Intelligent management method and system of photovoltaic energy storage equipment

Abstract

The invention provides an intelligent management method and system for photovoltaic energy storage equipment, and the method comprises the steps: analyzing the power fluctuation trend through monitoring the output power and environment parameters of the photovoltaic energy storage equipment in real time, and pre-judging the power fluctuation characteristics; according to the operation state parameters of the supercapacitor and the vanadium redox flow battery and the power fluctuation pre-judgment result, the power distribution proportion and the cooperative working mode of the energy storage element are determined; based on industrial load demand characteristics, an energy storage element is controlled to be flexibly switched between an active adjustment mode and a reactive compensation mode; and voltage regulation and harmonic compensation are realized by monitoring electric energy quality indexes of parallel connection points. According to the invention, the rapid response capability of the photovoltaic energy storage system to the industrial load can be effectively improved, the power supply stability and the electric energy quality are remarkably improved, the system power fluctuation is reduced, the utilization efficiency of the energy storage equipment is improved, and reliable clean energy support is provided for industrial users.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic management, and in particular to an intelligent management method and system for photovoltaic energy storage equipment. Background Art

[0002] With the rapid development of photovoltaic power generation technology and the continuous expansion of its application, photovoltaic energy storage systems are becoming increasingly widely used in the industrial sector. The industrial power environment is characterized by high load power, complex power consumption characteristics, and strict requirements for power quality. Traditional photovoltaic energy storage management methods have exposed significant technical limitations when addressing these complex requirements. Existing photovoltaic energy storage management technologies mainly use a single power leveling strategy, lacking in-depth consideration of the differences in the characteristics of energy storage devices, and are unable to achieve coordinated and optimized scheduling of different types of energy storage devices such as supercapacitors and vanadium liquid flow batteries. This single management model leads to inefficient energy storage resource utilization and fails to fully utilize the unique advantages of various energy storage technologies.

[0003] Existing technologies typically employ a unified control strategy to manage all energy storage devices, ignoring the differences in the characteristics of supercapacitors, which have fast response times but limited capacity, and vanadium flow batteries, which have large capacity but relatively slow response times. This management approach prevents the energy storage system from achieving optimal configuration when faced with varying power demands, leading to problems such as insufficient overall system regulation accuracy, slow response times, and low energy utilization efficiency. Especially in industrial environments, where multiple demands, such as rapid power fluctuations, long-term power regulation, and reactive power compensation, exist, the existing single management strategy struggles to meet the complex requirements of practical applications, severely restricting the widespread application of photovoltaic energy storage technology in the industrial sector. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem of poor coordination of multiple energy storage devices in existing photovoltaic energy storage management technology; A first aspect of the present invention provides an intelligent management method for photovoltaic energy storage equipment, the intelligent management method for photovoltaic energy storage equipment comprising: Continuously monitor the real-time output power and environmental parameters of photovoltaic energy storage equipment, and analyze the power change trend and power fluctuation parameters of photovoltaic energy storage equipment based on the monitoring data to obtain power fluctuation prediction results; Real-time monitoring of operating status parameters of supercapacitors and vanadium flow batteries in the photovoltaic energy storage device, and obtaining power allocation instructions for the photovoltaic energy storage device by determining the power allocation ratio and collaborative working mode of the supercapacitors and vanadium flow batteries based on the operating status parameters and power fluctuation prediction results; Real-time monitoring of industrial load demand of industrial loads, and controlling the supercapacitor and vanadium flow battery to switch between active power regulation and reactive power compensation modes according to the power allocation instructions and industrial load demand, thereby obtaining a power regulation solution that matches the photovoltaic energy storage device with the industrial load; The power quality index of the parallel point is monitored in real time, and the inverter of the photovoltaic energy storage device is controlled to perform voltage regulation and harmonic compensation according to the power regulation scheme and the power quality index to achieve power quality control.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the power fluctuation parameters include a fluctuation type and a fluctuation duration; and the analyzing the power change trend and power fluctuation parameters of the photovoltaic energy storage device based on the monitoring data to obtain a power fluctuation prediction result includes: The real-time output power in the monitoring data is sampled using a time sliding window technique, and the power change rate is calculated based on the sampling results. When the power change rate exceeds the preset threshold, the power change trend in the short, medium and long time windows is analyzed, and the comprehensive fluctuation index is calculated through weighted fusion; The cloud occlusion pattern is identified based on the numerical range of the comprehensive fluctuation index, the gradient of the ambient light intensity change in the environmental parameters, and the cloud movement speed parameter, and three types of fluctuations are distinguished: short-term occlusion, medium-term occlusion, and long-term occlusion. Based on the comprehensive fluctuation index, fluctuation type and cloud movement speed parameters, the fluctuation duration is predicted to obtain a power fluctuation prediction result including the fluctuation type and fluctuation duration.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the operating state parameters include the state of charge and internal resistance of the supercapacitor and the electrolyte flow rate and temperature of the vanadium redox flow battery; The power allocation ratio of the supercapacitor and the vanadium flow battery is determined according to the operating state parameters and the power fluctuation prediction result, and the power allocation instruction of the corresponding photovoltaic energy storage device is generated, which includes: Calculate the available regulation capacity of supercapacitors and vanadium flow batteries based on the state of charge and internal resistance of the supercapacitor and the electrolyte flow and temperature of the vanadium flow battery; When the fluctuation type in the power fluctuation prediction result is short-term obstruction and the available regulation capacity of the supercapacitor is within a preset reasonable range, the supercapacitor is allocated to undertake the preset main regulation task to obtain the corresponding power allocation ratio; When the fluctuation type in the power fluctuation prediction result is medium-duration or long-duration obstruction, a dynamic weight allocation algorithm is used to determine the power allocation ratio based on the fluctuation duration and the available regulation capacity of the supercapacitor and vanadium redox flow battery; According to the power allocation ratio, a power allocation instruction for the corresponding photovoltaic energy storage device is generated.

[0007] Optionally, in a third implementation of the first aspect of the present invention, calculating the available adjustable capacity of the supercapacitor and the vanadium flow battery based on the state of charge and internal resistance of the supercapacitor and the electrolyte flow rate and temperature of the vanadium flow battery includes: Evaluate the state of charge of the supercapacitor, calculate the difference between the current state of charge and the maximum state of charge and the minimum state of charge, and obtain the remaining charge of the supercapacitor; Evaluate the health status of the supercapacitor based on its internal resistance, perform health correction on the remaining charge, and calculate the adjustable capacity of the supercapacitor in its current state. Monitor and analyze the electrolyte flow rate of the vanadium flow battery, calculate the electrolyte circulation efficiency based on the flow parameters, and obtain the electrolyte flow state coefficient; A temperature correction coefficient is calculated according to the temperature parameters of the vanadium flow battery, the electrolyte flow state coefficient is temperature compensated, and the adjustable capacity of the vanadium flow battery is calculated based on the compensated electrolyte flow state coefficient.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, controlling the supercapacitor and the vanadium flow battery to switch between active power regulation and reactive power compensation modes according to the power allocation instruction and the industrial load demand to obtain a power regulation scheme that matches the photovoltaic energy storage device with the industrial load includes: Analyze the time-varying characteristics of active power and reactive power in industrial load demand, and identify the power signatures of different types of industrial equipment based on the time-varying characteristics; Determining the load type of the industrial load demand based on the identified power characteristic signature, wherein the load type includes an inductive load and a high-power starting load; When an inductive load is detected, the supercapacitor and vanadium flow battery are controlled to switch to reactive power compensation mode according to the power distribution instruction and output capacitive reactive power through the inverter; When a high-power starting load is detected, the supercapacitor and vanadium flow battery are controlled to switch to active power regulation mode according to the power allocation instruction and the charge state of the photovoltaic energy storage device is pre-adjusted to the preset optimal regulation range; Based on the periodic change pattern of industrial load demand and power allocation instructions, a power regulation plan is generated to match photovoltaic energy storage equipment with industrial load.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, when an inductive load is detected, controlling the supercapacitor and the vanadium flow battery to switch to a reactive compensation mode according to a power allocation instruction and outputting capacitive reactive power through an inverter includes: According to the reactive power allocation ratio in the power allocation instruction, the reactive power compensation tasks of the supercapacitor and the vanadium redox flow battery are allocated to obtain their respective reactive power output target values; Switching the supercapacitor inverter from active power regulation mode to reactive power compensation mode, adjusting the inverter's power factor control parameters, and obtaining a reactive power compensation control instruction for the supercapacitor; The inverter of the vanadium redox flow battery adjusts the inverter output phase angle according to the reactive power demand characteristics of the inductive load to obtain the reactive power compensation control instruction of the vanadium redox flow battery; According to the reactive power compensation control instructions of supercapacitors and vanadium flow batteries, the output timing and amplitude of the two sets of inverters are coordinated and controlled, and the coordinated capacitive reactive power output control strategy is obtained and executed to inject capacitive reactive power into the grid through the inverter.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, controlling the inverter of the photovoltaic energy storage device to perform voltage regulation and harmonic compensation according to the power regulation scheme and the power quality indicator to achieve power quality control includes: When the voltage deviation in the power quality indicator exceeds a preset range, the vanadium liquid flow battery is controlled to adjust the power factor through the inverter to achieve voltage regulation according to the reactive power allocation strategy in the power regulation scheme; When the total harmonic distortion rate in the power quality index exceeds the standard, the power quality multi-parameter linkage compensation mechanism is used to analyze the harmonic frequency characteristics, and according to the power regulation scheme, the inverter is controlled to generate harmonic currents with opposite phases for active filtering; When the three-phase voltage imbalance in the power quality index exceeds the standard, the reactive power output of each phase is controlled according to the power regulation scheme to achieve negative sequence voltage component compensation, thereby obtaining a power quality control effect in which the voltage deviation is controlled within a preset range and the total harmonic distortion rate is reduced.

[0011] A second aspect of the present invention provides an intelligent management system for photovoltaic energy storage equipment, the intelligent management system for photovoltaic energy storage equipment comprising: The power prediction module is used to continuously monitor the real-time output power and environmental parameters of the photovoltaic energy storage equipment, and analyze the power change trend and power fluctuation parameters of the photovoltaic energy storage equipment based on the monitoring data to obtain the power fluctuation prediction results; An allocation and scheduling module is used to monitor the operating status parameters of the supercapacitors and vanadium flow batteries in the photovoltaic energy storage device in real time, and obtain power allocation instructions for the photovoltaic energy storage device based on the power allocation ratio and collaborative working mode of the supercapacitors and vanadium flow batteries determined according to the operating status parameters and power fluctuation prediction results; A mode switching module is used to monitor the industrial load demand of the industrial load in real time, and control the supercapacitor and the vanadium flow battery to switch between active power regulation and reactive power compensation modes according to the power allocation instruction and the industrial load demand, so as to obtain a power regulation solution that matches the photovoltaic energy storage device with the industrial load; The quality control module is used to monitor the power quality indicators of the parallel points in real time, and control the inverter of the photovoltaic energy storage equipment to perform voltage regulation and harmonic compensation according to the power regulation scheme and power quality indicators to achieve power quality control.

[0012] The above-mentioned intelligent management method and system for photovoltaic energy storage equipment monitors the output power and environmental parameters of photovoltaic energy storage equipment in real time, analyzes power fluctuation trends, and predicts power fluctuation characteristics; determines the power distribution ratio and collaborative working mode of the energy storage elements based on the operating status parameters of the supercapacitor and vanadium liquid flow battery and the power fluctuation prediction results; controls the energy storage elements to flexibly switch between active regulation and reactive compensation modes based on the characteristics of industrial load demand; and realizes voltage regulation and harmonic compensation by monitoring the power quality indicators of the parallel points. The present invention can effectively enhance the rapid response capability of the photovoltaic energy storage system to industrial loads, significantly improve power supply stability and power quality, reduce system power fluctuations, improve the utilization efficiency of energy storage equipment, and provide reliable clean energy support for industrial users.

[0013] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0014] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of a first embodiment of an intelligent management method for photovoltaic energy storage equipment according to an embodiment of the present invention; Figure 2 Schematic diagram of an embodiment of an intelligent management system for photovoltaic energy storage equipment in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] The terms "including," "having," and any variations thereof, as used in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or device.

[0018] To facilitate understanding of this embodiment, a method for intelligent management of photovoltaic energy storage equipment disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, this method includes the following steps: 101. Continuously monitor the real-time output power and environmental parameters of the photovoltaic energy storage equipment, and analyze the power change trend and power fluctuation parameters of the photovoltaic energy storage equipment based on the monitoring data to obtain power fluctuation prediction results; In one embodiment of the present invention, the power fluctuation parameters include fluctuation type and fluctuation duration; the power change trend and power fluctuation parameters of the photovoltaic energy storage equipment are analyzed based on the monitoring data to obtain the power fluctuation prediction result, which includes: sampling the real-time output power in the monitoring data through the time sliding window technology, and calculating the power change rate based on the sampling results; when the power change rate exceeds the preset threshold, analyzing the power change trend in the three time windows of short time, medium time and long time, and calculating the comprehensive fluctuation index through weighted fusion; identifying the cloud occlusion mode according to the numerical range of the comprehensive fluctuation index, the ambient light intensity change gradient in the environmental parameters and the cloud movement speed parameter, and distinguishing the three fluctuation types of short time occlusion, medium time occlusion and long time occlusion; based on the comprehensive fluctuation index, fluctuation type and cloud movement speed parameter, predicting the fluctuation duration to obtain the power fluctuation prediction result including the fluctuation type and fluctuation duration.

[0019] Specifically, in the intelligent management of photovoltaic energy storage systems, accurately predicting power fluctuations is a key component in ensuring stable system operation. The power fluctuation prediction system first uses a sliding window technique to continuously sample the real-time output power in the monitoring data. This sliding window mechanism is essentially a fixed-length data buffer that continuously updates the power values ​​over time, ensuring that the system receives the latest power change information in real time. The sliding window operates similarly to a moving observation lens. The window size is typically set to dozens to hundreds of data points, corresponding to a time span of several seconds to minutes. When new power data arrives, the oldest data in the window is removed and the new data is added to the front of the window, maintaining a dynamically updated power data sequence. The advantage of this mechanism is that it both retains historical trends and responds promptly to the latest changes, avoiding response delays caused by excessive data caching. Based on these sampling results, the system calculates the power change rate by calculating the ratio of the difference between the power values ​​at adjacent time points to the time interval. This value directly reflects the speed and intensity of the change in photovoltaic output power. The calculation of the power change rate not only considers instantaneous changes, but also eliminates the influence of measurement noise and random fluctuations by smoothing the change rates at multiple time points, thereby obtaining a more stable and reliable change rate indicator.

[0020] Specifically, when the calculated power change rate exceeds a preset threshold, the system determines that significant power fluctuations may be occurring, necessitating a more detailed fluctuation analysis. This threshold is typically determined based on the installed capacity and historical operating data of the PV system, typically ranging from a few percent to more than ten percent of the rated power. The system simultaneously analyzes power variation trends within three different time windows: short, medium, and long. This multi-timescale analysis approach captures power variation characteristics across different time dimensions. The short-term window typically covers a range of 1 to 30 seconds and is primarily used to detect transient obstructions caused by small, rapidly moving clouds, which are characterized by rapid changes but limited impact. The medium-term window, spanning 30 seconds to 5 minutes, is used to identify the impact of medium-sized clouds, which move at a moderate speed and exhibit a gradual increase or decrease in PV output. The long-term window, extending from 5 minutes to 30 minutes or even longer, is used to analyze the impact of large-scale weather changes on PV output, such as the passage of large cloud systems or overall changes in weather systems.

[0021] Specifically, for each time window, the system calculates multiple statistical indicators to describe power trends, including the average rate of change, amplitude of change, and consistency of direction of change. The average rate of change reflects the overall speed of power change within that time period, the amplitude of change indicates the range of power fluctuations, and the consistency of direction of change is used to determine whether power is continuously rising, falling, or exhibiting oscillatory characteristics. By statistically analyzing the power data within these three time windows, the system calculates individual trend indicators. These indicators are then combined using a weighted fusion algorithm to form a unified comprehensive fluctuation index. This weighted fusion process dynamically adjusts the weights of different time windows based on their importance to current fluctuation predictions. The weighting algorithm takes into account the characteristics of current power fluctuations and historical experience. Short-term windows generally receive higher weight when detecting sudden changes because they can respond most quickly to sudden power fluctuations. Longer-term windows, on the other hand, are more important when assessing overall trends, particularly when determining the persistence and direction of fluctuations. The calculation formula for the comprehensive fluctuation index also incorporates a time decay factor, which increases the influence of recent data on the index while gradually diminishes the influence of more distant historical data, ensuring that the index reflects the latest changes.

[0022] Specifically, after obtaining the comprehensive fluctuation index, the system further combines key information from environmental parameters to identify specific cloud obstruction patterns. The gradient of ambient light intensity variation is a key factor in determining the distribution of solar radiation intensity. This gradient is calculated by analyzing spatial and temporal data from the illumination sensor array. This gradient reflects the degree of spatial unevenness in the distribution of solar radiation intensity. A large gradient typically indicates a significant cloud boundary effect, meaning that different parts of the photovoltaic array are obscured to varying degrees. The cloud movement speed parameter provides dynamic information on cloud motion. This parameter is derived by analyzing light intensity variations at multiple monitoring points over a continuous time period. Corrected with wind speed and direction data, it provides a more accurate estimate of cloud movement speed. The system correlates the range of the comprehensive fluctuation index with these environmental parameters and uses pre-set judgment logic to distinguish different types of cloud obstruction patterns. This judgment logic is based on statistical analysis of extensive historical data, forming a comprehensive set of classification rules. Short-term obstruction usually corresponds to the rapid passage of small clouds, which is characterized by a high comprehensive fluctuation index but a very short change time. At the same time, the cloud movement speed is relatively fast, and the light intensity gradient shows obvious boundary characteristics; medium-term obstruction is mostly caused by medium-sized clouds, with a medium comprehensive fluctuation index and relatively gentle changes, a moderate cloud movement speed, and a gradually changing light intensity gradient; long-term obstruction is often the result of a large-scale cloud system, with a comprehensive fluctuation index that may not be the highest but the duration is significantly extended, the cloud movement speed is usually slow, and the light intensity gradient changes slowly and over a large range.

[0023] Specifically, after identifying the fluctuation type, the system needs to further predict the duration of the fluctuation, which is an important basis for formulating appropriate energy storage scheduling strategies. The prediction process comprehensively considers the previously calculated comprehensive fluctuation index, the identified fluctuation type, and cloud movement speed parameters. The prediction algorithm uses a method based on a combination of physical models and statistical learning. The physical model considers the geometric characteristics and movement patterns of clouds, while the statistical learning component is based on a prediction model trained with historical data. For short-term obstruction, the system estimates the time required for a cloud to completely pass through the array based on cloud velocity and the geometric dimensions of the PV array. This calculation takes into account the irregularities of cloud shape and the variability of its trajectory, using a geometric projection algorithm to estimate the temporal variation of the obstructed area. For medium-term obstruction, in addition to considering velocity, the system also uses a comprehensive fluctuation index to assess cloud size and density. The algorithm analyzes the fluctuation pattern of the fluctuation index to infer the internal structure of the cloud, thereby more accurately predicting the duration of the obstruction. For long-term obstruction, the prediction algorithm references historical weather patterns and current atmospheric conditions, combining the magnitude of the fluctuation in the comprehensive fluctuation index to estimate the duration of the entire weather system. This prediction not only considers the current cloud state but also analyzes the evolution and seasonality of the weather system. Through this comprehensive analysis, the system ultimately generates a power fluctuation prediction result, including the specific fluctuation type and expected duration. This result is stored in a data structure containing key information such as the fluctuation type identifier, expected start time, expected end time, and impact intensity. This provides accurate prediction information for subsequent energy storage device scheduling and power management decisions, ensuring that the PV energy storage system can respond promptly to power changes and maintain stable operation of the entire system.

[0024] 102. Real-time monitoring of the operating status parameters of the supercapacitors and vanadium flow batteries in the photovoltaic energy storage device, and obtaining power allocation instructions for the photovoltaic energy storage device based on the power allocation ratio and collaborative working mode of the supercapacitors and vanadium flow batteries determined based on the operating status parameters and power fluctuation prediction results; In one embodiment of the present invention, the operating status parameters include the state of charge and internal resistance of the supercapacitor, and the electrolyte flow rate and temperature of the vanadium flow battery; determining the power allocation ratio of the supercapacitor and the vanadium flow battery based on the operating status parameters and the power fluctuation prediction result, and generating the corresponding power allocation instruction for the photovoltaic energy storage device includes: calculating the available regulation capacity of the supercapacitor and the vanadium flow battery based on the state of charge and internal resistance of the supercapacitor and the electrolyte flow rate and temperature of the vanadium flow battery; when the fluctuation type in the power fluctuation prediction result is short-term obstruction and the available regulation capacity of the supercapacitor is within a preset reasonable range, allocating the supercapacitor to undertake a preset main regulation task to obtain the corresponding power allocation ratio; when the fluctuation type in the power fluctuation prediction result is medium-term obstruction or long-term obstruction, determining the power allocation ratio using a dynamic weight allocation algorithm based on the fluctuation duration and the available regulation capacity of the supercapacitor and the vanadium flow battery; and generating the corresponding power allocation instruction for the photovoltaic energy storage device based on the power allocation ratio.

[0025] Specifically, in the intelligent management of photovoltaic energy storage systems, the decision-making process for energy storage device power allocation requires comprehensive consideration of the device's real-time operating status and predicted power fluctuations. The system first comprehensively collects and analyzes the energy storage device's operating status parameters; these parameters form the foundational data for subsequent allocation decisions. For supercapacitors, the system continuously monitors their state of charge and internal resistance. The state of charge reflects the percentage of the supercapacitor's current stored charge relative to its maximum capacity, directly affecting the amount of energy it can output or absorb. The internal resistance parameter characterizes the supercapacitor's health and charge / discharge efficiency. Increased internal resistance typically indicates device aging or performance degradation, affecting its rapid response capability. Simultaneously, the system monitors the electrolyte flow rate and temperature parameters of vanadium flow batteries. Electrolyte flow rate determines the electrochemical reaction rate and energy conversion efficiency within the battery; higher flow rates generally mean higher power output. Temperature affects the electrolyte's ionic conductivity and electrochemical reaction activity; excessively high or low temperatures can reduce battery performance. By monitoring these operating status parameters in real time, the system builds a comprehensive picture of the energy storage device's current operating status.

[0026] Specifically, based on the collected operating status parameters, the system calculates the available regulating capacity of supercapacitors and vanadium flow batteries. For supercapacitors, the calculation of available regulating capacity requires a comprehensive consideration of their current state of charge and internal resistance characteristics. The calculation process first analyzes the relationship between the state of charge and its theoretical maximum and minimum capacities, determining the maximum amount of charge the supercapacitor can discharge and the maximum charge it can accept under the current conditions. The system then adjusts this theoretical capacity based on the internal resistance. A higher internal resistance means greater energy loss during charge and discharge, reducing the actual available regulating capacity. The system also considers the supercapacitor's charge and discharge rate limits to ensure that the allocated power does not exceed the device's safe operating range. For vanadium flow batteries, the calculation of available regulating capacity is more complex. The system first assesses the battery's instantaneous power capability based on electrolyte flow parameters. The flow rate directly affects the maximum charge and discharge current the battery can support, thereby determining its power output range. The system then applies a temperature correction to this power range based on temperature parameters. Departures from the optimal operating temperature cause battery efficiency to decrease, requiring a corresponding adjustment to the estimated available capacity. The system also considers the current state of charge of the vanadium flow battery. Although vanadium flow batteries have deep discharge capabilities, extreme states of charge can still affect their regulation performance. Through this comprehensive calculation, the system determines the actual available regulation capacity of the two energy storage devices in their current state.

[0027] Specifically, after obtaining available regulation capacity data, the system formulates a corresponding power allocation strategy based on the fluctuation type in the power fluctuation prediction results. If the power fluctuation prediction results indicate a short-term obstruction, the system first checks whether the supercapacitor's available regulation capacity is within a preset acceptable range. This acceptable range is typically set between 20% and 80% of the supercapacitor's rated capacity to ensure sufficient regulation margin without overcharging or over-discharging the equipment. If the supercapacitor's available regulation capacity meets the requirements, the system assigns it primary regulation responsibility. This is because short-term obstructions are characterized by sharp but short-lived power fluctuations, and supercapacitors, with their fast response and high charge and discharge efficiency, are best suited to handling such rapid fluctuations. In this case, the power allocation ratio is typically set so that the supercapacitor handles 80% to 95% of the regulation load, with the vanadium flow battery handling the remainder. This fully leverages the supercapacitor's advantages while maintaining system redundancy. If the power fluctuation prediction results indicate a medium-term or long-term obstruction, the system uses a more complex dynamic weight allocation algorithm to determine the power allocation ratio. This algorithm comprehensively considers the duration of fluctuations and the available regulation capacity of the two energy storage devices. Because medium and long-term obstructions last for a long time, relying solely on supercapacitors can easily lead to their capacity exhaustion. The high capacity characteristics of vanadium liquid flow batteries make them more suitable for handling long-term power regulation needs.

[0028] Specifically, the dynamic weighted allocation algorithm works based on the degree of match between the characteristics of the energy storage devices and the fluctuation characteristics. The algorithm first analyzes the predicted fluctuation duration and compares it with the maximum time the supercapacitor can independently support with the current available regulation capacity. If the fluctuation duration exceeds this critical value, the system gradually increases the allocation ratio of the vanadium flow battery while reducing the burden on the supercapacitor to ensure sufficient regulation capacity throughout the fluctuation. The algorithm also considers the absolute value and relative ratio of the available regulation capacity of the two devices. If the available capacity of a device is significantly insufficient, the system adjusts the allocation strategy accordingly to avoid overloading the regulation task on the device with insufficient capacity. Furthermore, the algorithm has a built-in device protection mechanism. When the status parameters of a certain energy storage device are detected approaching the safety limit, the system automatically adjusts the allocation ratio, shifting more regulation tasks to the device in good condition. Through this dynamic adjustment, the system can find the optimal power allocation solution under different fluctuation conditions, ensuring both regulation effectiveness and safe device operation. Ultimately, the system generates the corresponding power allocation instructions for the photovoltaic energy storage equipment based on the calculated power allocation ratio. This instruction includes detailed information such as the specific power setting values, operating mode selection, response timing arrangement, etc. for the supercapacitors and vanadium liquid flow batteries. The instruction also includes a plan for handling abnormal situations to ensure that the system can adjust its strategy in time when encountering emergencies during execution to maintain the stability and reliability of the entire system.

[0029] Furthermore, the calculation of the available adjustable capacity of the supercapacitor and the vanadium flow battery based on the state of charge, internal resistance of the supercapacitor and the electrolyte flow and temperature of the vanadium flow battery includes: evaluating the state of charge of the supercapacitor, calculating the difference between the current state of charge and the maximum state of charge and the minimum state of charge, and obtaining the charge margin of the supercapacitor; evaluating the health state of the supercapacitor based on the internal resistance of the supercapacitor, performing health correction on the charge margin, and calculating the adjustable capacity of the supercapacitor in the current state; monitoring and analyzing the electrolyte flow of the vanadium flow battery, calculating the electrolyte circulation efficiency based on the flow parameters, and obtaining the electrolyte flow state coefficient; calculating the temperature correction coefficient based on the temperature parameters of the vanadium flow battery, performing temperature compensation on the electrolyte flow state coefficient, and calculating the adjustable capacity of the vanadium flow battery based on the compensated electrolyte flow state coefficient.

[0030] Specifically, in the operation and management of photovoltaic energy storage systems, accurately calculating the available regulating capacity of the energy storage device requires a series of detailed data processing steps. The system first obtains the current terminal voltage value from the supercapacitor's voltage monitoring module. This voltage value is then entered into a pre-stored voltage-State of Charge comparison table for a match. This comparison table, based on the supercapacitor's factory test data, records the State of Charge percentages corresponding to different voltage levels. Using linear interpolation, the system accurately determines the current State of Charge value. The system then reads the preset maximum and minimum State of Charge thresholds from the configuration file, typically set at 95% and 5%, respectively. The system performs a subtraction operation: subtracting the current State of Charge from the maximum State of Charge threshold to obtain the charge margin, and subtracting the minimum State of Charge threshold from the current State of Charge to obtain the discharge margin. These two calculation results, stored in kilowatt-hours, represent the maximum charge capacity and maximum discharge capacity that the supercapacitor can still accept, respectively.

[0031] Specifically, the system starts the internal resistance measurement program, injects a small AC current of a preset frequency into the supercapacitor, and simultaneously measures the resulting voltage response. The internal resistance measurement module calculates the ratio of the voltage amplitude to the current amplitude to obtain the current internal resistance value. The system retrieves the standard internal resistance value of the supercapacitor when it leaves the factory from the device database, and then performs a division operation, dividing the standard internal resistance value by the current internal resistance value to obtain the health correction coefficient. The value of this coefficient is usually between 0.5 and 1.0, and the closer the value is to 1, the better the health of the device. The system then multiplies the previously calculated charging charge margin and discharge charge margin by this health correction coefficient, performs two multiplication operations, and obtains the corrected charging adjustable capacity and discharge adjustable capacity. The system takes the smaller of the two values ​​as the final adjustable capacity of the supercapacitor, which ensures that the device has sufficient capacity margin in any adjustment direction.

[0032] Specifically, to calculate the capacity of a vanadium flow battery, the system reads the current electrolyte flow rate from the flow sensor, expressed in liters per minute. The system then reads the standard operating flow rate from the vanadium flow battery's technical specifications. This standard flow rate is typically determined based on the battery's rated power and optimal reaction conditions. The system then performs a division operation, dividing the current flow rate by the standard flow rate to obtain a flow ratio. The system then applies a preset flow-efficiency conversion function, which converts the flow ratio into an electrolyte circulation efficiency coefficient. This conversion function, based on experimental data, describes how battery performance varies under different flow conditions. When the flow ratio is between 0.8 and 1.2, the circulation efficiency coefficient approaches 1.0, indicating that the battery is operating optimally. When the flow ratio deviates from this range, the circulation efficiency coefficient decreases accordingly. The system stores the calculated result as the electrolyte flow state coefficient, which reflects the degree to which the current flow conditions affect the actual battery performance.

[0033] Specifically, the system simultaneously obtains temperature readings from a temperature sensor array at various locations within the vanadium flow battery, including the stack inlet, outlet, and electrolyte storage tank. The system calculates the average of these temperature readings to obtain the average operating temperature of the electrolyte. The system then reads the optimal operating temperature of the vanadium flow battery from the configuration parameters, typically set at 25 degrees Celsius. The system calculates the absolute value of the difference between the current average temperature and the optimal operating temperature to obtain a temperature deviation. Based on a preset temperature correction algorithm, the system inputs the temperature deviation value into a piecewise linear function for processing. This function specifies that when the temperature deviation is within 5 degrees Celsius, the temperature correction factor is 1.0; when the temperature deviation is between 5 and 15 degrees Celsius, the correction factor decreases linearly to 0.8; and when the temperature deviation exceeds 15 degrees Celsius, the correction factor is further reduced to below 0.6. The system performs a table lookup and interpolation operation to obtain the accurate temperature correction factor value. The system then multiplies the previously calculated electrolyte flow state coefficient by the temperature correction coefficient to obtain the temperature-compensated comprehensive state coefficient.

[0034] Specifically, the system calculates the actual adjustable capacity of the vanadium flow battery based on the compensated comprehensive state coefficient. The system reads the rated capacity of the vanadium flow battery from the device parameter library and then obtains the current state of charge percentage from the battery management system. The system performs a series of arithmetic operations, first calculating the battery's current available capacity (rated capacity multiplied by the current state of charge percentage) and then calculating the battery's remaining capacity (rated capacity minus the current available capacity). The system takes the smaller of the current available capacity and the remaining capacity to determine the theoretical maximum adjustable capacity. The system then multiplies this theoretical value by the previously calculated comprehensive state coefficient to obtain the actual adjustable capacity of the vanadium flow battery under the current operating conditions. This final value, stored in kilowatt-hours, represents the maximum power regulation capability of the vanadium flow battery under current operating conditions. The system packages the adjustable capacity values ​​of the supercapacitor and vanadium flow battery, along with the relevant calculation parameters, into a data structure and passes it to the power allocation algorithm module, which serves as the basis for developing a coordinated control strategy for the energy storage devices.

[0035] 103. Real-time monitoring of industrial load demand, and control of supercapacitors and vanadium flow batteries to switch between active power regulation and reactive power compensation modes according to power allocation instructions and industrial load demand, to obtain a power regulation solution that matches photovoltaic energy storage equipment with industrial loads; In one embodiment of the present invention, controlling the supercapacitor and the vanadium flow battery to switch between active power regulation and reactive power compensation modes according to the power allocation instruction and the industrial load demand to obtain a power regulation scheme that matches the photovoltaic energy storage device with the industrial load includes: analyzing the time-varying characteristics of the active power and reactive power in the industrial load demand, and identifying the power characteristic signatures of different types of industrial equipment according to the time-varying characteristics; judging the load type of the industrial load demand according to the identified power characteristic signature, wherein the load type includes an inductive load and a high-power starting load; when an inductive load is detected, controlling the supercapacitor and the vanadium flow battery to switch to a reactive power compensation mode and output capacitive reactive power through an inverter according to the power allocation instruction; when a high-power starting load is detected, controlling the supercapacitor and the vanadium flow battery to switch to an active power regulation mode according to the power allocation instruction and pre-adjusting the charge state of the photovoltaic energy storage device to a preset optimal regulation range according to the power allocation instruction; and generating a power regulation scheme that matches the photovoltaic energy storage device with the industrial load according to the periodic variation pattern of the industrial load demand and the power allocation instruction.

[0036] Specifically, during the coordinated control of the PV energy storage system and industrial loads, the system first performs a detailed time-varying analysis of the active and reactive power characteristics of the industrial load demand. The system continuously collects real-time active and reactive power data from load monitoring devices. This data is recorded at a fixed sampling interval, typically 10 to 100 data points per second, forming a continuous power time series. To extract time-varying features, the system performs multi-level digital signal processing on this raw data. First, the system applies a sliding window technique, setting time windows of varying lengths to capture variations at different time scales. Short windows are used to capture rapid changes at the second level, while long windows are used to identify trends at the minute level. Within each time window, the system calculates statistical power parameters, including mean, variance, skewness, and kurtosis. These parameters quantify the distribution characteristics and fluctuations of the power signal. The system then performs frequency domain analysis, converting the time domain signal to the frequency domain using a fast Fourier transform (FFT) to extract the spectral characteristics of the power signal. Spectral analysis can identify periodic components and dominant frequencies in power variations. Different types of industrial equipment generate power fluctuations within a specific frequency range during operation. The system also calculates the correlation parameters between active power and reactive power, and analyzes the time delay relationship and coupling strength between the two through the cross-correlation function. This analysis helps to identify the nature and working mode of the load.

[0037] Specifically, based on the extracted time-varying features, the system establishes a power signature recognition algorithm to distinguish different types of industrial equipment. This algorithm maintains a signature database that stores the power variation patterns of various typical industrial equipment. The signatures of inductive loads such as motors and transformers exhibit relatively stable active power changes, but significant and stable reactive power demand, and a typically lagging power factor. The signatures of resistive loads such as heaters show synchronized changes in active and reactive power, with a power factor close to unity. The signatures of nonlinear loads such as inverters and switching power supplies exhibit complex harmonic characteristics and irregular power fluctuations. The system matches the real-time monitored power time-varying features with standard signatures in the database. Using a minimum distance classification algorithm, it calculates the Euclidean distance between feature vectors. The signature class with the smallest distance is determined as the most likely type of the current load. In addition to basic load type identification, the system can also detect changes in the load's operating state, such as a motor switching from no-load to full-load or intermittent operation of a heating device. These state changes leave specific traces in the power signature.

[0038] Specifically, after identifying the power signature, the system further determines the specific load type of the industrial load demand, primarily distinguishing between inductive loads and high-power starting loads. Inductive loads are identified based on the persistent and relatively stable reactive power demand in their power signature. The system quantifies the load's inductiveness by analyzing the ratio of reactive power to active power. When this ratio exceeds a preset threshold and remains stable, the system classifies the load as inductive. High-power starting load identification focuses on sudden power changes. The system monitors the rate of change of active power. If it detects a rapid increase in power exceeding a preset multiple within a short period of time, it identifies a high-power device startup event. Upon detecting an inductive load, the system immediately reads the current power allocation command, which contains the power allocation ratio and operating parameters for the supercapacitor and vanadium flow battery in reactive compensation mode. The system then sends a mode switch command to the energy storage device controller, instructing the supercapacitor and vanadium flow battery to switch from their current operating mode to reactive compensation mode. In reactive power compensation mode, the energy storage device's inverter adjusts its output phase angle to inject capacitive reactive power into the grid. This capacitive reactive power offsets the inductive reactive power consumed by inductive loads, thereby reducing the system's total reactive power demand. The inverter's control algorithm determines the reactive power output target for each device based on the detected reactive power shortfall and power allocation instructions. Capacitive reactive power output is then achieved through precise phase angle control.

[0039] Specifically, when the system detects a high-power starting load, the processing flow differs. The system first analyzes the active power regulation mode configuration parameters in the power allocation instruction, including the power allocation ratios and response timing arrangements for each energy storage device. The system then sends a mode switch instruction to the energy storage device, switching it from its current mode to active power regulation mode. In this mode, the energy storage device's inverter adjusts to a configuration that can quickly respond to the active power demand. Simultaneously, the system performs a pre-adjustment of the state of charge (SOC). Based on the preset optimal adjustment range parameters, the system calculates the deviation between the current SOC and the target range. The optimal adjustment range is typically set between 40% and 60% of the energy storage device's rated capacity. This range ensures that the device has sufficient discharge capacity to meet the power demand and sufficient charging capacity to absorb the feedback power during a high-power starting event. The system gradually adjusts the SOC to within the target range by adjusting the charge and discharge power of the energy storage device. This adjustment process uses a progressive control strategy to avoid additional power surges on the grid. After the SOC adjustment is complete, the energy storage device enters standby mode, ready to handle the upcoming high-power starting surge. The system also estimates the duration and power amplitude of the startup shock based on historical data and current equipment status, and adjusts the power reserve of the energy storage device accordingly.

[0040] Specifically, the system comprehensively considers the cyclical variations in industrial load demand and current power allocation instructions to generate a comprehensive power regulation plan that matches PV energy storage equipment with industrial load. The system first analyzes historical load data to identify typical daily and weekly variations in industrial load. These patterns reflect the regular characteristics of industrial production, such as load differences between weekdays and non-weekdays and electricity consumption patterns at different times of the day. Based on these cyclical patterns, the system establishes a load forecasting model that can estimate load trends over a specified timeframe. Combining the current power allocation instructions with the energy storage equipment status, the system formulates a power regulation strategy for the next several hours to a day. This strategy includes details such as the energy storage equipment's operating mode arrangement, power output plan, and state-of-charge management scheme. The power regulation plan also includes an emergency response plan. If actual load deviates from the forecast, the system automatically adjusts the control strategy to ensure a consistent match between the energy storage equipment and the industrial load. The plan also defines the coordinated control logic for the energy storage equipment under different load conditions, ensuring that supercapacitors and vanadium flow batteries can perform corresponding regulation tasks based on their respective characteristics and strengths, achieving optimal overall system operation.

[0041] Furthermore, when an inductive load is detected, controlling the supercapacitor and the vanadium flow battery to switch to a reactive compensation mode and output capacitive reactive power through the inverter according to the power allocation instruction includes: allocating reactive compensation tasks for the supercapacitor and the vanadium flow battery according to the reactive power allocation ratio in the power allocation instruction to obtain respective reactive power output target values; switching the inverter of the supercapacitor from an active power regulation mode to a reactive power compensation mode, adjusting the power factor control parameters of the inverter, and obtaining a reactive compensation control instruction for the supercapacitor; adjusting the inverter output phase angle of the vanadium flow battery according to the reactive power demand characteristics of the inductive load to obtain a reactive compensation control instruction for the vanadium flow battery; and coordinating the output timing and amplitude of the two inverters according to the reactive compensation control instructions of the supercapacitor and the vanadium flow battery to obtain and execute a coordinated capacitive reactive power output control strategy, and injecting capacitive reactive power into the power grid through the inverter.

[0042] Specifically, when the PV energy storage system detects the presence of an inductive load, it immediately initiates the reactive power compensation process. It first extracts the reactive power allocation ratio parameters from the currently valid power allocation instructions. These instructions are stored in system memory as structured data and contain the power allocation configurations for the supercapacitors and vanadium flow batteries in different operating modes. The system reads the allocation ratio settings for the reactive compensation mode. This ratio is typically determined dynamically based on the characteristics of the two energy storage devices and their current available capacity. For example, if the supercapacitor has a large available capacity and requires a high response speed, its allocation ratio will be increased accordingly. Meanwhile, vanadium flow batteries, due to their high sustained output capability, may assume a greater role in long-term reactive power compensation scenarios. The system then obtains the total reactive power demand of the inductive load from the load monitoring device. This value represents the amount of capacitive reactive power the system must provide to achieve power factor correction. Based on the allocation ratio and the total demand, the system performs a multiplication operation to calculate the reactive power output target values ​​for the supercapacitors and vanadium flow batteries, respectively. These target values ​​are stored in kilovars (kVar) and transmitted to the control unit of each energy storage device in real time as a benchmark reference for inverter control.

[0043] Specifically, for supercapacitor inverter control, the system first performs a mode switch, switching the inverter from its current active power regulation mode to reactive power compensation mode. This switch requires reconfiguring the inverter's control algorithm and parameter settings, as active power regulation primarily focuses on the in-phase components of the output current and grid voltage, while reactive power compensation mode controls the quadrature components of current and voltage. The system sends a mode switch command to the inverter's digital signal processor, which then loads the control program for the reactive power compensation mode. Under the new control mode, the inverter begins adjusting its power factor control parameters. Power factor is the ratio of active power to apparent power. The magnitude and properties of the power factor can be altered by adjusting the phase angle of the inverter's output current. The system calculates the target power factor based on the current grid power factor and the amount of reactive power to be compensated. The inverter control algorithm adjusts the phase parameters of the pulse-width modulation (PWM) signal based on the target power factor. PWM is a technique that controls output voltage and current by varying pulse width. By precisely controlling the duty cycle and phase of the PWM signal, the inverter generates an output current with the desired phase angle, thereby achieving capacitive reactive power output. The system generates a reactive power compensation control command for the supercapacitor. This command includes detailed parameters such as the target reactive power value, power factor setting, and phase angle adjustment, and is transmitted to the inverter execution unit via the communication bus.

[0044] Specifically, the system adjusts the control strategy of the vanadium flow battery inverter accordingly. The control strategy for vanadium flow battery inverters differs slightly from that of supercapacitors, primarily due to differences in their response and capacity characteristics. The system first analyzes the reactive power demand characteristics of the inductive load, including key parameters such as demand stability, rate of change, and duration. The reactive power demand of inductive loads typically exhibits a relatively stable and continuous consumption, which aligns with the long-term, stable output of vanadium flow batteries. Based on this information, the system adjusts the output phase angle of the vanadium flow battery inverter. The phase angle refers to the phase difference between the output current and the grid voltage. By changing the phase angle, the direction and magnitude of reactive power output can be controlled. When the phase angle is positive, the inverter outputs inductive reactive power; when the phase angle is negative, it outputs capacitive reactive power. The system calculates the precise phase angle required to achieve the target reactive power output and then sends a phase angle adjustment command to the inverter's control system. Upon receiving the command, the inverter adjusts the control timing of its power electronic switching devices, regulating the output current phase by changing the timing of switching. This phase angle control technology can precisely control the output amount and properties of reactive power without changing the output current amplitude. The system generates reactive power compensation control instructions for vanadium redox flow batteries, which specify control factors such as output phase angle, current amplitude, and frequency synchronization parameters.

[0045] Specifically, the system coordinates the output timing and amplitude of the two inverters to avoid mutual interference and ensure optimal overall compensation. This coordinated control is essential because uncoordinated operation of the two inverters can lead to reactive power oscillations, which not only affect compensation effectiveness but can also negatively impact grid quality. The system establishes a central coordination control module that monitors the operating status and output parameters of the two inverters in real time, including output current, voltage, phase angle, and power factor. A coordination algorithm dynamically adjusts the operating parameters of the two inverters based on preset optimization objectives and constraints to ensure their coordinated operation. Timing coordination primarily involves the sequencing of the startup and adjustment actions of the two inverters. The system typically prioritizes the faster-responding supercapacitor inverter, followed by the vanadium flow battery inverter. This minimizes power fluctuations during the transition period. Amplitude coordination ensures that the output strength of the two inverters precisely matches their assigned reactive power targets, preventing one device from being overloaded while the other is idle. The coordinated control module generates the final capacitive reactive power output control strategy, which includes the detailed operating parameters, operation timing, output target values, and abnormal situation handling plans for the two inverters. The system then executes this control strategy, and the two inverters operate synchronously according to the coordinated parameters, injecting precisely calculated capacitive reactive power into the grid. By monitoring changes in the grid power factor in real time, the system verifies the effectiveness of the compensation and fine-tunes the inverter operating parameters based on actual conditions to ensure that the reactive power requirements of the inductive loads are fully met while maintaining the grid power factor within the ideal range.

[0046] 104. Monitor the power quality indicators of the parallel connection points in real time, and control the inverter of the photovoltaic energy storage equipment to perform voltage regulation and harmonic compensation according to the power regulation plan and power quality indicators to achieve power quality control.

[0047] In one embodiment of the present invention, the control of the inverter of the photovoltaic energy storage device according to the power regulation scheme and the power quality index to perform voltage regulation and harmonic compensation to achieve power quality control includes: when the voltage deviation in the power quality index exceeds a preset range, controlling the vanadium liquid flow battery to adjust the power factor through the inverter to achieve voltage regulation according to the reactive power distribution strategy in the power regulation scheme; when the total harmonic distortion rate in the power quality index exceeds the standard, adopting the power quality multi-parameter linkage compensation mechanism to analyze the harmonic frequency characteristics, and controlling the inverter to generate harmonic currents with opposite phases for active filtering according to the power regulation scheme; when the three-phase voltage imbalance in the power quality index exceeds the standard, controlling the reactive power output of each phase according to the power regulation scheme to achieve negative sequence voltage component compensation, thereby obtaining a power quality control effect in which the voltage deviation is controlled within the preset range and the total harmonic distortion rate is reduced.

[0048] Specifically, during the power quality control process of the photovoltaic energy storage system, the system first continuously monitors voltage deviations, a key indicator of power quality. Precision voltage sensors deployed at the parallel connection points collect three-phase voltage readings in real time. The system compares the measured actual voltage values ​​with the system's rated voltage and calculates the voltage deviation percentage for each phase. This deviation reflects the degree to which the current voltage deviates from the standard voltage. If the voltage deviation of any phase exceeds a preset range, typically set at ±5% of the rated voltage, the system immediately initiates voltage regulation. The system then retrieves the currently active power regulation scheme and extracts the detailed parameters of the reactive power allocation strategy. These parameters define the operating configuration and power output limits of the vanadium flow battery in voltage regulation mode. Since voltage regulation is primarily achieved through the injection or absorption of reactive power, and vanadium flow batteries have a continuous and stable reactive power output capability, the system prioritizes vanadium flow batteries for voltage regulation. The system then sends power factor adjustment commands to the vanadium flow battery inverter. Power factor measures the ratio of active power to apparent power in a circuit. Precisely controlling the inverter's power factor enables effective grid voltage regulation. After receiving the command, the inverter adjusts the phase angle of its output current. When the grid voltage needs to be increased, the inverter outputs capacitive reactive power, meaning the current phase leads the voltage. When the grid voltage needs to be decreased, the inverter outputs inductive reactive power, meaning the current phase lags the voltage. Using a real-time feedback control algorithm, the system dynamically adjusts the power factor setpoint based on the magnitude and direction of voltage deviations, ensuring that the voltage gradually returns to the preset normal range.

[0049] Specifically, when the system detects that the total harmonic distortion (THD) in the power quality indicator exceeds a preset threshold, it immediately initiates harmonic compensation. THD is a key indicator that measures the degree to which a voltage or current waveform deviates from an ideal sine wave. It is primarily caused by harmonic pollution generated by nonlinear loads such as inverters and rectifiers. The system first performs frequency domain analysis on the current voltage and current signals using a fast Fourier transform algorithm, converting the time-domain signals into a frequency-domain representation to identify the frequency, amplitude, and phase information of each harmonic. This analysis accurately determines the frequency characteristics of harmonics, including the dominant harmonic order, the relative strength of each harmonic, and their distribution within the frequency spectrum. Based on the harmonic analysis results, the system implements a multi-parameter coordinated power quality compensation mechanism, a coordinated control strategy that comprehensively considers multiple parameters such as voltage, current, and power. The system retrieves harmonic compensation control parameters from the power regulation scheme, including the type of energy storage device available for harmonic compensation, the power allocation ratio, and the compensation frequency range. The system then calculates the harmonic compensation currents that need to be injected into the grid. These compensation currents have the same frequency as the detected harmonics but opposite phases, reducing the total harmonic distortion (THD) by canceling each other out. The system then sends active filtering control instructions to the energy storage device's inverter, which include detailed parameters such as the compensation current amplitude, phase angle, and injection timing for each harmonic. Upon receiving the instructions, the inverter's control system adjusts its pulse width modulation strategy to generate output currents containing specific harmonic components that are opposite in phase to the original harmonics in the grid, thereby achieving active filtering.

[0050] Specifically, three-phase voltage imbalance is another significant power quality issue. When the system detects that the three-phase voltage imbalance exceeds a preset standard, it initiates a negative-sequence voltage compensation program. Three-phase voltage imbalance is typically caused by uneven single-phase load distribution or equipment failure, manifesting as unequal three-phase voltage amplitudes or phase angles deviating from the ideal 120-degree interval. The system uses a symmetrical component method to decompose the three-phase voltage imbalance into three symmetrical components: positive, negative, and zero sequence. The negative sequence component is the primary contributor to voltage imbalance. The presence of negative sequence components causes the motor to generate a reverse-rotating magnetic field, increasing equipment losses and potentially causing vibration and noise. The system determines the reactive power output allocation for each phase's energy storage device based on the voltage balance control strategy within the power regulation scheme. Because three-phase voltage imbalance requires independent phase adjustment, the system controls the inverters of each phase to output reactive power of varying amplitudes and phases. The specific control strategy injects capacitive reactive power into phases with lower voltage to boost their voltage and inductive reactive power into phases with higher voltage to reduce their voltage. This differentiated regulation achieves three-phase voltage rebalancing. The system calculates the reactive power compensation required for each phase based on the magnitude of the negative sequence voltage component and the deviation of the phase voltage, and then converts these compensation amounts into control parameters for the inverter.

[0051] Specifically, in implementing the aforementioned power quality control measures, the system employs a multi-parameter linkage control strategy to coordinate the interactions between the various compensation functions. Since voltage regulation, harmonic compensation, and voltage balance control all affect the voltage and current characteristics of the power grid, uncoordinated control actions can interfere with each other and even deteriorate power quality. The system incorporates a comprehensive control module that monitors changes in all power quality parameters, including voltage deviation, total harmonic distortion, and three-phase imbalance, in real time. It dynamically adjusts the intensity and timing of each control measure based on the priority and interplay of these parameters. When multiple power quality issues occur simultaneously, the system implements control measures step by step according to a preset priority order. Voltage deviation control typically has the highest priority, as voltage deviations directly impact equipment operation. Harmonic compensation and voltage balance control are then executed in a sequence determined by the severity and urgency of the issue. Through this coordinated control approach, the system ultimately achieves comprehensive power quality control results: controlling voltage deviation within a preset range, significantly reducing total harmonic distortion, and improving three-phase voltage balance. During the entire control process, the system continuously monitors the improvement of power quality indicators and adjusts control parameters according to actual results to ensure that power quality always remains at an ideal level.

[0052] In this embodiment, by real-time monitoring of the output power and environmental parameters of the photovoltaic energy storage equipment, analyzing the power fluctuation trend, and predicting the power fluctuation characteristics; determining the power distribution ratio and collaborative working mode of the energy storage elements based on the operating status parameters of the supercapacitor and vanadium liquid flow battery and the power fluctuation prediction results; controlling the energy storage elements to flexibly switch between active regulation and reactive compensation modes based on the industrial load demand characteristics; and achieving voltage regulation and harmonic compensation by monitoring the power quality indicators of the parallel points. The present invention can effectively enhance the photovoltaic energy storage system's ability to respond quickly to industrial loads, significantly improve power supply stability and power quality, reduce system power fluctuations, improve the utilization efficiency of energy storage equipment, and provide reliable clean energy support for industrial users.

[0053] The above describes the intelligent management method of photovoltaic energy storage equipment in the embodiment of the present invention. The following describes the intelligent management system of photovoltaic energy storage equipment in the embodiment of the present invention. Figure 2 An embodiment of the intelligent management system of photovoltaic energy storage equipment in the embodiment of the present invention includes: The power prediction module 201 is used to continuously monitor the real-time output power and environmental parameters of the photovoltaic energy storage device, and analyze the power change trend and power fluctuation parameters of the photovoltaic energy storage device based on the monitoring data to obtain the power fluctuation prediction result; The allocation and scheduling module 202 is used to monitor the operating status parameters of the supercapacitors and vanadium flow batteries in the photovoltaic energy storage device in real time, and obtain power allocation instructions for the photovoltaic energy storage device based on the power allocation ratio and collaborative working mode of the supercapacitors and vanadium flow batteries determined according to the operating status parameters and the power fluctuation prediction results; A mode switching module 203 is configured to monitor the industrial load demand of the industrial load in real time and control the supercapacitor and the vanadium flow battery to switch between active power regulation and reactive power compensation modes according to the power allocation instruction and the industrial load demand, thereby obtaining a power regulation solution that matches the photovoltaic energy storage device with the industrial load; The quality control module 204 is used to monitor the power quality index of the parallel point in real time, and control the inverter of the photovoltaic energy storage device to perform voltage regulation and harmonic compensation according to the power regulation scheme and the power quality index to achieve power quality control.

[0054] In an embodiment of the present invention, the intelligent management system of the photovoltaic energy storage device runs the intelligent management method of the photovoltaic energy storage device described above. The intelligent management system of the photovoltaic energy storage device monitors the output power and environmental parameters of the photovoltaic energy storage device in real time, analyzes the power fluctuation trend, and predicts the power fluctuation characteristics; determines the power distribution ratio and collaborative working mode of the energy storage element according to the operating status parameters of the supercapacitor and the vanadium liquid flow battery and the power fluctuation prediction results; controls the energy storage element to flexibly switch between active regulation and reactive compensation modes based on the industrial load demand characteristics; and realizes voltage regulation and harmonic compensation by monitoring the power quality indicators of the parallel points. The present invention can effectively improve the rapid response capability of the photovoltaic energy storage system to industrial loads, significantly improve the power supply stability and power quality, reduce system power fluctuations, improve the utilization efficiency of the energy storage equipment, and provide reliable clean energy support for industrial users.

[0055] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0056] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0057] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent management method for photovoltaic energy storage equipment, characterized in that: The intelligent management method of the photovoltaic energy storage device includes: Continuously monitor the real-time output power and environmental parameters of photovoltaic energy storage equipment, and analyze the power change trend and power fluctuation parameters of photovoltaic energy storage equipment based on the monitoring data to obtain power fluctuation prediction results; Real-time monitoring of operating status parameters of supercapacitors and vanadium flow batteries in the photovoltaic energy storage device, and obtaining power allocation instructions for the photovoltaic energy storage device by determining the power allocation ratio and collaborative working mode of the supercapacitors and vanadium flow batteries based on the operating status parameters and power fluctuation prediction results; Real-time monitoring of industrial load demand of industrial loads, and controlling the supercapacitor and vanadium flow battery to switch between active power regulation and reactive power compensation modes according to the power allocation instructions and industrial load demand, thereby obtaining a power regulation solution that matches the photovoltaic energy storage device with the industrial load; The power quality index of the parallel point is monitored in real time, and the inverter of the photovoltaic energy storage device is controlled to perform voltage regulation and harmonic compensation according to the power regulation scheme and the power quality index to achieve power quality control.

2. The intelligent management method of photovoltaic energy storage equipment according to claim 1, characterized in that: The power fluctuation parameters include fluctuation type and fluctuation duration; The power fluctuation prediction result obtained by analyzing the power change trend and power fluctuation parameters of the photovoltaic energy storage device based on the monitoring data includes: The real-time output power in the monitoring data is sampled using a time sliding window technique, and the power change rate is calculated based on the sampling results. When the power change rate exceeds the preset threshold, the power change trend in the short, medium and long time windows is analyzed, and the comprehensive fluctuation index is calculated through weighted fusion; The cloud occlusion pattern is identified based on the numerical range of the comprehensive fluctuation index, the gradient of the ambient light intensity change in the environmental parameters, and the cloud movement speed parameter, and three types of fluctuations are distinguished: short-term occlusion, medium-term occlusion, and long-term occlusion. Based on the comprehensive fluctuation index, fluctuation type and cloud movement speed parameters, the fluctuation duration is predicted to obtain a power fluctuation prediction result including the fluctuation type and fluctuation duration.

3. The intelligent management method of photovoltaic energy storage equipment according to claim 1, characterized in that: The operating state parameters include the state of charge and internal resistance of the supercapacitor and the electrolyte flow rate and temperature of the vanadium liquid flow battery; The power allocation ratio of the supercapacitor and the vanadium flow battery is determined according to the operating state parameters and the power fluctuation prediction result, and the power allocation instruction of the corresponding photovoltaic energy storage device is generated, which includes: Calculate the available regulation capacity of supercapacitors and vanadium flow batteries based on the state of charge and internal resistance of the supercapacitor and the electrolyte flow and temperature of the vanadium flow battery; When the fluctuation type in the power fluctuation prediction result is short-term obstruction and the available regulation capacity of the supercapacitor is within a preset reasonable range, the supercapacitor is allocated to undertake the preset main regulation task to obtain the corresponding power allocation ratio; When the fluctuation type in the power fluctuation prediction result is medium-duration or long-duration obstruction, a dynamic weight allocation algorithm is used to determine the power allocation ratio based on the fluctuation duration and the available regulation capacity of the supercapacitor and vanadium flow battery; According to the power allocation ratio, a power allocation instruction for the corresponding photovoltaic energy storage device is generated.

4. The intelligent management method of photovoltaic energy storage equipment according to claim 3, characterized in that: Calculating the available adjustment capacity of the supercapacitor and the vanadium flow battery according to the state of charge and internal resistance of the supercapacitor and the electrolyte flow and temperature of the vanadium flow battery includes: Evaluate the state of charge of the supercapacitor, calculate the difference between the current state of charge and the maximum state of charge and the minimum state of charge, and obtain the remaining charge of the supercapacitor; Evaluate the health status of the supercapacitor based on its internal resistance, perform health correction on the remaining charge, and calculate the adjustable capacity of the supercapacitor in its current state. Monitor and analyze the electrolyte flow rate of the vanadium flow battery, calculate the electrolyte circulation efficiency based on the flow parameters, and obtain the electrolyte flow state coefficient; A temperature correction coefficient is calculated according to the temperature parameters of the vanadium flow battery, the electrolyte flow state coefficient is temperature compensated, and the adjustable capacity of the vanadium flow battery is calculated based on the compensated electrolyte flow state coefficient.

5. The intelligent management method of photovoltaic energy storage equipment according to claim 1, characterized in that: According to the power distribution instruction and industrial load demand, controlling the supercapacitor and the vanadium flow battery to switch between the active power regulation mode and the reactive power compensation mode to obtain a power regulation scheme that matches the photovoltaic energy storage device with the industrial load includes: Analyze the time-varying characteristics of active power and reactive power in industrial load demand, and identify the power signatures of different types of industrial equipment based on the time-varying characteristics; Determining the load type of the industrial load demand based on the identified power characteristic signature, wherein the load type includes an inductive load and a high-power starting load; When an inductive load is detected, the supercapacitor and vanadium flow battery are controlled to switch to reactive power compensation mode according to the power distribution instruction and output capacitive reactive power through the inverter; When a high-power starting load is detected, the supercapacitor and vanadium flow battery are controlled to switch to active power regulation mode according to the power allocation instruction and the charge state of the photovoltaic energy storage device is pre-adjusted to the preset optimal regulation range; Based on the periodic change pattern of industrial load demand and power allocation instructions, a power regulation plan is generated to match photovoltaic energy storage equipment with industrial load.

6. The intelligent management method of photovoltaic energy storage equipment according to claim 5, characterized in that: When an inductive load is detected, controlling the supercapacitor and the vanadium flow battery to switch to a reactive power compensation mode according to a power distribution instruction and outputting capacitive reactive power through an inverter includes: According to the reactive power allocation ratio in the power allocation instruction, the reactive power compensation tasks of the supercapacitor and the vanadium redox flow battery are allocated to obtain their respective reactive power output target values; Switching the supercapacitor inverter from active power regulation mode to reactive power compensation mode, adjusting the inverter's power factor control parameters, and obtaining a reactive power compensation control instruction for the supercapacitor; The inverter of the vanadium redox flow battery adjusts the inverter output phase angle according to the reactive power demand characteristics of the inductive load to obtain the reactive power compensation control instruction of the vanadium redox flow battery; According to the reactive power compensation control instructions of supercapacitors and vanadium flow batteries, the output timing and amplitude of the two sets of inverters are coordinated and controlled, and the coordinated capacitive reactive power output control strategy is obtained and executed to inject capacitive reactive power into the grid through the inverter.

7. The intelligent management method of photovoltaic energy storage equipment according to claim 1, characterized in that: Controlling the inverter of the photovoltaic energy storage device to perform voltage regulation and harmonic compensation according to the power regulation scheme and power quality indicators to achieve power quality control includes: When the voltage deviation in the power quality indicator exceeds a preset range, the vanadium liquid flow battery is controlled to adjust the power factor through the inverter to achieve voltage regulation according to the reactive power allocation strategy in the power regulation scheme; When the total harmonic distortion rate in the power quality index exceeds the standard, the power quality multi-parameter linkage compensation mechanism is used to analyze the harmonic frequency characteristics, and according to the power regulation scheme, the inverter is controlled to generate harmonic currents with opposite phases for active filtering; When the three-phase voltage imbalance in the power quality index exceeds the standard, the reactive power output of each phase is controlled according to the power regulation scheme to achieve negative sequence voltage component compensation, thereby obtaining a power quality control effect in which the voltage deviation is controlled within a preset range and the total harmonic distortion rate is reduced.

8. An intelligent management system for photovoltaic energy storage equipment, characterized in that: The intelligent management system of the photovoltaic energy storage equipment includes: The power prediction module is used to continuously monitor the real-time output power and environmental parameters of the photovoltaic energy storage equipment, and analyze the power change trend and power fluctuation parameters of the photovoltaic energy storage equipment based on the monitoring data to obtain the power fluctuation prediction results; An allocation and scheduling module is used to monitor the operating status parameters of the supercapacitors and vanadium flow batteries in the photovoltaic energy storage device in real time, and obtain power allocation instructions for the photovoltaic energy storage device based on the power allocation ratio and collaborative working mode of the supercapacitors and vanadium flow batteries determined according to the operating status parameters and power fluctuation prediction results; A mode switching module is used to monitor the industrial load demand of the industrial load in real time, and control the supercapacitor and the vanadium flow battery to switch between active power regulation and reactive power compensation modes according to the power allocation instruction and the industrial load demand, so as to obtain a power regulation solution that matches the photovoltaic energy storage device with the industrial load; The quality control module is used to monitor the power quality indicators of the parallel points in real time, and control the inverter of the photovoltaic energy storage equipment to perform voltage regulation and harmonic compensation according to the power regulation scheme and power quality indicators to achieve power quality control.

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