend factor are calculated respectively, and the two are coupled to form a trend coupling factor, including: Perform time series analysis on a set number of historical period load demand data, calculate the rate of change between every two adjacent period load demand data, and average all the change rates to obtain the mean change rate; Perform polynomial fitting on the historical period load demand data, extract the slope of the fitting curve, and weight the slope value and the mean value of the rate of change according to the preset trend factor synthesis weight coefficient to form a load change trend factor; The historical period output power data is processed step by step using a sliding window. The variance and range of the output power are calculated in each window and multiplied by the preset variance weight coefficient and range weight coefficient respectively. The weighted variance value and range value are added together to obtain the initial value of the power fluctuation trend factor. Directional analysis is performed on the initial value of the power fluctuation trend factor, and the duration of the fluctuation increase or decrease is counted. Based on the correlation between the duration period and the initial value of the power fluctuation trend factor, a preset duration period weight coefficient and an initial value weight coefficient are assigned respectively, and the two are weighted and superimposed to form the power fluctuation trend factor; The load change trend factor and the power fluctuation trend factor are weighted and summed according to the preset coupling weight coefficient. If the two are in the same direction, the coupling weight is increased, and if the directions are opposite, the coupling weight is reduced to generate a trend coupling factor.

4. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 1 is characterized in that: The preliminary power allocation parameters are adaptively modified using the trend coupling factor to generate the modified target power allocation parameters, including: Determining whether the magnitude of the trend coupling factor exceeds a preset trend coupling threshold; When it exceeds, the trend coupling factor is multiplied by the preliminary power allocation parameter item by item to obtain the first correction value of each power source allocation parameter; Based on the first correction value, combined with the current cycle load demand change and the available capacity of the energy storage device, the two are weighted and superimposed on each first correction value according to a preset feedback adjustment weight coefficient to obtain a second correction value; All second correction values ​​are summed and then normalized to ensure that the sum of the allocation parameters is equal to the current allocable total power, thereby generating target power allocation parameters after multi-level adaptive correction and normalization.

5. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 1 is characterized in that: Based on the target power allocation parameters, combined with real-time grid-connected point load demand data and energy storage device status data, target power allocation instructions and energy storage charge and discharge adjustment instructions are generated, including: Obtain the grid connection point load demand data of the current cycle in real time, calculate the difference between the target power allocation parameter and the load demand data, and distribute the difference to each power source according to the allocation ratio of the allocation parameter to form the target power allocation instruction; At the same time, the system monitors the energy storage device's current remaining capacity, charge and discharge power upper limit, and temperature safety range. Combining the target power allocation parameters with the load demand margin, it determines whether the energy storage device needs to be charged or discharged in the current cycle, sets the charge and discharge power values ​​accordingly, and generates energy storage charge and discharge adjustment instructions. Send the target power allocation instruction to the inverter control unit, and the energy storage charge and discharge adjustment instruction to the energy storage device control unit, driving the relevant equipment to work in coordination according to their respective instructions; Monitor the inverter output power and the actual charging and discharging status of the energy storage device, and compare them with the target instructions in real time. When deviation occurs, automatic feedback is given and the initial power allocation parameters are automatically returned to recalculate to achieve continuous closed-loop adaptive optimization.

6. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 3 is characterized in that: Perform polynomial fitting on the historical period load demand data, extract the slope of the fitting curve, and weight the slope value and the mean value of the rate of change according to the preset trend factor synthesis weight coefficient to form the load change trend factor, including: According to the historical period load demand data, each data item is arranged in chronological order to form a time series sample; Apply a polynomial fitting algorithm to the time series samples to obtain a curve reflecting the load change trend, and extract the slope value of the curve in the latest cycle; According to the preset trend factor synthesis weight coefficient, the slope value and the average value of the change rate are multiplied by their respective weight coefficients, and the two products are accumulated to generate the load change trend factor of the current cycle.

7. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 3 is characterized in that: Directional analysis is performed on the initial value of the power fluctuation trend factor, and the duration of the fluctuation increase or decrease is counted. Based on the correlation between the duration and the initial value of the power fluctuation trend factor, a preset duration weight coefficient and an initial value weight coefficient are assigned respectively. The two are weighted and superimposed to form the power fluctuation trend factor, including: Based on the initial value of the power fluctuation trend factor, the output power data of the historical period is compared cycle by cycle to identify and count the number of cycles with continuous same-direction rise or fall changes to obtain the continuous cycle length; According to the duration period length and the initial value of the power fluctuation trend factor, and based on their influence in previous adjustments, the duration period weight coefficient and the initial value weight coefficient are set; The power fluctuation trend factor of the current cycle is obtained by multiplying the duration period length by the duration period weight coefficient, and multiplying the initial value of the power fluctuation trend factor by the initial value weight coefficient, and adding the two weighted results.

8. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 4 is characterized in that: Based on the first correction value, combined with the current cycle load demand change and the available capacity of the energy storage device, the two are weighted and superimposed on each first correction value according to a preset feedback adjustment weight coefficient to obtain a second correction value, including: Obtain the current cycle load demand change and the available capacity of the energy storage device, and assign feedback adjustment weight coefficients to each of them. The feedback adjustment weight coefficients are dynamically adjusted based on the historical contribution of each parameter to power distribution stability; Multiply the load demand change by its feedback adjustment weight coefficient, and multiply the available capacity of the energy storage device by its feedback adjustment weight coefficient to obtain weighted feedback values ​​respectively; Each first correction value is added to the corresponding weighted feedback value to obtain a second correction value of each power source in the current cycle.

9. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 5 is characterized in that: Obtain the grid connection point load demand data of the current cycle in real time, calculate the difference between the target power allocation parameter and the load demand data, and distribute the difference to each power source according to the allocation ratio of the allocation parameter to form a target power allocation instruction, including: Obtain the grid connection point load demand data of the current cycle in real time, compare it with the target power allocation parameters of the current cycle item by item, calculate the difference between the theoretical output power of each power source and the actual load demand, and obtain the power difference that needs to be adjusted for each power source; The power difference of each power source is added together to obtain the total power difference; According to the proportion of each power source in the target power allocation parameter, the total power difference is proportionally distributed to each power source to form their respective allocation adjustment amounts; Combine the output history data and dynamic response capability of each power source in the previous cycle to determine the correction coefficient of each power source allocation adjustment, and add the corrected allocation adjustment to its target power allocation parameter to obtain the final target output value; The final target output value is safety checked. If it exceeds the maximum or minimum power range allowed by the device, it is automatically corrected to the boundary value allowed by the device; The final target output value after safety verification is used as the target power allocation instruction for the current cycle.

10. The photovoltaic energy storage inverter power coordination control method based on the adaptive algorithm according to claim 5, characterized in that: At the same time, the remaining power of the energy storage device, the upper limit of the charge and discharge power, and the temperature safety range are monitored. Combined with the target power allocation parameters and the load demand margin, it is determined whether the energy storage device needs to be charged or discharged in the current cycle. The charge and discharge power values ​​are set accordingly, and energy storage charge and discharge adjustment instructions are generated, including: Real-time monitoring of the remaining power, charge and discharge power limits, and actual temperature of the energy storage device during the current cycle to form an energy storage status data input set; Comprehensively analyzing the energy storage state data input set with the target power allocation parameters of the current cycle and the load demand margin of the current cycle to determine whether the energy storage device should adopt the charging mode or the discharging mode in the current cycle, thereby obtaining an operation mode determination result; Based on the result of the operation mode determination, the charging or discharging direction of the energy storage device in the current cycle is determined, and the actual executable charging and discharging power values ​​are set based on the remaining power, the upper power limit of the equipment, and the temperature safety range. The safety and rationality of the charge and discharge power values, target power allocation parameters, and actual load requirements are checked. If the value exceeds the equipment limit or poses a safety hazard, it is automatically adjusted to the maximum or minimum value within the equipment's allowable range to generate a corrected charge and discharge power value. According to the corrected charge and discharge power values, the energy storage charge and discharge adjustment instructions for the current cycle are generated.

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