Array type photovoltaic panel intelligent power regulation and energy optimization distribution method

Abstract

The invention discloses an array type photovoltaic panel intelligent power adjustment and energy optimization distribution method, and relates to the technical field of photovoltaic panel power adjustment and energy distribution, and the method specifically comprises the following steps: obtaining the operation state information of each affected photovoltaic panel in real time; respectively generating a reflected light intensity contribution coefficient and a power fluctuation response index of each influenced photovoltaic panel; and constructing a reflected light power regulation evaluation model for the generated reflected light intensity contribution coefficient and power fluctuation response index of each influenced photovoltaic panel, generating a power regulation coefficient of each influenced photovoltaic panel, and evaluating the power fluctuation degree of each influenced photovoltaic panel caused by reflected light. And dividing the affected photovoltaic panels into low-fluctuation photovoltaic panels, medium-fluctuation photovoltaic panels and high-fluctuation photovoltaic panels according to evaluation results. According to the invention, the problem of photovoltaic panel power fluctuation caused by reflected light is solved, and accurate monitoring, intelligent adjustment and comprehensive improvement of system operation efficiency and stability are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic panel power regulation and energy distribution, and in particular to an array photovoltaic panel intelligent power regulation and energy optimization distribution method. Background Art

[0002] Array photovoltaic panels are power generation systems composed of multiple photovoltaic panels, which are connected in series, parallel or hybrid ways to collect solar energy and convert it into electrical energy. However, since photovoltaic panels are affected by various environmental factors such as light intensity, temperature changes, and shadows during operation, the power generation efficiency between photovoltaic panels is often uneven, resulting in unstable energy output of the entire array and even energy loss. In addition, the power generation of photovoltaic panels does not always match the demand of power loads, and this imbalance further reduces the efficiency of the system. Therefore, it is necessary to adjust the power of array photovoltaic panels and reasonably allocate energy to improve power generation efficiency, reduce energy waste and ensure the stability of output power. By monitoring the power generation status of each photovoltaic panel and coordinating the relationship between power generation and load in combination with the energy storage system, it is possible to achieve optimal energy distribution and significantly improve the overall performance and reliability of the photovoltaic power generation system.

[0003] The prior art has the following deficiencies:

[0004] In some photovoltaic power stations, especially photovoltaic arrays located in cities or high-density building areas, some photovoltaic panels are affected by the reflected light from surrounding buildings or the ground, especially at noon or during strong sunlight periods, the reflected light intensity in local areas may be much higher than in other areas. Due to the drastic changes in the intensity of reflected light received by these photovoltaic panels, the power output of local photovoltaic panels is much higher than that of other photovoltaic panels that are not affected by reflected light. Existing technologies can usually only adjust power distribution through global light intensity data, and cannot monitor and accurately identify the impact of reflected light in real time, especially when the reflected light intensity changes rapidly, the system fails to perceive the power changes of local photovoltaic panels in time. Since the system fails to dynamically adjust the output power of these photovoltaic panels, the power of photovoltaic panels in areas with high reflected light intensity is too high, which in turn disrupts the power regulation of the entire array, resulting in low overall system efficiency. More seriously, long-term excessive power output may cause photovoltaic panels to overheat, and may even damage these photovoltaic panels, causing equipment failure and energy loss. Due to the lag in power regulation, the energy storage system cannot operate efficiently according to the actual power output during the charging and discharging process, resulting in insufficient or overcharged energy storage equipment, which in turn affects the stability of power supply and the long-term operation reliability of the system.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an array photovoltaic panel intelligent power regulation and energy optimization distribution method to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: an array photovoltaic panel intelligent power regulation and energy optimization allocation method, which specifically includes the following steps:

[0008] During the operation of the array photovoltaic panel, the basic operation information of each photovoltaic panel in the array photovoltaic panel is obtained in real time, and the photovoltaic panel affected by the reflected light is determined in combination with the environmental information, and is marked as the affected photovoltaic panel;

[0009] Acquire the operating status information of each affected photovoltaic panel in real time, analyze it after acquisition, and generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively;

[0010] A reflected light power regulation evaluation model is constructed for the generated reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel, and the power regulation coefficient of each affected photovoltaic panel is generated. After the generation, the power fluctuation degree of each affected photovoltaic panel caused by reflected light is evaluated, and the affected photovoltaic panels are divided into low-fluctuation photovoltaic panels, medium-fluctuation photovoltaic panels and high-fluctuation photovoltaic panels according to the evaluation results;

[0011] According to the classification results of each affected photovoltaic panel, corresponding power regulation and energy allocation measures are taken for low-fluctuation photovoltaic panels, medium-fluctuation photovoltaic panels and high-fluctuation photovoltaic panels respectively;

[0012] Continuously monitor the operating status and reflected light impact of the affected photovoltaic panels, and dynamically adjust the reflected light power regulation evaluation model and power regulation strategy based on real-time monitoring data to optimize the operating status of the photovoltaic array and improve the operating efficiency and stability of the photovoltaic system.

[0013] Preferably, the operating status information of each affected photovoltaic panel is acquired in real time, and analyzed after acquisition to generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively, which specifically includes the following steps:

[0014] Obtain the operating status information of each affected photovoltaic panel in real time and perform preprocessing after acquisition;

[0015] Extracting photovoltaic panel irradiation information and photovoltaic panel power dynamic information from the pre-processed operation status information of each affected photovoltaic panel;

[0016] The extracted photovoltaic panel irradiation information and photovoltaic panel power dynamic information are analyzed to generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively.

[0017] Preferably, the logic for obtaining the reflected light intensity contribution coefficient of each affected photovoltaic panel is as follows:

[0018] Extract the irradiation information of the photovoltaic panels from the pre-processed operating status information of each affected photovoltaic panel, including the solar irradiation intensity received by the front of each affected photovoltaic panel at different times within a period of time, the irradiation intensity of the reflected light received, and the actual area of ​​​​light received, and use the function , and To express, For time point, Indicates that within a period of time Moment The solar radiation intensity received by the front side of the affected photovoltaic panel is Indicates that within a period of time Moment The reflected light irradiance intensity received by each affected photovoltaic panel is Indicates that within a period of time Moment The actual light-receiving area of ​​the affected photovoltaic panels is defined as the time period , , is a positive integer;

[0019] Calculate the reflected light intensity contribution coefficient of each affected photovoltaic panel. The specific calculation formula is as follows:

[0020]

[0021] In the formula, For the The reflected light intensity contribution coefficient of the affected photovoltaic panel.

[0022] Preferably, the logic for obtaining the power fluctuation response index of each affected photovoltaic panel is as follows:

[0023] The photovoltaic panel power dynamic information is extracted from the pre-processed operating status information of each affected photovoltaic panel, including the output power of each affected photovoltaic panel at different times within a period of time, the change rate of the received reflected light irradiation intensity, and the efficiency of converting irradiation energy into electrical energy, and the function is used to calculate the power of each affected photovoltaic panel according to the time series. , and To express, For time point, Indicates that within a period of time Moment The output power of the affected photovoltaic panels, Indicates that within a period of time Moment The rate of change of the reflected light irradiance intensity received by the affected photovoltaic panel, Indicates that within a period of time Moment The efficiency of the affected photovoltaic panels in converting irradiation energy into electrical energy is defined as the time period , , is a positive integer;

[0024] Calculate the power fluctuation response index of each affected photovoltaic panel. The specific calculation formula is as follows:

[0025]

[0026] In the formula, For the The power fluctuation response index of the affected photovoltaic panels.

[0027] Preferably, a reflected light power regulation evaluation model is constructed based on the generated reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel to generate a power regulation coefficient of each affected photovoltaic panel, specifically comprising the following steps:

[0028] Collect the reflected light intensity contribution coefficients, power fluctuation response indexes and corresponding power regulation coefficients of several affected photovoltaic panels generated in the past period of time, and calibrate them as , and , Indicates the reflected light intensity contribution coefficients, power fluctuation response indexes and corresponding power regulation coefficient numbers of several affected photovoltaic panels generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set;

[0029] The multivariate regression model is selected as the reflected light power adjustment evaluation model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula:

[0030] In the formula, , and is the regression coefficient;

[0031] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of

[0032] Using the final determined regression coefficient, the reflected light intensity contribution coefficient of each affected photovoltaic panel generated in real time is input into the constructed reflected light power regulation evaluation model. and power fluctuation response index , generate the power regulation coefficient of each affected photovoltaic panel in real time .

[0033] Preferably, the power adjustment coefficients of each affected photovoltaic panel generated in real time are The power adjustment coefficient threshold interval is set A comparison is conducted, and the degree of power fluctuation of each affected photovoltaic panel due to reflected light is evaluated based on the comparison results. Based on the evaluation results, each affected photovoltaic panel is divided into low-fluctuation photovoltaic panel, medium-fluctuation photovoltaic panel and high-fluctuation photovoltaic panel. The specific comparison analysis and division are as follows:

[0034] like , the power fluctuation degree of the affected photovoltaic panel caused by reflected light is low-level fluctuation, and the affected photovoltaic panel is classified as a low-fluctuation photovoltaic panel;

[0035] like , the power fluctuation degree of the affected photovoltaic panel caused by reflected light is medium fluctuation, and the affected photovoltaic panel is classified as a medium fluctuation photovoltaic panel;

[0036] like The power fluctuation degree of the affected photovoltaic panel due to reflected light is a high degree of fluctuation, and the affected photovoltaic panel is classified as a high-fluctuation photovoltaic panel.

[0037] Preferably, according to the classification results of each affected photovoltaic panel, corresponding power regulation and energy allocation measures are respectively taken for the low-fluctuation photovoltaic panel, the medium-fluctuation photovoltaic panel and the high-fluctuation photovoltaic panel, specifically:

[0038] For the affected PV panels classified as low-fluctuation PV panels, the power regulation and energy allocation measures taken are: maintain the current operating status without additional adjustments to ensure that they continue to operate under a stable power output state, and monitor their operating status in real time to record their long-term stable performance and provide reference for subsequent evaluations;

[0039] For the affected photovoltaic panels classified as medium-fluctuation photovoltaic panels, the power regulation and energy allocation measures taken are: adjust the power output parameters of the affected photovoltaic panels to reduce the impact of power fluctuations on the overall system, optimize the energy interaction efficiency with the energy storage equipment, and continuously track the operating data of the affected photovoltaic panels to dynamically adjust the power regulation strategy to avoid aggravated fluctuations;

[0040] For the affected photovoltaic panels classified as high-fluctuation photovoltaic panels, the power regulation and energy allocation measures taken are: immediately activate the power limitation mechanism to limit the power output of the high-fluctuation photovoltaic panels to a safe range, and prioritize the power output of other stable photovoltaic panels to compensate for load demand. At the same time, the operating status and causes of fluctuations of the high-fluctuation photovoltaic panels are analyzed.

[0041] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0042] 1. The present invention obtains the basic operating information and environmental information of photovoltaic panels in real time, combines the influencing factors of reflected light, accurately identifies photovoltaic panels affected by reflected light and calibrates them. This process achieves accurate monitoring of the impact of reflected light through efficient data collection and dynamic analysis, laying the foundation for subsequent evaluation and adjustment. By generating the reflected light intensity contribution coefficient and the power fluctuation response index, the system can dynamically quantify the static and dynamic impact of reflected light on the power fluctuation of photovoltaic panels, avoiding the rough adjustment method in the prior art that only relies on global light intensity data, and effectively improving the adaptability to complex lighting environments. The present invention significantly enhances the response speed and detection accuracy of photovoltaic systems under heterogeneous lighting conditions, and solves the problem that traditional methods are difficult to identify the impact of reflected light in real time.

[0043] 2. The present invention constructs a reflected light power regulation evaluation model, takes the reflected light intensity contribution coefficient and the power fluctuation response index as input, and uses a multivariate regression model to dynamically generate a power regulation coefficient. Combined with the preset power regulation coefficient threshold interval, the system can scientifically evaluate the degree of power fluctuation of photovoltaic panels caused by reflected light, and intelligently divide it into three categories: low fluctuation, medium fluctuation and high fluctuation. The advantage of this solution is that the classification method has a clear physical meaning, and the adjustment measures are accurately adapted to the fluctuation characteristics of each type of photovoltaic panel, which improves the scientificity and pertinence of the system regulation. At the same time, the dynamic adjustment capability of the threshold interval makes the evaluation and classification more robust, and can adapt to the operating requirements under different environmental conditions, thereby ensuring the stability and efficiency of the photovoltaic system under complex working conditions.

[0044] 3. The present invention achieves a global improvement in the operating status of the photovoltaic system through targeted power regulation and energy allocation of photovoltaic panel classification results, as well as real-time adjustment of evaluation models and dynamic optimization of regulation strategies. Low-fluctuation photovoltaic panels ensure long-term stability through state monitoring, medium-fluctuation photovoltaic panels reduce system interference through dynamic regulation, and high-fluctuation photovoltaic panels protect equipment safety through power limitation and scheduling switching. This comprehensive regulation strategy significantly reduces problems such as excessive local power and system regulation lag caused by reflected light, while optimizing the energy interaction efficiency of energy storage equipment and ensuring the stability and reliability of power output. The dynamic optimization mechanism further enhances the system's adaptability to environmental changes, improves the overall operating efficiency and equipment life of the photovoltaic system, and provides comprehensive technical support for the efficient and safe operation of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 It is a flow chart of the intelligent power regulation and energy optimization distribution method of the array photovoltaic panel of the present invention. DETAILED DESCRIPTION

[0047] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0048] The present invention provides Figure 1 The method for intelligent power regulation and energy optimization distribution of array photovoltaic panels shown in the figure specifically includes the following steps:

[0049] During the operation of the array photovoltaic panel, the basic operation information of each photovoltaic panel in the array photovoltaic panel is obtained in real time, and the photovoltaic panel affected by the reflected light is determined in combination with the environmental information, and is marked as the affected photovoltaic panel;

[0050] To achieve real-time acquisition of basic operating information of each photovoltaic panel in the array photovoltaic panel, a variety of sensor modules can be installed for each photovoltaic panel, including voltage sensors, current sensors and temperature sensors, etc. These sensors can monitor the key operating parameters of the photovoltaic panel in real time. The sensor module of each photovoltaic panel uploads the collected operating data to the central control system or distributed control node through the data acquisition module. These data are encrypted and integrity checked during the transmission process to ensure the accuracy and security of the data. The central control system uses the collected data to update the operating status information library of the photovoltaic panel in real time, and preliminarily organizes and stores the operating information through the data analysis module to ensure that all basic operating information can be obtained and called in real time. In addition, the system software manages the sampling frequency of the sensor through the scheduling protocol, and dynamically adjusts the accuracy and speed of data acquisition to meet the real-time requirements in different scenarios.

[0051] In order to determine the photovoltaic panels affected by reflected light, it is necessary to combine environmental information and operating data to establish a reflected light impact assessment model. The specific implementation method is: install ambient light sensors around each photovoltaic panel. These sensors can monitor the irradiation intensity, light incident angle and reflected light ratio around the photovoltaic panel in real time. At the same time, combined with the inclination information and surface reflectivity parameters of each photovoltaic panel, the reflected light intensity contribution value received by each photovoltaic panel is calculated through advanced mathematical formulas. The reflected light intensity contribution value is compared with the reflected light impact threshold preset by the system. The photovoltaic panels that exceed the threshold are calibrated as photovoltaic panels affected by reflected light. In addition, in order to improve the calibration accuracy, the system will also combine the basic operating information of the photovoltaic panel (such as power output and voltage fluctuations) to verify the actual impact of reflected light intensity on power changes, and further improve the accuracy of the calibration of the affected photovoltaic panels. In this way, the system can dynamically identify and calibrate the affected photovoltaic panels in the photovoltaic array.

[0052] The purpose of doing so is to solve the core problem that existing technologies cannot identify photovoltaic panels affected by reflected light in real time, and ensure that the system can accurately adjust the power output to achieve optimal energy distribution. Since the rapid change in the intensity of reflected light can cause the power output of some photovoltaic panels to be much higher than that of other photovoltaic panels, if the existing system fails to perceive this local fluctuation in time, it will cause the overall power regulation of the photovoltaic array to fail, ultimately affecting the power generation efficiency and the long-term stability of the system. By obtaining the basic operating information of the photovoltaic panels in real time, the system can fully grasp the real-time operating status of the photovoltaic array, and by combining environmental information for analysis and calibration, it can accurately identify the degree of influence of reflected light on each photovoltaic panel and calibrate the affected photovoltaic panels in time. This provides a data basis and judgment basis for the subsequent construction of an evaluation model and optimization of the power allocation strategy, enabling the system to achieve dynamic adjustment under complex lighting conditions, solve problems such as overheating, equipment damage, and energy storage system failure caused by excessive local power, thereby improving the efficiency and reliability of the photovoltaic system.

[0053] Acquire the operating status information of each affected photovoltaic panel in real time, analyze it after acquisition, and generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively;

[0054] In this embodiment, the operating status information of each affected photovoltaic panel is obtained in real time, and analyzed after acquisition to generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively, which specifically includes the following steps:

[0055] Obtain the operating status information of each affected photovoltaic panel in real time and perform preprocessing after acquisition;

[0056] Real-time acquisition of the operating status information of each affected photovoltaic panel can be achieved by deploying a sensor network on each photovoltaic panel. These sensors include ambient light sensors, current sensors, voltage sensors, and temperature sensors, which are used to monitor key parameters such as irradiation intensity, output current, output voltage, and surface temperature of the photovoltaic panel. The data collected by the sensors are transmitted to the data acquisition terminal or central control system through wireless communication modules (such as Zigbee, LoRa, or Wi-Fi) or wired communication modules (such as Ethernet). The data acquisition terminal will perform preliminary verification and timestamp marking on the data of all sensor nodes to ensure data synchronization. The central control system dynamically adjusts the sampling frequency through real-time scheduling strategies, and increases the sampling frequency in highly dynamic change scenarios according to the operating status of the photovoltaic panel and changes in the external environment, thereby ensuring the real-time and accuracy of the data. The entire process is coordinated by the software through a scheduling algorithm to achieve seamless data collection and real-time update.

[0057] Preprocessing is an important part of ensuring the accuracy and consistency of operating status information. Its main purpose is to remove noise and outliers in the collected data and standardize the data into a unified format for subsequent analysis. First, since the sensor may be subject to external interference (such as electromagnetic noise, transmission delay or hardware error), there may be random noise and outliers in the collected data. Preprocessing can eliminate noise through filtering algorithms (such as Kalman filtering or low-pass filtering) to ensure data smoothness. Secondly, the data may contain erroneous values ​​or extreme values. The abnormal data can be filtered out and replaced or eliminated through anomaly detection algorithms (such as methods based on threshold detection or statistical analysis). In addition, preprocessing also requires unified formatting of the output data of different sensors, such as converting voltage and current data into power units, or aligning timestamps to ensure synchronization of various types of data. All preprocessing operations are implemented at the software level, and are automatically completed by the data cleaning module through preset algorithms and rules. Finally, the cleaned data is stored in the operating status information library to provide high-quality input data for subsequent analysis.

[0058] Extracting photovoltaic panel irradiation information and photovoltaic panel power dynamic information from the pre-processed operation status information of each affected photovoltaic panel;

[0059] Extracting the PV panel irradiation information and PV panel power dynamic information from the pre-processed operating status information of each affected PV panel can be achieved by setting specific data parsing rules in the data processing module. Specifically, the system stores the pre-processed operating status information in a database or real-time cache, where each data record contains multiple fields, such as timestamp, irradiation intensity, current, voltage, temperature and other parameters. The data processing module automatically extracts irradiation-related fields (such as direct irradiation intensity and reflected light intensity) from the data record as PV panel irradiation information based on field labels or attribute identifiers, and extracts power dynamic-related fields (such as voltage, current, power output change rate) as PV panel power dynamic information. The extraction process is completed through data filters. The software matches the field name or type and classifies the qualified fields according to preset rules to form two independent high-quality data streams. At the same time, the system performs timestamp proofreading and synchronization checks on the extracted data streams to ensure that the time points of the PV panel irradiation information and the power dynamic information are consistent, providing an accurate data basis for subsequent analysis and parameter generation.

[0060] The extracted photovoltaic panel irradiation information and photovoltaic panel power dynamic information are analyzed to generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively.

[0061] In this embodiment, the logic for obtaining the reflected light intensity contribution coefficient of each affected photovoltaic panel is as follows:

[0062] Extract the irradiation information of the photovoltaic panels from the pre-processed operating status information of each affected photovoltaic panel, including the solar irradiation intensity received by the front of each affected photovoltaic panel at different times within a period of time, the irradiation intensity of the reflected light received, and the actual area of ​​​​light received, and use the function , and To express, For time point, Indicates that within a period of time Moment The solar radiation intensity received by the front side of the affected photovoltaic panel is Indicates that within a period of time Moment The reflected light irradiance intensity received by each affected photovoltaic panel is Indicates that within a period of time Moment The actual light-receiving area of ​​the affected photovoltaic panels is defined as the time period , , is a positive integer;

[0063] To obtain the three types of data, namely, "the solar radiation intensity received by the front of each affected photovoltaic panel at different times over a period of time, the reflected light radiation intensity received, and the actual illuminated area", it can be achieved by deploying a sensor network and combining the geometric parameters of the photovoltaic panel for calculation. Specifically, the solar radiation intensity can be collected in real time by the ambient light sensor installed on the front of the photovoltaic panel. The sensor directly measures the direct solar radiation intensity received on the front. The data is uploaded to the data acquisition system through a wireless communication module (such as Zigbee or LoRa), and the timestamp is recorded to form a time series; the reflected light radiation intensity is collected by the ambient light sensor network deployed around the photovoltaic panel. The network sensor can measure the radiation intensity of reflected light in different directions. The system will combine the inclination and position parameters of the photovoltaic panel to calculate the reflected light intensity actually projected onto the surface of the photovoltaic panel through a geometric optical model. As for the actual illuminated area, it can be calculated based on the geometric size parameters of the photovoltaic panel and the real-time monitored solar incident angle and reflected light incident angle through the three-dimensional geometric model in the software. The specific formula will take into account the angle between the azimuth and inclination of the photovoltaic panel and the incident light direction. The entire data acquisition process is automatically managed by the software through a scheduling algorithm, including sensor data sampling, noise filtering, geometric model calculation, and result storage, ensuring the accuracy and synchronization of dynamic acquisition and calculation of each type of data at different times. This method combines real-time acquisition and model calculation to fully generate the required time series data and provide comprehensive support for subsequent analysis.

[0064] Calculate the reflected light intensity contribution coefficient of each affected photovoltaic panel. The specific calculation formula is as follows:

[0065]

[0066] In the formula, For the The reflected light intensity contribution coefficient of the affected photovoltaic panel.

[0067] This formula captures the dynamic cumulative effect of reflected light on the irradiation environment of the photovoltaic panel over a period of time through integral operation. The numerator converts the square of the reflected light irradiation intensity into Introduced to amplify the effect of high-intensity reflected light, while adding a logarithmic correction term to the direct radiation intensity , making the sensitivity of low-intensity direct light to the contribution of reflected light more prominent; in addition, the actual light-receiving area of ​​the photovoltaic panel As a correction factor for the spatial dimension, it ensures that the formula can truly reflect the contribution of reflected light under different photovoltaic panel area conditions. The denominator uses the square of the direct light intensity. As a comparison benchmark, combined with the reflected light intensity and the area squared The combined items further balance the relative weights of direct light and reflected light, ensuring the scientificity and rationality of normalization. The integration results are averaged to avoid the influence of time span differences on the calculation results, and a coefficient is generated that can fully quantify the contribution of reflected light to the irradiation environment of photovoltaic panels. This design logic fully considers the interaction between reflected light and direct light, the dynamic balance of high and low intensities, and the comprehensive correction of time and space dimensions, and can accurately evaluate the static impact of reflected light on power fluctuations.

[0068] No. Contribution coefficient of reflected light intensity of the affected photovoltaic panels The size of directly reflects the contribution of reflected light to the irradiation environment of the photovoltaic panel, thereby indirectly evaluating the potential for power fluctuations caused by reflected light. Specifically, when When the value is large, it means that the reflected light accounts for a significant proportion of the total irradiance. At this time, the dynamic fluctuation of the reflected light may be more sensitive to the power output of the photovoltaic panel, which is easy to cause large power fluctuations. On the contrary, when When the value is small, it means that the reflected light contributes less to the irradiation of the photovoltaic panel, and its fluctuation has a lower impact on the power output, and the possibility and amplitude of power fluctuation are also weakened accordingly. Therefore, the size of the reflected light intensity contribution coefficient is a key indicator for evaluating the degree of power fluctuation caused by reflected light, and can provide a static reference for further optimizing the power regulation strategy.

[0069] In this embodiment, the logic for obtaining the power fluctuation response index of each affected photovoltaic panel is as follows:

[0070] The photovoltaic panel power dynamic information is extracted from the pre-processed operating status information of each affected photovoltaic panel, including the output power of each affected photovoltaic panel at different times within a period of time, the change rate of the received reflected light irradiation intensity, and the efficiency of converting irradiation energy into electrical energy, and the function is used to calculate the power of each affected photovoltaic panel according to the time series. , and To express, For time point, Indicates that within a period of time Moment The output power of the affected photovoltaic panels, Indicates that within a period of time Moment The rate of change of the reflected light irradiance intensity received by the affected photovoltaic panel, Indicates that within a period of time Moment The efficiency of the affected photovoltaic panels in converting irradiation energy into electrical energy is defined as the time period , , is a positive integer;

[0071] Acquiring the three types of data, namely, the output power of each affected photovoltaic panel at different times within a period of time, the rate of change of the received reflected light irradiance intensity, and the efficiency of converting irradiance energy into electrical energy, requires the coordinated work of the sensor network and the data processing system. First, the output power is measured in real time by the voltage sensor and current sensor installed on the photovoltaic panel. The system transmits these raw data to the central processing unit for dynamic calculation and generates the corresponding output power data sequence at each time point; secondly, the rate of change of reflected light irradiance intensity is based on the data collected by the ambient light sensor network deployed around the photovoltaic panel. These sensors can continuously monitor the irradiance intensity of reflected light. The system analyzes the trend of light intensity change at consecutive moments and obtains the dynamic rate of change of reflected light irradiance intensity; finally, the efficiency of converting irradiance energy into electrical energy depends on the comprehensive processing of output power data and irradiance data. The system combines the total irradiance intensity received by the photovoltaic panel, including direct solar irradiation and reflected light irradiation, and dynamically evaluates the conversion efficiency of the photovoltaic panel at different times by comparing the relationship between the actual output power and the total received irradiance energy. The entire data acquisition process is automatically scheduled and managed by the software module to ensure the timing synchronization of sensor data collection. After data cleaning and processing, accurate time series are generated to provide comprehensive and reliable basic information for subsequent analysis.

[0072] Calculate the power fluctuation response index of each affected photovoltaic panel. The specific calculation formula is as follows:

[0073]

[0074] In the formula, For the The power fluctuation response index of the affected photovoltaic panels.

[0075] The power fluctuation response index The calculation formula of fully quantifies the response characteristics of the dynamic change of reflected light to the fluctuation of photovoltaic panel output power through integration and weighted operations. The design of the formula fully considers multi-dimensional factors such as the change rate of reflected light, the dynamic fluctuation of power output and the photoelectric conversion efficiency. Among them, the numerator part The rate of change of reflected light The square of , emphasizing the intensity of the reflected light fluctuations, combined with the instantaneous rate of change of power output , describes the sensitivity of PV panels in dynamic response; It reflects the influence of photoelectric conversion efficiency on the fluctuation amplitude. The square of the output power and the joint term of the reflected light change rate and efficiency are combined to balance and normalize the dynamic response characteristics, ensuring that the formula results can objectively reflect the intensity of fluctuations without being affected by extreme conditions. The dynamic response over the entire time period is accumulated and normalized to the average fluctuation response per unit time through the integral operation. The formula can accurately capture the dynamic characteristics of power fluctuations caused by reflected light, providing key support for optimizing the operation strategy of photovoltaic systems.

[0076] No. Power fluctuation response index of the affected photovoltaic panels The size of directly reflects the sensitivity and degree of the dynamic change of reflected light to the fluctuation of the output power of the photovoltaic panel, and is therefore an important indicator for evaluating the power fluctuation caused by reflected light. When the value is large, it means that the rapid change of reflected light intensity significantly affects the power output of the photovoltaic panel, and the power fluctuation responds more violently to the change of reflected light, which usually means that the power output of the photovoltaic panel is unstable and has a high degree of fluctuation. On the contrary, when When the value is small, it means that the photovoltaic panel responds less to the change of reflected light, the power output is relatively stable, and the fluctuation is low. The size of the power fluctuation caused by reflected light of each photovoltaic panel can be accurately evaluated, thereby identifying photovoltaic panels with higher fluctuation sensitivity, providing a scientific basis for subsequent targeted power regulation measures.

[0077] A reflected light power regulation evaluation model is constructed for the generated reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel, and the power regulation coefficient of each affected photovoltaic panel is generated. After the generation, the power fluctuation degree of each affected photovoltaic panel caused by reflected light is evaluated, and the affected photovoltaic panels are divided into low-fluctuation photovoltaic panels, medium-fluctuation photovoltaic panels and high-fluctuation photovoltaic panels according to the evaluation results;

[0078] In this embodiment, a reflected light power regulation evaluation model is constructed based on the generated reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel to generate a power regulation coefficient of each affected photovoltaic panel, specifically including the following steps:

[0079] Collect the reflected light intensity contribution coefficients, power fluctuation response indexes and corresponding power regulation coefficients of several affected photovoltaic panels generated in the past period of time, and calibrate them as , and , Indicates the reflected light intensity contribution coefficients, power fluctuation response indexes and corresponding power regulation coefficient numbers of several affected photovoltaic panels generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set;

[0080] The collection of the reflected light intensity contribution coefficient, power fluctuation response index and corresponding power regulation coefficient of several affected photovoltaic panels generated in the past period of time can be achieved through the data recording module and database management system in the software platform. Specifically, the software will automatically record the reflected light intensity contribution coefficient and power fluctuation response index generated in real time by each affected photovoltaic panel. These data are transmitted to the central processing system through the data acquisition module at each time point, and are stored in association with the power regulation coefficient generated in real time by the power regulation model. In order to achieve systematic management of historical data, the database will classify the data according to timestamp, photovoltaic panel number and coefficient type, and mark the generation time and source information to form a complete data record entry. By configuring the query and filtering functions, the software can extract relevant data within the specified time period in the past from the database, and export the data set for model training based on the preset time window (such as the past hour, day or week). At the same time, the data recording module can also automatically clean up expired or redundant data to ensure efficient use of storage space. This method combines real-time data acquisition with historical data management to provide a high-quality data input source for model construction.

[0081] Will The value range of is limited to positive integers greater than or equal to 3 to ensure that the specific values ​​of all regression coefficients can be calculated in the multiple regression model. The target equation contains three unknown regression coefficients: , and In order to accurately solve these coefficients, at least three independent equations must be constructed, so at least three sample data are needed to meet the basic mathematical solution requirements. At the same time, from a statistical point of view, increasing the number of samples can improve the stability and accuracy of the regression model, but when the number of samples is insufficient (such as less than 3), the equations will become unsolvable or the solution results will be unreliable. Therefore, Limiting it to a positive integer greater than or equal to 3 can not only ensure the basic solution conditions of the model, but also provide more sufficient data support for subsequent model optimization and coefficient adjustment, thereby enhancing the robustness and generalization ability of the regression model.

[0082] The multivariate regression model is selected as the reflected light power adjustment evaluation model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula:

[0083] In the formula, , and is the regression coefficient;

[0084] The multivariate regression model is a statistical analysis method used to study the relationship between a dependent variable (target variable) and multiple independent variables (predictor variables). Its core is to quantify the impact of changes in independent variables on the target variable by fitting linear equations. The reason for choosing the multivariate regression model as the reflected light power regulation evaluation model is that the model can comprehensively consider the impact of the two key parameters, the reflected light intensity contribution coefficient and the power fluctuation response index, on the power regulation coefficient. Through training with historical data sets, the regression coefficient of the model can be optimized so that it can accurately describe the linear relationship between these two factors and the power regulation coefficient. Among them, the regression coefficient represents the base value of the power adjustment coefficient when all predictors are zero, which is the intercept of the model; Indicates the influence weight of the reflected light intensity contribution coefficient on the power adjustment coefficient, reflecting the effect of the static lighting environment; It represents the influence weight of the power fluctuation response index on the power regulation coefficient, reflecting the contribution of dynamic power fluctuation. By optimizing and adjusting these three regression coefficients, the model can accurately predict the real-time power regulation coefficient, thus providing scientific support for the power regulation strategy of photovoltaic panels.

[0085] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of

[0086] Optimizing the regression coefficient of the regression model by minimizing the error between the predicted value and the actual value can be achieved through an optimization algorithm based on the least squares method, specifically by constructing an error function and iteratively solving the optimal value of the regression coefficient. When implementing, first collect the corresponding data of the reflected light intensity contribution coefficient, the power fluctuation response index and the historical power adjustment coefficient, substitute them into the initial equation of the multivariate regression model, and calculate the error between the predicted value and the actual value of each sample; then define an error function, such as the mean square error function, and sum the squared errors of all samples as the target value, and continuously adjust the regression coefficient through optimization algorithms such as the gradient descent method to minimize the error function, and finally determine , and The reason for this is that minimizing the error function can optimize the prediction ability of the regression model, ensuring that the generated power regulation evaluation model has high accuracy and generalization in reflected light power regulation, thereby improving the reliability and adaptability of the photovoltaic panel power regulation strategy.

[0087] Using the final determined regression coefficient, the reflected light intensity contribution coefficient of each affected photovoltaic panel generated in real time is input into the constructed reflected light power regulation evaluation model. and power fluctuation response index , generate the power regulation coefficient of each affected photovoltaic panel in real time .

[0088] In this embodiment, the power adjustment coefficients of each affected photovoltaic panel generated in real time are The power adjustment coefficient threshold interval is set A comparison is conducted, and the degree of power fluctuation of each affected photovoltaic panel due to reflected light is evaluated based on the comparison results. Based on the evaluation results, each affected photovoltaic panel is divided into low-fluctuation photovoltaic panel, medium-fluctuation photovoltaic panel and high-fluctuation photovoltaic panel. The specific comparison analysis and division are as follows:

[0089] like , the power fluctuation degree of the affected photovoltaic panel caused by reflected light is low-level fluctuation, and the affected photovoltaic panel is classified as a low-fluctuation photovoltaic panel;

[0090] This situation means that the reflected light of the photovoltaic panel has a relatively small impact, its power output is relatively stable, and the dynamic impact of reflected light on its power fluctuation is weak. Since the power regulation coefficient is low, it indicates that the contribution of reflected light intensity is not large or the degree of power fluctuation response is not significant, so the operation status of the photovoltaic panel is within the normal range and the system does not need to make special adjustments or interventions to its power. The impact is that this type of photovoltaic panel can be given priority as a stable operating part during the power regulation process, which helps the overall system to concentrate resources and strategies to deal with photovoltaic panels with higher volatility during power regulation, thereby optimizing regulation efficiency and system resource utilization.

[0091] like , the power fluctuation degree of the affected photovoltaic panel caused by reflected light is medium fluctuation, and the affected photovoltaic panel is classified as a medium fluctuation photovoltaic panel;

[0092] This situation indicates that the dynamic impact of the reflected light of the photovoltaic panel on its power output is at a medium level, and the power fluctuation has neither reached the level of significant interference nor is it completely stable. The operating status of the photovoltaic panel with a power regulation coefficient within the threshold range may fluctuate to a certain extent with changes in ambient light, but it is generally controllable. The impact is that this type of photovoltaic panel needs to be given medium priority in the power regulation strategy, and the system may need to adjust its operating parameters appropriately based on real-time data to ensure that its fluctuation does not expand further, while avoiding local efficiency reduction or uneven power distribution due to reflected light fluctuations.

[0093] like The power fluctuation degree of the affected photovoltaic panel due to reflected light is a high degree of fluctuation, and the affected photovoltaic panel is classified as a high-fluctuation photovoltaic panel.

[0094] This situation means that the photovoltaic panel is significantly affected by the reflected light, and the power fluctuation is high, which may have a negative impact on the stability of the entire photovoltaic array. The high power regulation coefficient indicates that the power fluctuation response caused by the reflected light is significant, which requires the system to pay great attention. The impact is that such photovoltaic panels may be in a high-load state, and long-term power fluctuations may cause the photovoltaic panels to overheat or shorten their lifespan, while posing a threat to the charging and discharging efficiency of the energy storage system and the output stability of the overall power grid. Therefore, the system needs to give priority to active power regulation measures for these photovoltaic panels, such as limiting their output power, optimizing energy distribution strategies, and even load switching when necessary, to ensure the reliability and operating efficiency of the entire photovoltaic system.

[0095] The "pre-set power regulation coefficient threshold interval" can be determined by combining a dynamic adjustment algorithm based on historical data analysis and statistics. The specific implementation method is to first collect a large number of historical data on the power regulation coefficient, reflected light intensity contribution coefficient and power fluctuation response index generated during the operation of photovoltaic panels, and construct a time series data set containing multivariate relationships; then use the statistical analysis module in the software to perform frequency distribution analysis on the historical data of the power regulation coefficient, and preliminarily determine the normal range of the power regulation coefficient by calculating the mean and standard deviation of the data. Then, combined with the actual operating conditions (such as different seasons, photovoltaic panel types or ambient light conditions), the software can group and distribute the threshold intervals, use normal distribution or non-parametric methods to model, and finally generate a more accurate threshold interval. In addition, to ensure that the threshold dynamically adapts to the actual operating conditions, the software can introduce a real-time update mechanism to evaluate the applicability of the threshold interval based on the latest real-time data, and dynamically correct the minimum and maximum values ​​through weighted sliding average or adaptive threshold adjustment algorithm. This method combines historical analysis with dynamic update of real-time data to ensure that the power regulation coefficient threshold interval can adapt to the complex operating environment of the photovoltaic system, thereby providing a scientific basis for accurately dividing the degree of fluctuation of photovoltaic panels.

[0096] According to the classification results of each affected photovoltaic panel, corresponding power regulation and energy allocation measures are taken for low-fluctuation photovoltaic panels, medium-fluctuation photovoltaic panels and high-fluctuation photovoltaic panels respectively;

[0097] In this embodiment, according to the classification results of each affected photovoltaic panel, corresponding power regulation and energy allocation measures are respectively taken for the low-fluctuation photovoltaic panel, the medium-fluctuation photovoltaic panel and the high-fluctuation photovoltaic panel, specifically:

[0098] For the affected photovoltaic panels classified as low-fluctuation photovoltaic panels, the power regulation and energy allocation measures taken are: maintain the current operating status without additional adjustment to ensure that they continue to operate under a stable power output state, and monitor their operating status in real time to record their long-term stable performance and provide reference for subsequent evaluation;

[0099] For low-fluctuation photovoltaic panels, maintaining their current operating status and real-time monitoring of operating data can be achieved through the status monitoring module and data storage module in the software. Specifically, the status monitoring module continuously receives real-time operating data of low-fluctuation photovoltaic panels, such as power output, voltage, current and temperature, and verifies the stability of its operating status through trend analysis algorithms. If the data fluctuation range is always within the set range, the system will maintain the current power output parameters of the photovoltaic panel without additional adjustments. At the same time, the data storage module automatically archives the monitored operating data and records the stable operating performance in time series to provide a reference for subsequent system evaluation and optimization. The reason for this is that the low-fluctuation photovoltaic panels are already in a stable state. Maintaining the status quo can not only save system adjustment resources, but also ensure its operating stability through continuous monitoring, laying the foundation for the efficient operation of the overall system.

[0100] For the affected photovoltaic panels classified as medium-fluctuation photovoltaic panels, the power regulation and energy allocation measures taken are: adjust the power output parameters of the affected photovoltaic panels to reduce the impact of power fluctuations on the overall system, optimize the energy interaction efficiency with the energy storage equipment, and continuously track the operating data of the affected photovoltaic panels to dynamically adjust the power regulation strategy to avoid aggravated fluctuations;

[0101] For medium-fluctuation photovoltaic panels, this can be achieved through the power regulation module and the energy storage management module by adjusting the power output parameters and optimizing the energy interaction with the energy storage device. Specifically, the power regulation module uses the real-time monitored power fluctuation data to dynamically adjust the output power of the photovoltaic panel, such as appropriately reducing or smoothing the output power curve to reduce the interference of the fluctuation on the overall system; at the same time, the energy storage management module optimizes the energy storage and release strategy according to the power fluctuation degree of the photovoltaic panel, such as increasing the energy storage absorption ratio when the photovoltaic panel power fluctuates, and reducing the impact of fluctuations on load power supply. In addition, the system continuously tracks the operating status of the photovoltaic panel and dynamically adjusts the power regulation strategy through the feedback control mechanism to ensure that its fluctuation degree will not be further aggravated. The reason for this is that the power output of the medium-fluctuation photovoltaic panel has a certain impact on the stability of the system. Through timely adjustment and energy storage coordination, the risk of fluctuations can be effectively reduced and the system operation efficiency can be improved.

[0102] For the affected photovoltaic panels classified as high-fluctuation photovoltaic panels, the power regulation and energy allocation measures taken are: immediately start the power limitation mechanism to limit the power output of the high-fluctuation photovoltaic panels to a safe range, and give priority to allocating the power output of other stable photovoltaic panels to compensate for load demand. At the same time, the operating status and causes of fluctuations of the high-fluctuation photovoltaic panels are analyzed, and the high-fluctuation photovoltaic panels are switched to standby mode through the scheduling system to protect the photovoltaic panel equipment and ensure the stability of the system operation.

[0103] For high-fluctuation photovoltaic panels, the power limiting mechanism, resource scheduling and switching to standby mode can be achieved through the system's power limiting module, resource scheduling module and load switching module. Specifically, the power limiting module will automatically limit its power output to a safe range based on the power fluctuation amplitude of the high-fluctuation photovoltaic panel monitored in real time; the resource scheduling module will give priority to calling the power output of other stable photovoltaic panels to compensate for the insufficient load caused by the power limitation of the high-fluctuation photovoltaic panel. If the high fluctuation continues, the load switching module will automatically switch the photovoltaic panel to standby mode, stop its power output to the system, and record the cause of the fluctuation and abnormal operation data for subsequent analysis. The reason for this is that the operating status of high-fluctuation photovoltaic panels may seriously affect the system stability and equipment life. Through multi-layer protection measures to timely reduce its negative impact on the system, the reliability of the photovoltaic system can be effectively guaranteed and the risk of equipment failure can be reduced.

[0104] Continuously monitor the operating status and reflected light impact of the affected photovoltaic panels, and dynamically adjust the reflected light power regulation evaluation model and power regulation strategy based on real-time monitoring data to optimize the operating status of the photovoltaic array and improve the operating efficiency and stability of the photovoltaic system.

[0105] Continuous monitoring of the operating status and reflected light impact of the affected photovoltaic panels can be achieved by combining the sensor network with the data acquisition module. Specifically, the system deploys ambient light sensors, current sensors, voltage sensors and temperature sensors on each photovoltaic panel to collect real-time irradiation intensity, output power, operating temperature and other key parameters, and at the same time, the reflected light irradiation intensity around the photovoltaic panel is recorded in real time through the reflected light monitoring module. The data collected by the sensor is transmitted to the central monitoring system through a wireless or wired communication network, and the data acquisition module timestamps and formats it to ensure the timing consistency and integrity of the data. Subsequently, the data visualization module in the software generates a real-time status monitoring interface, which displays the operating status and reflected light changes of the photovoltaic panel in the form of curves or tables. The reason for this is that through real-time monitoring, the operating dynamics of the photovoltaic panel and the degree to which it is affected by reflected light can be fully grasped, providing real-time data support for subsequent model adjustments and strategy optimization, ensuring that the system is always in the best operating state.

[0106] Dynamically adjusting the reflected light power regulation evaluation model according to real-time monitoring data can be achieved through adaptive algorithms and online training modules. Specifically, the system updates the existing evaluation model through the reflected light intensity contribution coefficient and power fluctuation response index collected in real time, as well as the corresponding power regulation coefficient data, as model input. The online training module uses the new input data to incrementally update the model, and optimizes the regression coefficient or adjusts the model parameters so that the model can adapt to the current operating conditions and environmental changes more accurately. In order to avoid the impact of real-time adjustment on the stability of the model, the system can adopt a sliding window mechanism, only use the data of the most recent period for update, and use the cross-validation method to ensure the prediction accuracy and reliability of the updated model. The reason for this is that the operating environment and reflected light conditions of the photovoltaic panel may change dynamically over time. By continuously adjusting the evaluation model, the adaptability and prediction accuracy of the model can be improved, providing a more reliable basis for the power regulation strategy.

[0107] Dynamically adjusting the power regulation strategy and optimizing the operating status of the photovoltaic array according to real-time monitoring data can be achieved through the intelligent scheduling module and decision engine. Specifically, the system combines the real-time monitored operating status of the photovoltaic panels and the influence of reflected light to dynamically calculate the power regulation coefficient of each photovoltaic panel and compare it with the current power regulation strategy. The decision engine generates new adjustment instructions based on the comparison results, such as adjusting the power output of a specific photovoltaic panel, optimizing the energy interaction with the energy storage system, or redistributing the power load distribution within the array. The intelligent scheduling module transmits these instructions to the execution layer, and verifies the adjusted operating effect through a closed-loop control mechanism to further optimize the overall performance of the photovoltaic array. The reason for this is that by adjusting the power regulation strategy in real time, it can quickly respond to changes in the operating status of the photovoltaic panels and the external environment, minimize the negative impact of fluctuations on the system, and improve the operating efficiency and long-term stability of the photovoltaic system.

[0108] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0109] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0110] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0111] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0112] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0115] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An array photovoltaic panel intelligent power regulation and energy optimization allocation method, characterized in that: The specific steps include: During the operation of the array photovoltaic panel, the basic operation information of each photovoltaic panel in the array photovoltaic panel is obtained in real time, and the photovoltaic panel affected by the reflected light is determined in combination with the environmental information, and is marked as the affected photovoltaic panel; Acquire the operating status information of each affected photovoltaic panel in real time, analyze it after acquisition, and generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively; A reflected light power regulation evaluation model is constructed for the generated reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel, and the power regulation coefficient of each affected photovoltaic panel is generated. After the generation, the power fluctuation degree of each affected photovoltaic panel caused by reflected light is evaluated, and the affected photovoltaic panels are divided into low-fluctuation photovoltaic panels, medium-fluctuation photovoltaic panels and high-fluctuation photovoltaic panels according to the evaluation results; According to the classification results of each affected photovoltaic panel, corresponding power regulation and energy allocation measures are taken for low-fluctuation photovoltaic panels, medium-fluctuation photovoltaic panels and high-fluctuation photovoltaic panels respectively; Continuously monitor the operating status and reflected light impact of the affected photovoltaic panels, and dynamically adjust the reflected light power regulation evaluation model and power regulation strategy based on real-time monitoring data to optimize the operating status of the photovoltaic array and improve the operating efficiency and stability of the photovoltaic system.

2. The method for intelligent power regulation and energy optimization distribution of array photovoltaic panels according to claim 1, characterized in that: The operating status information of each affected photovoltaic panel is obtained in real time, and analyzed after acquisition to generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively, which specifically includes the following steps: Obtain the operating status information of each affected photovoltaic panel in real time and perform preprocessing after acquisition; Extracting photovoltaic panel irradiation information and photovoltaic panel power dynamic information from the pre-processed operation status information of each affected photovoltaic panel; The extracted photovoltaic panel irradiation information and photovoltaic panel power dynamic information are analyzed to generate the reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel respectively.

3. The method for intelligent power regulation and energy optimization distribution of array photovoltaic panels according to claim 2, characterized in that: The logic for obtaining the reflected light intensity contribution coefficient of each affected photovoltaic panel is as follows: Extract the irradiation information of the photovoltaic panels from the pre-processed operating status information of each affected photovoltaic panel, including the solar irradiation intensity received by the front of each affected photovoltaic panel at different times within a period of time, the irradiation intensity of the reflected light received, and the actual area of ​​​​light received, and use the function , and To express, For time point, Indicates that within a period of time Moment The solar radiation intensity received by the front side of the affected photovoltaic panel is Indicates that within a period of time Moment The reflected light irradiance intensity received by each affected photovoltaic panel is Indicates that within a period of time Moment The actual light-receiving area of ​​the affected photovoltaic panels is defined as the time period , , is a positive integer; Calculate the reflected light intensity contribution coefficient of each affected photovoltaic panel. The specific calculation formula is as follows: In the formula, For the The reflected light intensity contribution coefficient of the affected photovoltaic panel.

4. The method for intelligent power regulation and energy optimization distribution of array photovoltaic panels according to claim 3, characterized in that: The logic for obtaining the power fluctuation response index of each affected photovoltaic panel is as follows: The photovoltaic panel power dynamic information is extracted from the pre-processed operating status information of each affected photovoltaic panel, including the output power of each affected photovoltaic panel at different times within a period of time, the change rate of the received reflected light irradiation intensity, and the efficiency of converting irradiation energy into electrical energy, and the function is used to calculate the power of each affected photovoltaic panel according to the time series. , and To express, For time point, Indicates that within a period of time Moment The output power of the affected photovoltaic panels, Indicates that within a period of time Moment The rate of change of the reflected light irradiance intensity received by the affected photovoltaic panel, Indicates that within a period of time Moment The efficiency of the affected photovoltaic panels in converting irradiation energy into electrical energy is defined as the time period , , is a positive integer; Calculate the power fluctuation response index of each affected photovoltaic panel. The specific calculation formula is as follows: In the formula, For the The power fluctuation response index of the affected photovoltaic panels.

5. The method for intelligent power regulation and energy optimization distribution of array photovoltaic panels according to claim 4, characterized in that: A reflected light power regulation evaluation model is constructed based on the generated reflected light intensity contribution coefficient and power fluctuation response index of each affected photovoltaic panel to generate a power regulation coefficient of each affected photovoltaic panel, specifically including the following steps: Collect the reflected light intensity contribution coefficients, power fluctuation response indexes and corresponding power regulation coefficients of several affected photovoltaic panels generated in the past period of time, and calibrate them as , and , Indicates the reflected light intensity contribution coefficients, power fluctuation response indexes and corresponding power regulation coefficient numbers of several affected photovoltaic panels generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; The multivariate regression model is selected as the reflected light power adjustment evaluation model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Using the final determined regression coefficient, the reflected light intensity contribution coefficient of each affected photovoltaic panel generated in real time is input into the constructed reflected light power regulation evaluation model. and power fluctuation response index , generate the power regulation coefficient of each affected photovoltaic panel in real time .

6. The method for intelligent power regulation and energy optimization distribution of array photovoltaic panels according to claim 5, characterized in that: The power regulation coefficient of each affected photovoltaic panel will be generated in real time The power adjustment factor threshold interval is set A comparison is conducted, and the degree of power fluctuation of each affected photovoltaic panel due to reflected light is evaluated based on the comparison results. Based on the evaluation results, each affected photovoltaic panel is divided into low-fluctuation photovoltaic panel, medium-fluctuation photovoltaic panel and high-fluctuation photovoltaic panel. The specific comparison analysis and division are as follows: like , the power fluctuation degree of the affected photovoltaic panel caused by reflected light is low-level fluctuation, and the affected photovoltaic panel is classified as a low-fluctuation photovoltaic panel; like , the power fluctuation degree of the affected photovoltaic panel caused by reflected light is medium fluctuation, and the affected photovoltaic panel is classified as a medium fluctuation photovoltaic panel; like The power fluctuation degree of the affected photovoltaic panel due to reflected light is a high degree of fluctuation, and the affected photovoltaic panel is classified as a high-fluctuation photovoltaic panel.

7. The method for intelligent power regulation and energy optimization distribution of array photovoltaic panels according to claim 6, characterized in that: According to the classification results of each affected photovoltaic panel, corresponding power regulation and energy allocation measures are taken for low-fluctuation photovoltaic panels, medium-fluctuation photovoltaic panels and high-fluctuation photovoltaic panels, specifically: For the affected photovoltaic panels classified as low-fluctuation photovoltaic panels, the power regulation and energy allocation measures taken are: maintain the current operating status without additional adjustment to ensure that they continue to operate under a stable power output state, and monitor their operating status in real time to record their long-term stable performance and provide reference for subsequent evaluation; For the affected photovoltaic panels classified as medium-fluctuation photovoltaic panels, the power regulation and energy allocation measures taken are: adjust the power output parameters of the affected photovoltaic panels to reduce the impact of power fluctuations on the overall system, optimize the energy interaction efficiency with the energy storage equipment, and continuously track the operating data of the affected photovoltaic panels to dynamically adjust the power regulation strategy to avoid aggravated fluctuations; For the affected photovoltaic panels classified as high-fluctuation photovoltaic panels, the power regulation and energy allocation measures taken are: immediately activate the power limitation mechanism to limit the power output of the high-fluctuation photovoltaic panels to a safe range, and prioritize the power output of other stable photovoltaic panels to compensate for load demand. At the same time, the operating status and causes of fluctuations of the high-fluctuation photovoltaic panels are analyzed.

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