Energy storage charging regulation system adapted to photovoltaic devices

Abstract

The application belongs to the technical field of photovoltaic equipment management and control, and particularly relates to an energy storage charging regulation system suitable for photovoltaic equipment, which comprises a comprehensive monitoring output module, a regulation strategy analysis and generation module, a regulation strategy execution control module, an energy storage safety influence analysis module and a background management end; the comprehensive monitoring output module is used for monitoring the operation of photovoltaic equipment, energy storage devices and charging devices, the regulation strategy analysis and generation module is used for processing and analyzing monitoring data and generating optimal energy storage and charging regulation strategies, the regulation strategy execution control module is used for controlling the operation process of the energy storage devices based on the regulation strategies, the stability of the photovoltaic power generation system is enhanced, the energy storage safety hazards of the energy storage devices are analyzed by the energy storage safety influence analysis module, cause investigation and analysis are performed and reasonable improvement measures are taken when a high energy storage safety influence signal is generated, the safe and stable operation of the energy storage devices is ensured, and the system has high intelligence and automation levels.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic equipment control technology, specifically to an energy storage and charging control system adapted to photovoltaic equipment. Background Technology

[0002] Photovoltaic equipment refers to equipment that converts solar energy into electrical energy using the photovoltaic effect. It is widely used in homes, businesses, and industries and is an important means of achieving clean energy utilization. With increasing emphasis on renewable energy, photovoltaic power generation, as an important component of new energy, is continuously expanding its application scale.

[0003] Currently, it is difficult to effectively manage the energy storage and charging of photovoltaic equipment, and it is impossible to reasonably analyze the energy storage risks and stability of the corresponding energy storage devices and accurately assess the ease of control for all energy storage devices. This is not conducive to achieving effective control of energy storage devices and ensuring their safe and stable operation, and the level of intelligence and automation is low.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an energy storage charging control system adapted to photovoltaic equipment, which solves the problems of existing technologies that make it difficult to effectively manage the energy storage charging of photovoltaic equipment, and that cannot reasonably analyze and accurately assess the energy storage risk and stability of corresponding energy storage devices, as well as the low level of intelligence and automation.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The energy storage and charging control system adapted to photovoltaic equipment includes a comprehensive monitoring output module, a control strategy analysis and generation module, a control strategy execution and control module, an energy storage safety impact analysis module, and a back-end management terminal. The comprehensive monitoring output module monitors the operation of photovoltaic equipment, energy storage devices, and charging devices, and sends all monitoring data to the control strategy analysis and generation module.

[0008] The regulation strategy analysis and generation module uses machine learning or deep learning algorithms to process and analyze the collected monitoring data, predict the power generation and electricity demand in the future, automatically generate the optimal energy storage and charging regulation strategy, and send the generated regulation strategy to the regulation strategy execution control module and the back-end management terminal.

[0009] The regulation strategy execution control module controls the operation of the energy storage device based on the regulation strategy. The energy storage safety impact analysis module analyzes the energy storage safety hazards of the energy storage device, generates a high-impact signal or a low-impact signal for energy storage safety through analysis, and sends the high-impact signal for energy storage safety to the back-end management terminal. When the back-end management terminal receives the high-impact signal for energy storage safety, it issues a corresponding warning.

[0010] Furthermore, photovoltaic equipment converts light energy into electrical energy and outputs it to energy storage devices or the power grid; the energy storage device is used to store the electrical energy generated by the photovoltaic equipment and release the electrical energy to the charging device or the power grid when needed, and the energy storage device is a lithium-ion battery pack; the charging device is used to convert the electrical energy in the energy storage device into electrical energy suitable for electric vehicles or other electrical equipment and to charge it, and the charging device has multiple charging modes, including constant current charging, constant voltage charging and pulse charging.

[0011] Furthermore, the specific analysis process of the energy storage safety impact analysis module includes:

[0012] Several detection periods are set within a unit of time. The actual power consumption and actual power output of the energy storage device are collected within the corresponding detection period. The difference between the actual power consumption and the actual power output is calculated and the difference result is marked as the energy storage loss value. The energy storage loss value is compared with the preset energy storage loss threshold. If the energy storage loss value exceeds the preset energy storage loss threshold, the corresponding detection period is marked as the energy storage risk period.

[0013] The energy storage risk time period is obtained by calculating the ratio of the number of energy storage risk periods to the number of detection periods. The energy storage anomaly value is calculated by averaging the energy storage anomaly values ​​of all detection periods within a unit time. The energy storage anomaly value with the largest value within a unit time is marked as the energy storage anomaly value. The energy storage leakage assessment value is obtained by numerically calculating the energy storage risk time period value, the energy storage anomaly value, and the energy storage anomaly value. The energy storage leakage assessment value is compared with the preset energy storage leakage assessment threshold. If the energy storage leakage assessment value exceeds the preset energy storage leakage assessment threshold, a high impact signal for energy storage safety is generated.

[0014] Furthermore, if the energy storage leakage assessment value does not exceed the preset energy storage leakage assessment threshold, temperature data at several locations within the energy storage device are collected, and the average value of the temperature data at all locations is marked as the internal temperature value of the energy storage device. A rectangular coordinate system is established with time as the X-axis and the internal temperature value of the energy storage device as the Y-axis, and the change curve of the internal temperature value of the energy storage device is plotted in the first quadrant and marked as a gradient curve. The starting point of the gradient curve is located on the Y-axis.

[0015] In the first quadrant, draw a ray parallel to the X-axis with its endpoint on the Y-axis. If the end point of the gradient curve is above the ray, connect the end point of the gradient curve to the ray with a vertical line segment. Mark the area enclosed by the part of the gradient curve above the ray and the ray in red and define it as the temperature warning zone. Sum the areas of all temperature warning zones to obtain the temperature warning performance value.

[0016] Furthermore, the average value of the vibration data of the energy storage device per unit time is marked as the vibration analysis value, and the average value of the noise decibel value generated by the energy storage device per unit time is marked as the noise analysis value. The energy storage safety impact coefficient is obtained by numerically calculating the temperature alarm performance value, vibration analysis value and noise analysis value, and the energy storage safety impact coefficient is numerically compared with the preset energy storage safety impact coefficient threshold.

[0017] If the energy storage safety impact coefficient exceeds the preset energy storage safety impact coefficient threshold, a high energy storage safety impact signal is generated; if the energy storage safety impact coefficient does not exceed the preset energy storage safety impact coefficient threshold, a low energy storage safety impact signal is generated.

[0018] Furthermore, the energy storage safety impact analysis module is connected to the energy storage stability analysis module. The energy storage safety impact analysis module sends the energy storage safety high impact signal to the energy storage stability analysis module. The energy storage stability analysis module analyzes the energy storage stability status of the energy storage device during the detection period, generates a high-stability signal or a low-stability signal through analysis, and sends the high-stability signal or low-stability signal to the back-end management terminal. When the back-end management terminal receives the low-stability signal, it issues a corresponding warning.

[0019] Furthermore, the specific analysis process of the energy storage stability analysis module includes:

[0020] The number of times the energy storage device generates a high-impact energy storage safety signal during the detection period is obtained and marked as the energy storage hazard frequency value. The generation time of the corresponding high-impact energy storage safety signal is obtained and marked as the energy storage hazard meter time. The interval between two adjacent sets of energy storage hazard meter times is marked as the hazard interval value. The hazard interval value is compared with the preset hazard interval threshold. If the hazard interval value does not exceed the preset hazard interval threshold, the corresponding hazard interval value is marked as the hazard interval measurement value.

[0021] The system acquires the number of isolated fault values ​​during the detection period and marks them as isolated fault values. It also calculates the energy storage stability impact coefficient by weighting and summing the energy storage fault frequency value and isolated fault values. The energy storage stability impact coefficient is then compared with a preset energy storage stability impact coefficient threshold. If the energy storage stability impact coefficient exceeds the preset energy storage stability impact coefficient threshold, a low energy storage stability signal is generated. If the energy storage stability impact coefficient does not exceed the preset energy storage stability impact coefficient threshold, a high energy storage stability signal is generated.

[0022] Furthermore, the energy storage stability analysis module is connected to the energy storage management and analysis module. The energy storage stability analysis module sends the high-stability or low-stability energy storage signals of the corresponding energy storage devices to the energy storage management and analysis module. The energy storage management and analysis module obtains all the energy storage devices that need to be managed, analyzes the control difficulty of all energy storage devices, and determines whether to generate an energy storage management enhancement signal through analysis. The energy storage management enhancement signal is then sent to the back-end management terminal. When the back-end management terminal receives the energy storage management enhancement signal, it issues a corresponding warning.

[0023] Furthermore, the specific process for determining whether to generate an energy storage management enhancement signal through analysis is as follows:

[0024] If the corresponding energy storage device corresponds to a low energy storage stability signal, then the corresponding energy storage device is marked as an abnormal energy storage device; if the corresponding energy storage device corresponds to a high energy storage stability signal, then the energy storage tracking value of the corresponding energy storage device is obtained through energy storage device tracking analysis, and the energy storage tracking value is compared with a preset energy storage tracking threshold. If the energy storage tracking value exceeds the preset energy storage tracking threshold, then the corresponding energy storage device is marked as an abnormal energy storage device.

[0025] The number of abnormal energy storage devices is obtained and its ratio is calculated with the total number of energy storage devices that need to be monitored to obtain the abnormal energy storage detection value. The abnormal energy storage detection value is compared with the preset abnormal energy storage detection threshold. If the abnormal energy storage detection value exceeds the preset abnormal energy storage detection threshold, an enhanced energy storage management signal is generated.

[0026] Furthermore, the specific analysis process for tracking and analyzing energy storage devices is as follows:

[0027] The production date of the corresponding energy storage device is collected, and the interval between the current date and the production date is marked as the energy storage duration. The power storage performance degradation of the corresponding energy storage device is collected and marked as the storage performance degradation.

[0028] The total duration of the corresponding energy storage device being in an overcharged state and the total duration of the over-discharged state are collected, and the total duration of the overcharged state and the total duration of the over-discharged state are summed to calculate the charge and discharge anomaly value;

[0029] Real-time monitoring data of various environmental parameters of the environment in which the corresponding energy storage device is located are collected. If there are environmental parameters whose real-time monitoring data do not meet the corresponding preset data requirements, the corresponding energy storage device is judged to be in an external risk state. The total duration of the corresponding energy storage device in an external risk state in the historical period is obtained and marked as the external risk time value. The energy storage tracking value is obtained by numerically calculating the energy storage duration, storage degradation, charging and discharging anomaly value and the external risk time value.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. In this invention, the photovoltaic equipment, energy storage device and charging device are monitored by a comprehensive monitoring output module. The regulation strategy analysis and generation module processes and analyzes the monitoring data and generates the optimal energy storage and charging regulation strategy. The regulation strategy execution control module controls the operation of the energy storage device based on the regulation strategy, thereby enhancing the stability of the photovoltaic power generation system. Furthermore, the energy storage safety impact analysis module analyzes the energy storage safety hazards of the energy storage device. When a high energy storage safety impact signal is generated, the cause is investigated and analyzed, and reasonable improvement measures are taken, which is conducive to ensuring the safe and stable operation of the energy storage device.

[0032] 2. In this invention, the energy storage stability analysis module analyzes the energy storage stability of the energy storage device during the detection period. When a low energy storage stability signal is generated, the operation supervision and control of the corresponding energy storage device is strengthened, further ensuring the safe and stable operation of the energy storage device. Furthermore, the energy storage management analysis module analyzes the difficulty of managing all energy storage devices. When an enhanced energy storage management signal is generated, corresponding management measures are adjusted and improved, which is conducive to improving the subsequent management effect and ensuring the safe and stable operation of the energy storage device. The invention has a high level of intelligence and automation. Attached Figure Description

[0033] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0034] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0035] Figure 2 This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1: As Figure 1 As shown, the energy storage and charging control system for photovoltaic equipment proposed in this invention includes a comprehensive monitoring output module, a control strategy analysis and generation module, a control strategy execution and control module, an energy storage safety impact analysis module, and a back-end management terminal. The comprehensive monitoring output module monitors the operation of photovoltaic equipment, energy storage devices, and charging devices, collects data such as the power generation of photovoltaic equipment, the power status of energy storage devices, and the demand of power-consuming equipment in real time, and sends all monitoring data to the control strategy analysis and generation module.

[0038] It should be noted that photovoltaic equipment converts light energy into electrical energy and outputs it to energy storage devices or the power grid; energy storage devices are used to store the electrical energy generated by photovoltaic equipment and release the electrical energy to power charging devices or the power grid when needed, and the energy storage devices are lithium-ion battery packs, which have advantages such as high energy density, long cycle life, and no pollution; charging devices are used to convert the electrical energy in the energy storage devices into electrical energy suitable for electric vehicles or other electrical equipment and charge them, and the charging devices have multiple charging modes, including constant current charging, constant voltage charging, and pulse charging.

[0039] The regulation strategy analysis and generation module uses machine learning or deep learning algorithms to process and analyze the collected monitoring data, predict power generation and electricity demand in the future, automatically generate the optimal energy storage and charging regulation strategy, and send the generated regulation strategy to the regulation strategy execution control module and the back-end management terminal. The regulation strategy execution control module controls the operation of the energy storage device based on the regulation strategy, and the management personnel can remotely manually control the energy storage device through the back-end management terminal. The operation process of the regulation strategy analysis and generation module can be summarized as follows:

[0040] Data collection and preprocessing: Collect monitoring data, such as photovoltaic power generation data (including real-time power generation of photovoltaic panels, solar radiation intensity, temperature, etc.); electricity demand data (from smart meters, electricity management systems, etc., recording real-time electricity consumption and consumption patterns); energy storage device data (key parameters such as the energy capacity and charging / discharging status of energy storage devices); and external data (such as weather forecasts, holiday information, seasonal changes, etc.). This data is crucial for predicting future power generation and electricity demand.

[0041] The collected data undergoes preprocessing steps, including data cleaning (removing outliers and handling missing values), data standardization (converting data into a uniform format and standard), and data normalization (scaling data to the same range) to ensure data quality and consistency.

[0042] Feature extraction and selection: Feature extraction is performed on the preprocessed data, that is, identifying variables that have an important impact on predicting future power generation and electricity demand. These features include historical power generation, historical electricity demand, solar radiation intensity, temperature, etc. Feature selection is another key step, which aims to select the most predictive subset of features from the extracted features in order to reduce computational complexity and improve prediction accuracy.

[0043] Model training and validation: Machine learning or deep learning algorithms are used to build predictive models. Common machine learning algorithms include linear regression, support vector machines, random forests, etc., while deep learning algorithms may include neural networks, recurrent neural networks (RNNs), long short-term memory networks (LSTMs), etc.

[0044] Furthermore, during the model training phase, historical data is used to train the model, enabling it to learn the complex relationships between power generation, electricity demand, and feature variables. During training, the model parameters are continuously adjusted to minimize prediction errors. During the model validation phase, a portion of data not used in training is used to evaluate the model's performance. By comparing the model's prediction results with actual observations, the model's prediction accuracy and generalization ability can be assessed.

[0045] Predicting future power generation and electricity demand: Once the model is trained and validated, it uses real-time collected data and the prediction model to predict power generation and electricity demand in the future. The prediction results take into account a variety of factors, such as weather changes, holidays, and seasonal changes, to ensure the accuracy and reliability of the prediction.

[0046] Automatic generation of optimal energy storage and charging control strategies: Based on the predicted future power generation and electricity demand, optimization algorithms (such as dynamic programming, genetic algorithms, etc.) are used to automatically generate optimal energy storage and charging control strategies. These strategies fully consider factors such as the capacity of energy storage devices, charging / discharging efficiency, and grid demand to ensure that energy storage devices can provide sufficient power when needed, while avoiding overcharging or discharging.

[0047] Real-time adjustment and optimization: During actual operation, new monitoring data is continuously collected, and the prediction model and control strategy are adjusted and optimized in real time based on the real-time data. This includes updating the parameters of the prediction model, adjusting the feature selection strategy, and optimizing the energy storage and charging control strategy, so as to ensure that the system can adapt to changes in sunlight and fluctuations in electricity demand and maintain stable operation.

[0048] This invention enables efficient storage and intelligent distribution of electrical energy generated by photovoltaic equipment, reducing energy waste, improving energy utilization efficiency, and enhancing system stability. Furthermore, by predicting the power generation of photovoltaic equipment and the power consumption of electrical equipment in the future, it can adjust the charging and discharging strategies of the energy storage device in advance, achieving balanced and optimized utilization of electrical energy, enhancing the stability of the photovoltaic power generation system, and selecting different charging modes and power according to the type and needs of electrical equipment, thereby improving charging efficiency, shortening charging time, and exhibiting a high level of intelligence and automation.

[0049] The energy storage safety impact analysis module analyzes potential safety hazards in energy storage devices. Through analysis, it generates high-impact or low-impact safety signals. The high-impact signal is sent to the backend management terminal. Upon receiving the high-impact signal, the backend management terminal issues a corresponding warning to remind managers to promptly investigate and analyze the causes and take appropriate corrective measures to reduce the operational risks of the energy storage device, thereby ensuring its safe and stable operation. The specific analysis process of the energy storage safety impact analysis module is as follows:

[0050] Several detection periods are set within a unit of time, and all detection periods have the same duration; the actual power consumption value and actual power output value of the energy storage device are collected within the corresponding detection period; the difference between the actual power consumption value (i.e., the power consumed by the energy storage device) and the actual power output value (i.e., the power received by the power grid and electrical equipment from the corresponding energy storage device) is calculated and the difference result is marked as the energy storage loss value.

[0051] It should be noted that the larger the value of the energy storage loss value, the greater the possibility of leakage of the energy storage device during the corresponding detection period. The energy storage loss value is compared with the preset energy storage loss threshold. If the energy storage loss value exceeds the preset energy storage loss threshold, it indicates that there is a risk of leakage during the corresponding detection period. The corresponding detection period is then marked as an energy storage risk period.

[0052] The number of energy storage risk periods per unit time is obtained and the ratio of it to the number of detection periods is calculated to obtain the energy storage risk status value. The average value of energy storage anomaly loss values ​​of all detection periods per unit time is calculated to obtain the energy storage anomaly value. The energy storage anomaly loss value with the largest value per unit time is marked as the energy storage anomaly value.

[0053] The energy storage leakage assessment value XL is obtained by numerically calculating the energy storage risk situation value WY, the energy storage anomaly value ZQ, and the energy storage anomaly value SP using the formula XL=eq×WY+uy×ZQ+te×SP. Here, eq, uy, and te are preset proportional coefficients with values ​​greater than zero. Furthermore, the larger the value of the energy storage leakage assessment value XL, the more serious the leakage risk of the energy storage device.

[0054] The energy storage leakage assessment value XL is compared with the preset energy storage leakage assessment threshold. If the energy storage leakage assessment value XL exceeds the preset energy storage leakage assessment threshold, it indicates that the leakage risk of the energy storage device is serious and is not conducive to the safe operation of the energy storage power supply. In this case, a high impact signal on energy storage safety is generated.

[0055] Furthermore, if the energy storage leakage assessment value XL does not exceed the preset energy storage leakage assessment threshold, temperature data from several locations within the energy storage device are collected, and the average value of the temperature data from all locations is marked as the internal temperature value of the energy storage device. A rectangular coordinate system is established with time as the X-axis and the internal temperature value of the energy storage device as the Y-axis. The change curve of the internal temperature value of the energy storage device is plotted in the first quadrant and marked as a gradient curve. The starting point of the gradient curve is located on the Y-axis.

[0056] In the first quadrant, draw a ray parallel to the X-axis with its endpoint on the Y-axis. If the end point of the gradient curve is above the ray, connect the end point of the gradient curve to the ray with a vertical line segment. Mark the area enclosed by the part of the gradient curve above the ray and the ray in red and define it as the temperature warning zone. Sum the areas of all temperature warning zones to obtain the temperature warning performance value.

[0057] Furthermore, the average value of the vibration data (i.e., vibration amplitude) of the energy storage device per unit time is marked as the vibration analysis value, and the average value of the noise decibel value generated by the energy storage device per unit time is marked as the noise analysis value. The energy storage safety impact coefficient FM is obtained by numerically calculating the temperature alarm performance value RY, vibration analysis value KL, and noise analysis value WN using the formula FM=(a×RY+c×KL+n×WN) / 3. Among them, a, c, and n are preset weighting coefficients with values ​​greater than zero. Moreover, the larger the value of the energy storage safety impact coefficient FM, the higher the operating risk of the energy storage device.

[0058] The energy storage safety impact coefficient FM is compared with the preset energy storage safety impact coefficient threshold. If the energy storage safety impact coefficient FM exceeds the preset energy storage safety impact coefficient threshold, it indicates that the operation risk of the energy storage device is high, which is not conducive to the safe operation of the energy storage power supply, and a high energy storage safety impact signal is generated. If the energy storage safety impact coefficient FM does not exceed the preset energy storage safety impact coefficient threshold, it indicates that the operation risk of the energy storage device is low, and a low energy storage safety impact signal is generated.

[0059] Example 2: Figure 2 As shown, the difference between this embodiment and embodiment one is that the energy storage safety impact analysis module is communicatively connected to the energy storage stability analysis module. The energy storage safety impact analysis module sends the energy storage safety high impact signal to the energy storage stability analysis module. The energy storage stability analysis module analyzes the energy storage stability status of the energy storage device during the detection period (preferably, the detection period is twenty days) and generates an energy storage high stability signal or an energy storage low stability signal through analysis.

[0060] Furthermore, the system sends either a high-stability signal or a low-stability signal to the backend management terminal. Upon receiving a low-stability signal, the backend management terminal issues a corresponding warning to remind managers to continuously monitor the operation of the corresponding energy storage device, strengthen its operational supervision and control, and facilitate targeted enhanced management of the energy storage device to ensure its safe and stable operation. The specific analysis process of the energy storage stability analysis module is as follows:

[0061] The number of times the energy storage device generates a high-impact energy storage safety signal during the detection period is obtained and marked as the energy storage hazard frequency value. The generation time of the corresponding high-impact energy storage safety signal is obtained and marked as the energy storage hazard meter time. The interval between two adjacent sets of energy storage hazard meter times is marked as the hazard interval value. The hazard interval value is compared with the preset hazard interval threshold. If the hazard interval value does not exceed the preset hazard interval threshold, the corresponding hazard interval value is marked as the hazard interval measurement value.

[0062] The number of hazard isolation values ​​during the detection period is obtained and marked as hazard isolation values. The energy storage stability impact coefficient QW is calculated by weighting and summing the energy storage hazard frequency value HK and the hazard isolation value NS using the formula QW = bg × HK + us × NS. Here, bg and us are preset weight coefficients with values ​​greater than zero, and the value of us is greater than bg. Furthermore, the larger the value of the energy storage stability impact coefficient QW, the worse the operating stability of the corresponding energy storage device during the detection period.

[0063] The energy storage stability impact coefficient QW is compared with the preset energy storage stability impact coefficient threshold. If the energy storage stability impact coefficient QW exceeds the preset energy storage stability impact coefficient threshold, it indicates that the operation stability of the corresponding energy storage device is poor during the testing period and needs to be strengthened. In this case, a low energy storage stability signal is generated. If the energy storage stability impact coefficient QW does not exceed the preset energy storage stability impact coefficient threshold, it indicates that the operation stability of the corresponding energy storage device is good during the testing period. In this case, a high energy storage stability signal is generated.

[0064] Example 3: Figure 2 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the energy storage stability analysis module is communicatively connected to the energy storage management and analysis module. The energy storage stability analysis module sends the energy storage high stability signal or energy storage low stability signal of the corresponding energy storage device to the energy storage management and analysis module. The energy storage management and analysis module obtains all the energy storage devices that need to be managed, analyzes the control difficulty of all energy storage devices, and determines whether to generate an energy storage management enhancement signal through analysis.

[0065] Furthermore, the enhanced energy storage management signal is sent to the back-end management terminal. Upon receiving the enhanced energy storage management signal, the back-end management terminal issues a corresponding warning. When the management personnel receive the enhanced energy storage management signal, they make corresponding adjustments and improvements to the management measures, thereby strengthening the subsequent supervision of all energy storage devices in a timely manner. This is conducive to improving the subsequent management effect and ensuring the safe and stable operation of energy storage devices. The specific process of analyzing to determine whether to generate an enhanced energy storage management signal is as follows:

[0066] If the corresponding energy storage device corresponds to the low stability signal of energy storage, it indicates that the operation stability of the corresponding energy storage device is poor and the control is difficult. In this case, the corresponding energy storage device will be marked as an abnormal energy storage device.

[0067] If the corresponding energy storage device corresponds to the energy storage high stability signal, the energy storage tracking value GP of the corresponding energy storage device is obtained through energy storage device tracking analysis. Specifically, the production date of the corresponding energy storage device is collected, the interval between the current date and the production date is marked as the energy storage duration, and the power storage performance degradation of the corresponding energy storage device (i.e., the data value of the difference between the actual amount of electricity that can be stored and its theoretical maximum amount of electricity) is collected and marked as the storage performance degradation.

[0068] The total duration of the corresponding energy storage device being in an overcharged state and the total duration of the over-discharged state are collected, and the total duration of the overcharged state and the total duration of the over-discharged state are summed to calculate the charge and discharge anomaly value;

[0069] Real-time monitoring data of various environmental parameters (such as temperature and humidity) of the environment where the corresponding energy storage device is located are collected. If there are environmental parameters whose real-time monitoring data do not meet the corresponding preset data requirements, the corresponding energy storage device is judged to be in an external risk state. The total duration of the corresponding energy storage device in an external risk state in the historical period is obtained and marked as the external risk time value.

[0070] The energy storage tracking value GP is obtained by numerically calculating the energy storage duration WS, storage degradation FX, charge / discharge anomaly value QL, and external risk value HY using the formula GP=mu×WS+re×FX+hg×QL+kp×HY. Among them, mu, re, hg, and kp are preset weighting coefficients with values ​​greater than zero. Furthermore, the larger the value of the energy storage tracking value GP, the worse the current quality status of the corresponding energy storage device is, and the more difficult it is to ensure its safe and stable operation.

[0071] The energy storage tracking value GP is compared with the preset energy storage tracking threshold. If the energy storage tracking value GP exceeds the preset energy storage tracking threshold, it indicates that the current quality of the corresponding energy storage device is poor and it is difficult to ensure its continuous, safe and stable operation. In this case, the corresponding energy storage device is marked as an abnormal energy storage device.

[0072] The number of abnormal energy storage devices is obtained and its ratio is calculated to the total number of energy storage devices that need to be regulated to obtain the abnormal energy storage detection value. The abnormal energy storage detection value is compared with the preset abnormal energy storage detection threshold. If the abnormal energy storage detection value exceeds the preset abnormal energy storage detection threshold, it indicates that the control of all energy storage devices is more difficult and the control of energy storage devices needs to be strengthened. Then, an enhanced energy storage control signal is generated.

[0073] The working principle of this invention is as follows: During use, the comprehensive monitoring output module monitors the operation of photovoltaic equipment, energy storage devices, and charging devices. The regulation strategy analysis and generation module uses machine learning or deep learning algorithms to process and analyze the collected monitoring data, automatically generating the optimal energy storage and charging regulation strategy. The regulation strategy execution control module controls the operation of the energy storage device based on the regulation strategy, enabling efficient storage and intelligent distribution of the electrical energy generated by the photovoltaic equipment, improving energy utilization efficiency, achieving balanced and optimized utilization of electrical energy, and enhancing the stability of the photovoltaic power generation system. Furthermore, the energy storage safety impact analysis module analyzes the energy storage safety hazards of the energy storage device, conducts cause investigation and analysis when a high-impact signal for energy storage safety is generated, and makes reasonable improvement measures to reduce the operational risks of the energy storage device, which is conducive to ensuring the safe and stable operation of the energy storage device.

[0074] The above formulas are all dimensionless numerical calculations. These formulas are derived from software simulations using collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to actual conditions. The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. The preferred embodiments do not describe all details exhaustively, nor do they limit the invention to specific implementations. Obviously, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An energy storage charging control system adapted to photovoltaic equipment, characterized in that, It includes a comprehensive monitoring output module, a regulation strategy analysis and generation module, a regulation strategy execution and control module, an energy storage safety impact analysis module, and a back-end management terminal; the comprehensive monitoring output module monitors the operation of photovoltaic equipment, energy storage devices, and charging devices, and sends all monitoring data to the regulation strategy analysis and generation module; The regulation strategy analysis and generation module uses machine learning algorithms to process and analyze the collected monitoring data, predict the power generation and electricity demand in the future, automatically generate the optimal energy storage and charging regulation strategy, and send the generated regulation strategy to the regulation strategy execution control module and the back-end management terminal. The regulation strategy execution control module controls the operation of the energy storage device based on the regulation strategy. The energy storage safety impact analysis module analyzes the energy storage safety hazards of the energy storage device, generates a high-impact signal or a low-impact signal for energy storage safety through analysis, and sends the high-impact signal for energy storage safety to the back-end management terminal. The specific analysis process of the energy storage safety impact analysis module includes: Several detection periods are set within a unit of time. If the energy storage loss value exceeds the preset energy storage loss threshold, the corresponding detection period is marked as an energy storage risk period. The energy storage risk status value is obtained by calculating the ratio of the number of energy storage risk periods per unit time to the number of detection periods. The energy storage anomaly value is obtained by averaging the energy storage anomaly values ​​of all detection periods per unit time. The energy storage anomaly value with the largest value per unit time is marked as the energy storage anomaly value. The energy storage leakage assessment value is obtained by numerically calculating the energy storage risk status value, energy storage anomaly value, and energy storage anomaly value. If the energy storage leakage assessment value exceeds the preset energy storage leakage assessment threshold, a high impact signal for energy storage safety is generated. If the energy storage leakage assessment value does not exceed the preset energy storage leakage assessment threshold, then temperature data at several locations inside the energy storage device are collected, and the average value of the temperature data at all locations is marked as the energy storage internal temperature value. A rectangular coordinate system is established with time as the X-axis and the energy storage internal temperature value as the Y-axis, and the change curve of the energy storage internal temperature value is plotted in the first quadrant and marked as a gradient curve. The starting point of the gradient curve is located on the Y-axis. In the first quadrant, draw a ray parallel to the X-axis with its endpoint on the Y-axis. Mark the area enclosed by the ray and the part of the gradient curve above the ray in red and define it as the temperature warning zone. Sum the areas of all temperature warning zones to calculate the temperature warning performance value. The energy storage safety impact coefficient is obtained by numerically calculating the temperature alarm performance value, vibration analysis value, and noise analysis value. If the energy storage safety impact coefficient exceeds the preset energy storage safety impact coefficient threshold, a high energy storage safety impact signal is generated; if the energy storage safety impact coefficient does not exceed the preset energy storage safety impact coefficient threshold, a low energy storage safety impact signal is generated.

2. The energy storage charging control system adapted to photovoltaic equipment according to claim 1, characterized in that, Photovoltaic equipment converts light energy into electrical energy and outputs it to energy storage devices or the power grid. The energy storage device is used to store the electrical energy generated by the photovoltaic equipment and releases the electrical energy to the charging device or the power grid when needed. The energy storage device is a lithium-ion battery pack. The charging device is used to convert the electrical energy in the energy storage device into electrical energy suitable for electric vehicles or other electrical equipment and to charge it. The charging device has multiple charging modes, including constant current charging, constant voltage charging and pulse charging.

3. The energy storage charging control system adapted to photovoltaic equipment according to claim 1, characterized in that, The energy storage safety impact analysis module communicates with the energy storage stability analysis module. The energy storage safety impact analysis module sends the energy storage safety high impact signal to the energy storage stability analysis module. The energy storage stability analysis module analyzes the energy storage stability status of the energy storage device during the detection period, and generates an energy storage high stability signal or an energy storage low stability signal through analysis. The high stability signal or the low stability signal is then sent to the back-end management terminal. When the back-end management terminal receives the energy storage low stability signal, it issues a corresponding warning.

4. The energy storage charging control system adapted to photovoltaic equipment according to claim 3, characterized in that, The specific analysis process of the energy storage stability analysis module includes: The number of times the energy storage device generates a high-impact energy storage safety signal during the detection period is obtained and marked as the energy storage risk frequency value. The generation time of the corresponding high-impact energy storage safety signal is obtained and marked as the energy storage risk meter time. The interval between two adjacent sets of energy storage risk meter times is marked as the risk interval value. If the risk interval value does not exceed the preset risk interval threshold, the corresponding risk interval value is marked as the risk interval measurement value. The number of hazard isolation values ​​during the detection period is obtained and marked as hazard isolation values. The energy storage hazard frequency value and hazard isolation value are weighted and summed to obtain the energy storage stability influence coefficient. If the energy storage stability influence coefficient exceeds the preset energy storage stability influence coefficient threshold, a low energy storage stability signal is generated; if the energy storage stability influence coefficient does not exceed the preset energy storage stability influence coefficient threshold, a high energy storage stability signal is generated.

5. The energy storage charging control system adapted to photovoltaic equipment according to claim 4, characterized in that, The energy storage stability analysis module communicates with the energy storage management and analysis module. The energy storage management and analysis module obtains all the energy storage devices that need to be managed, analyzes the difficulty of managing all the energy storage devices, and determines whether to generate an energy storage management enhancement signal based on the analysis. The energy storage management enhancement signal is then sent to the backend management terminal.

6. The energy storage charging control system adapted to photovoltaic equipment according to claim 5, characterized in that, The specific process for analyzing and determining whether to generate an energy storage management enhancement signal is as follows: If the corresponding energy storage device corresponds to the low stability signal, the corresponding energy storage device is marked as an abnormal energy storage device; if the corresponding energy storage device corresponds to the high stability signal, the energy storage tracking value of the corresponding energy storage device is obtained through energy storage device tracking analysis; if the energy storage tracking value exceeds the preset energy storage tracking threshold, the corresponding energy storage device is marked as an abnormal energy storage device. The number of abnormal energy storage devices is obtained and its ratio is calculated with the total number of energy storage devices that need to be regulated to obtain the abnormal energy storage detection value. If the abnormal energy storage detection value exceeds the preset abnormal energy storage detection threshold, an enhanced energy storage management signal is generated.

7. The energy storage charging control system adapted to photovoltaic equipment according to claim 6, characterized in that, The specific analysis process for tracking and analyzing energy storage devices is as follows: The production date of the corresponding energy storage device is collected, and the interval between the current date and the production date is marked as the energy storage duration. The power storage performance degradation of the corresponding energy storage device is collected and marked as the storage performance degradation. The system collects the total duration of the corresponding energy storage device in an overcharged state and the total duration of the over-discharged state, and sums the total duration of the overcharged state and the total duration of the over-discharged state to obtain the charge and discharge anomaly value; it also obtains the total duration of the corresponding energy storage device in an external risk state in historical periods and marks it as the external risk time value, and obtains the energy storage tracking value by numerically calculating the energy storage duration, storage degradation, charge and discharge anomaly value and external risk time value.

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