A distributed photovoltaic grid-connected power generation control method based on big data

Abstract

A kind of distributed photovoltaic grid-connected power generation control method based on big data, it is related to big data field, it is characterized in that, the state information of virtual power grid is acquired, if the state information of virtual power grid, satisfy first grid-connected condition, then the virtual power grid is incorporated into power system, the virtual power grid includes one or more photovoltaic power generation unit;If the virtual power grid does not satisfy grid-connected condition, then based on the state information, generate second grid-connected condition;Based on second grid-connected condition, one or more photovoltaic power generation unit in the virtual power grid is removed from the virtual power grid, and / or one or more to be grid-connected power generation unit outside the virtual power grid is added to the virtual power grid, until the virtual power grid satisfies third grid-connected condition.The present application utilizes big data technology, and simulates as centralized photovoltaic power generation system by virtual networking technology to the distributed photovoltaic power generation unit, improves the reliability and stability of grid connection.

Description

Technical Field

[0001] This invention belongs to the field of big data technology, specifically designing a distributed photovoltaic grid-connected power generation control method based on big data. Background Technology

[0002] Big data technology refers to the techniques and capabilities for rapidly extracting valuable information from various types of data. It encompasses multiple stages, including data collection, storage, processing, analysis, and visualization.

[0003] In distributed photovoltaic (PV) power generation grid-connected control systems, big data technology can collect a large amount of operational data from distributed PV power generation systems in real time, including power generation, voltage, current, and temperature. This provides fundamental data support for the stable operation of the system. By analyzing historical operational data, big data algorithms can identify potential faults in advance, enabling timely warnings and handling, reducing system downtime, and improving system reliability. Big data-based PV power generation grid-connected control systems can analyze power generation data under different time periods and weather conditions, optimizing the PV power generation system's operating strategies, such as adjusting the inverter's output power and optimizing the charging and discharging strategies of the battery energy storage system. This improves the system's power generation efficiency and economic benefits. Integrating relevant data from the distributed PV power generation system and the power grid enables energy management and dispatching of the entire power system, improving grid stability and the absorption capacity of renewable energy. However, the integration of distributed PV power generation systems still falls short of ideal standards. Individual PV power generation units in distributed PV power generation systems are small in capacity and numerous, leading to frequent grid connection operations that threaten the stability of the power system. Summary of the Invention

[0004] This invention provides a distributed photovoltaic grid-connected power generation control method based on big data, which can reduce the difficulty of distributed photovoltaic power generation grid connection control and improve the stability of grid connection operation.

[0005] This invention provides a distributed photovoltaic grid-connected power generation control method based on big data. The method includes: acquiring the status information of a virtual power grid; if the status information of the virtual power grid meets a first grid connection condition, then connecting the virtual power grid into the power system, wherein the virtual power grid includes one or more photovoltaic power generation units; if the virtual power grid does not meet the grid connection condition, then generating a second grid connection condition based on the status information; based on the second grid connection condition, removing one or more photovoltaic power generation units within the virtual power grid from the virtual power grid, and / or adding one or more power generation units outside the virtual power grid to the virtual power grid, until the virtual power grid meets the first grid connection condition.

[0006] In some embodiments, the status information of the virtual power grid includes at least one of voltage, frequency, active power, reactive power, and power balance among photovoltaic power generation units.

[0007] In some embodiments, the first grid connection conditions include: the voltage is within ±5% of the rated voltage, the frequency is stable at 50±0.2Hz, the active power and reactive power meet the power system access requirements, and the power balance deviation between each photovoltaic power generation unit does not exceed 10%.

[0008] In some embodiments, generating the second grid connection condition based on the status information includes: using big data analysis to determine the difference between the status information and the first grid connection condition, and combining historical grid connection data and a prediction model to determine the second grid connection condition.

[0009] In some embodiments, removing one or more photovoltaic power generation units from the virtual power grid and / or adding one or more power generation units outside the virtual power grid to the virtual power grid includes: assessing the impact of each photovoltaic power generation unit and the power generation units to be connected to the grid on the state of the virtual power grid through big data based on a second grid connection condition, and selecting photovoltaic power generation units to be removed or added.

[0010] In some embodiments, before obtaining the status information of the virtual power grid, the method further includes: collecting and analyzing historical operating data of each photovoltaic power generation unit through big data to construct an initial model of the virtual power grid.

[0011] In some embodiments, after the virtual grid is integrated into the power system, the method further includes: acquiring real-time operating data of the power system and the virtual grid, using big data analysis to assess the impact of distributed photovoltaic grid-connected power generation on the stability and power quality of the power system, and adjusting the operating parameters of the virtual grid based on the assessment results.

[0012] In some embodiments, the status information of the photovoltaic power generation unit includes at least one of the following: photovoltaic module temperature, photovoltaic module output voltage, photovoltaic module output current, inverter operating status, and remaining power of the energy storage device.

[0013] In some embodiments, the method further includes: using big data to establish a real-time monitoring and early warning mechanism for a virtual power grid, and selecting photovoltaic power generation units to be removed or added when the status information of the virtual power grid exceeds a preset threshold.

[0014] In some embodiments, after the virtual grid is integrated into the power system, the method further includes: analyzing the power generation data after the virtual grid is integrated using big data analysis, and updating the setting parameters of the first grid connection condition and the second grid connection condition.

[0015] In some embodiments, in the step of removing one or more photovoltaic power generation units from the virtual grid, the photovoltaic power generation units are removed from the virtual grid in a manner that maximizes the number of units removed each time; and in the step of adding one or more photovoltaic power generation units to be connected to the grid outside the virtual grid, the photovoltaic power generation units to be connected to the grid are added to the virtual grid in a manner that minimizes the number of units added each time.

[0016] In some embodiments, in the step of removing one or more photovoltaic power generation units from the virtual power grid, only one photovoltaic power generation unit is removed from the virtual power grid at a time; and in the step of adding one or more photovoltaic power generation units to be connected to the grid outside the virtual power grid to the virtual power grid, only one photovoltaic power generation unit to be connected to the grid is added to the virtual power grid at a time.

[0017] In some embodiments, each time a photovoltaic power generation unit is removed from the virtual grid or a photovoltaic power generation unit to be connected to the grid is added to the virtual grid, it is determined whether the virtual grid meets the first grid connection condition.

[0018] In some embodiments, the second grid connection conditions are recalculated each time a photovoltaic power generation unit is removed from the virtual grid or a photovoltaic power generation unit to be connected to the grid is added to the virtual grid.

[0019] In some embodiments, in the steps of removing one or more photovoltaic power generation units from the virtual grid and / or adding one or more photovoltaic power generation units to be connected to the virtual grid from outside the virtual grid, if the number of photovoltaic power generation units in the virtual grid meets a predetermined threshold, removing one or more photovoltaic power generation units from the virtual grid is performed first; otherwise, adding one or more photovoltaic power generation units to be connected to the virtual grid from outside the virtual grid is performed first.

[0020] This invention utilizes big data technology to simulate distributed photovoltaic power generation units as a centralized photovoltaic power generation system through virtual networking technology. The multiple photovoltaic power generation units in the virtual network have autonomous systems and a certain adjustment margin, which improves the reliability and stability of grid connection and reduces the difficulty of grid connection. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the power system architecture in some embodiments of this application.

[0022] Figure 2 This is a schematic diagram of a virtual power grid in some embodiments of this application.

[0023] Figure 3 This is a schematic diagram of an autonomous system in some embodiments of this application.

[0024] Figure 4 This is a flowchart illustrating a distributed photovoltaic grid-connected power generation control method based on big data in some embodiments of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Distributed photovoltaic (PV) power generation refers to PV facilities built near user sites, operating with the user's self-consumption of generated electricity and surplus electricity fed into the grid, and characterized by balancing and regulating the power distribution system. Compared to centralized PV power generation, distributed PV power generation is typically smaller in scale, allowing for flexible construction based on user needs and site conditions. Initial investment is relatively low, making it suitable for individuals, businesses, or small groups. For example, some households can install small distributed PV systems on their rooftops, achieving self-sufficiency in power generation with an investment of tens of thousands of yuan. Installation is more flexible, requiring less extensive site leveling and infrastructure construction like centralized PV power generation. Generally, small-scale distributed PV power generation projects can be installed and commissioned within a few weeks and quickly put into use. Distributed PV power generation systems are often installed near users, such as factory rooftops, commercial building rooftops, or residential rooftops, allowing for direct local use of the generated electricity and reducing losses during power transmission. It is estimated that distributed PV power generation can reduce transmission losses by approximately 10%-20% compared to centralized PV power generation. Distributed photovoltaic (PV) power generation enables a self-consumption model with surplus electricity fed into the grid. Users can prioritize using the electricity generated by their own PV systems to meet their own needs, while any excess electricity is fed back into the grid, improving energy efficiency. It can utilize vast amounts of idle rooftops and vacant land in urban and rural areas, without occupying large land areas, making it particularly suitable for densely populated regions with limited land resources. Furthermore, distributed PV systems have relatively low requirements for terrain and topography, allowing for successful application even in complex terrain environments.

[0027] Although individual distributed photovoltaic (PV) power generation projects occupy a small area, achieving a power generation scale comparable to centralized PV power generation requires a larger overall land area. This is because distributed PV systems need to be installed in multiple different locations, making it difficult to utilize land resources as efficiently as centralized PV systems. Distributed PV systems typically consist of multiple smaller-power PV modules, with a single system's power output generally ranging from a few kW to tens of MW. Compared to the tens or even hundreds of MW of power output of centralized PV systems, the power output of a single distributed PV project is significantly lower. Due to the dispersed distribution and large number of distributed PV projects, the difficulty and cost of operation and maintenance are relatively high. Regular inspections, maintenance, and troubleshooting are required for each project, consuming substantial human and material resources. Connecting distributed PV systems to the power grid may have some impact on the grid's voltage, frequency, and power quality. If the scale of the connection is large and mismanaged, it may lead to grid instability, necessitating upgrades and modifications to the grid to meet the requirements of distributed PV integration.

[0028] The grid connection method and voltage level of distributed photovoltaic (PV) power generation systems must comply with the grid connection regulations. Distributed PV projects planned for connection at 10 kV and above must meet the requirements for data acquisition, monitoring, and execution of instructions from the power dispatching agency. Simultaneously, the system should possess corresponding protection devices and automatic safety devices to ensure the safe operation of the grid and the PV system, such as overcurrent protection, overvoltage protection, and leakage protection.

[0029] When generating electricity through grid connection, the quality of the generated power must meet relevant standards, including indicators such as voltage deviation, frequency deviation, harmonic content, and three-phase imbalance. For example, voltage deviation should generally be within ±5% of the rated voltage, and harmonic content must meet the limits specified in national standards to avoid adverse effects on the power grid and other electrical equipment.

[0030] Figure 1 This is a schematic diagram of the power system architecture in some embodiments of this application; Figure 2 This is a schematic diagram of a virtual power grid in some embodiments of this application; Figure 3 These are schematic diagrams of autonomous systems in some embodiments of this application; Figure 4 This is a flowchart illustrating a distributed photovoltaic grid-connected power generation control method based on big data in some embodiments of this application; For example... Figure 1-4 As shown, a distributed photovoltaic grid-connected power generation control method based on big data is applied to a distributed photovoltaic power generation system.

[0031] First, data acquisition devices deployed in a distributed manner in various photovoltaic power generation units and virtual power grids are used to obtain the status information of the virtual power grid in real time and accurately.

[0032] The data acquisition device can collect key data during the operation of the virtual power grid at a sampling frequency of milliseconds. If the status information of the virtual power grid meets the first grid connection condition, that is, the condition is compared by the logic judgment module preset in the control algorithm. When all indicators meet the requirements, the virtual power grid is connected to the power system. The virtual power grid includes one or more photovoltaic power generation units, which are distributed in different geographical locations and realize data interaction and collaborative control through a communication network.

[0033] If the virtual power grid does not meet the grid connection conditions, a second grid connection condition is generated based on the state information and utilizing the powerful computing capabilities and algorithm models of the big data analytics platform. Specifically, the big data analytics platform performs correlation analysis and trend prediction on the massive amounts of real-time and historical data collected.

[0034] Based on the second grid connection condition, an intelligent decision-making module removes one or more photovoltaic power generation units from the virtual grid and / or adds one or more power generation units outside the virtual grid to the virtual grid until the virtual grid meets the third grid connection condition, which is higher than the first grid connection condition. During this process, the intelligent decision-making module comprehensively considers multiple factors, such as the power generation efficiency of each power generation unit and the health status of the equipment. Through the above operations, precise control of distributed photovoltaic grid-connected power generation is achieved, effectively improving the efficiency of the virtual grid in meeting the grid connection conditions, ensuring the smooth progress of subsequent grid connection processes, and avoiding grid connection failures or impacts on the power system due to unmet conditions. The third grid connection condition being higher than the first grid connection condition provides a margin for internal scheduling of the autonomous system, improving system stability. In some embodiments, the third grid connection condition is generally more than 30% higher than the first grid connection condition; preferably, the third grid connection condition is 50% to 75% higher than the first grid connection condition. In some cases, some specific conditions of the third grid connection condition are higher than the first grid connection condition, while others are equal. Therefore, the third grid connection condition is higher than the first grid connection condition. This does not mean that all specific conditions are higher than the first grid connection condition. Rather, some conditions that are more critical to system regulation are higher, such as the capacity information of the virtual grid. Some conditions can be equal, such as voltage, while some conditions are numerically lower than the third grid connection condition, such as the frequency fluctuation range of the grid connection. Those skilled in the art can understand that a lower frequency fluctuation range is a higher requirement.

[0035] like Figure 2-3As shown, the virtual power grid includes an autonomous system (AAS), which integrates high-performance data processing chips, large-capacity storage devices, and dedicated communication modules. The AAS carries specially designed software programs for calculating the virtual power grid's state information, grid connection conditions, and the connection and disconnection operations of photovoltaic (PV) power generation units. In the specific calculation process, advanced numerical calculation algorithms and optimization algorithms are employed, enabling the rapid and accurate processing of complex power system parameters. By setting up this AAS, the large amount of distributed PV grid connection-related data and decision-making tasks that originally required processing by the power system dispatch center can be processed locally by the AAS, significantly reducing the pressure on the power system dispatch center, alleviating the data transmission and processing burden, and avoiding processing delays or system crashes caused by excessive data volume. This AAS can be considered a miniature dispatch center based on big data technology. Furthermore, the AAS, through autonomous control of the virtual power grid, improves the stability of the power system in responding to distributed power source integration and reduces the adverse effects of distributed PV output fluctuations on the power grid.

[0036] The hardware of an autonomous system can be deployed in traditional power plants, such as hydroelectric power plants, thermal power plants, and nuclear power plants. In hydroelectric power plants, the stable power supply and existing communication network infrastructure allow the hardware to be connected, enabling collaborative operation with the power plant's control system and sharing of some data and computing resources. In thermal power plants, the mature operation and maintenance system and professional technical personnel facilitate routine maintenance and troubleshooting of the hardware. Alternatively, centralized photovoltaic (PV) power plants can be chosen. These plants offer ample space and comprehensive power facilities, providing a favorable operating environment for the hardware and allowing for direct access to abundant PV power generation data for analysis and decision-making. Finally, large-capacity, well-equipped distributed PV power plants can be selected. These distributed plants are often located closer to load centers, allowing for faster response to changes in local power demand and improving the real-time performance and effectiveness of virtual grid control.

[0037] This invention utilizes big data technology to simulate a centralized photovoltaic power generation system by virtual networking technology, where the third grid connection condition is higher than the first grid connection condition. This allows the combined virtual grid to have a certain adjustment margin, and the autonomous system can share the computing power pressure of the dispatch center. The multiple photovoltaic power generation units in the virtual network have autonomous systems and adjustment margins, which improves the reliability and stability of grid connection and reduces the difficulty of grid connection.

[0038] In some embodiments, the state information of the virtual power grid includes at least one of voltage, frequency, active power, reactive power, and power balance among photovoltaic power generation units. To acquire this state information, high-precision measuring devices are installed at each node of the virtual power grid and at the output end of each photovoltaic power generation unit. For example, high-precision voltage transformers are used for voltage measurement, with measurement errors controlled within ±0.5%. For power balance calculation, the output power data of each photovoltaic power generation unit is collected in real time, and a specific power balance algorithm is used for calculation. These detailed methods of acquiring and calculating state information provide a rich and reliable data foundation for accurately determining the operating status of the virtual power grid, making the control of the virtual power grid more precise and scientific.

[0039] In some embodiments, the first grid connection conditions include: voltage within ±5% of the rated voltage range, frequency stable at 50±0.2Hz, active and reactive power meeting the power system access requirements, and power balance deviation among photovoltaic power generation units not exceeding 10%. These first grid connection conditions were determined after extensive theoretical research, simulation experiments, and verification using actual operating data. In practical applications, when the parameters of the virtual grid meet these conditions, a safe and stable grid connection between the virtual grid and the power system can be ensured, reducing the impact of grid connection on key indicators such as voltage and frequency of the power system, and guaranteeing the stable operation and power quality of the power system.

[0040] In some embodiments, generating a second grid connection condition based on state information includes: utilizing big data analysis to identify the differences between the state information and the first grid connection condition, and combining historical grid connection data and a prediction model to determine the second grid connection condition. During the big data analysis process, data mining algorithms are used to extract key factors and patterns affecting the grid connection condition from massive amounts of historical grid connection data. Machine learning algorithms are then used to train and optimize the prediction model, enabling it to accurately predict future operating trends based on current state information. For example, when a low voltage is detected in the virtual grid, by analyzing successful grid connection cases under similar conditions in historical data, and combining the prediction model's forecast of voltage change trends, the adjustment strategy and parameter requirements for the photovoltaic power generation unit in the second grid connection condition are determined, thereby providing a scientific basis for subsequent adjustment operations.

[0041] In some embodiments, removing one or more photovoltaic (PV) power generation units from the virtual grid and / or adding one or more power generation units outside the virtual grid to the virtual grid includes: assessing the impact of each PV power generation unit and the power generation units to be connected to the virtual grid on the virtual grid state based on a second grid connection condition using big data, and selecting PV power generation units to be removed or added. During the assessment process, a multi-dimensional assessment index system is constructed, covering factors such as the power generation efficiency of the power generation unit, remaining equipment lifespan, geographical location, and synergy with other units. Big data analytics is used to quantitatively analyze and comprehensively evaluate these indicators, and intelligent decision-making algorithms are used to select the most suitable units to be removed or added from numerous candidate units, ensuring that the virtual grid meets the first grid connection condition at the lowest cost during the adjustment process, thereby improving the optimization and adjustment efficiency of the virtual grid.

[0042] In some embodiments, before acquiring the state information of the virtual power grid, the method further includes: collecting and analyzing historical operating data of each photovoltaic power generation unit through big data analytics to construct an initial model of the virtual power grid. The collected data includes, but is not limited to, power generation, ambient temperature, irradiance, and equipment operating parameters. Data cleaning and preprocessing techniques are used to process the raw data, removing noise and outliers. Advanced modeling algorithms, such as neural network algorithms and genetic algorithms, are employed to construct an initial model of the virtual power grid based on the processed data. This model accurately reflects the operating characteristics and patterns of the virtual power grid under different operating conditions. This provides a reliable foundation for subsequently obtaining accurate state information of the virtual power grid and making effective control decisions, improving the accuracy and reliability of the entire control method.

[0043] In some embodiments, after integrating the virtual grid into the power system, the method further includes: acquiring real-time operational data of the power system and the virtual grid; utilizing big data analysis to assess the impact of distributed photovoltaic grid-connected power generation on power system stability and power quality; and adjusting the operating parameters of the virtual grid based on the assessment results. Regarding data acquisition, real-time data transmission is achieved through a high-speed communication network. During the analysis and assessment process, power system stability analysis algorithms and power quality assessment index systems are used to conduct in-depth analysis of the collected data. For example, when voltage fluctuations in the power system after grid connection are found to exceed the allowable range, the operating parameters of each photovoltaic power generation unit in the virtual grid, such as the output power and reactive power compensation device parameters, are adjusted promptly based on the assessment results to ensure stable operation of the power system and meet power quality requirements.

[0044] In some embodiments, the status information of the photovoltaic power generation unit includes at least one of the following: photovoltaic module temperature, photovoltaic module output voltage, photovoltaic module output current, inverter operating status, and remaining power of the energy storage device. To obtain this status information, various types of sensors and monitoring devices are deployed inside the photovoltaic power generation unit. The photovoltaic module temperature is monitored in real time by a high-precision thermistor, which can quickly and accurately detect temperature changes. The inverter operating status is detected by a built-in monitoring module, which can obtain information such as the inverter's operating mode and fault codes in real time. This detailed status information comprehensively reflects the operating status of the photovoltaic power generation unit, providing strong support for precise control and fault diagnosis of the photovoltaic power generation unit.

[0045] In some embodiments, the method further includes: establishing a real-time monitoring and early warning mechanism for the virtual power grid using big data; when the status information of the virtual power grid exceeds a preset threshold, activating the autonomous system of the virtual power grid to select photovoltaic power generation units to be removed or added. The real-time monitoring and early warning mechanism sets multiple threshold levels and corresponding early warning strategies. A reasonable threshold range is determined by learning from historical data through big data analysis. When the status information exceeds the threshold, the autonomous system responds rapidly, quickly selecting suitable photovoltaic power generation units to be removed or added according to preset decision logic and algorithms, achieving rapid adjustment and recovery of the virtual power grid status, effectively avoiding serious faults and accidents caused by abnormal status, and improving the safety and reliability of the virtual power grid operation.

[0046] In some embodiments, after integrating the virtual grid into the power system, the method further includes: updating the setting parameters of the first and second grid connection conditions by analyzing the power generation data after the virtual grid is integrated using big data analytics. During the analysis, statistical data analysis methods and machine learning algorithms are employed to deeply mine the power generation data after grid connection. For example, if it is found that the original first grid connection conditions are no longer applicable in certain situations due to time and environmental changes, the parameters in the first grid connection conditions are adjusted and optimized based on the analysis results to better meet actual operational needs. By continuously updating the grid connection condition parameters, the method can adapt to changes in the operating environment of the power system and the virtual grid, improving the adaptability and effectiveness of the control method.

[0047] In some embodiments, in the step of removing one or more photovoltaic (PV) power generation units from the virtual grid, the PV power generation units are removed from the virtual grid in a manner that maximizes the number of units removed each time; and in the step of adding one or more PV power generation units to the virtual grid from outside the virtual grid, the PV power generation units to be connected to the virtual grid are added to the virtual grid in a manner that minimizes the number of units added each time. The "maximizing the number" approach means that, given a fixed capacity and other conditions, the operation selects the option with the most PV power generation units in each operation; conversely, the "minimizing the number" approach means that, given a fixed capacity and other conditions, the operation selects the option with the fewest PV power generation units in each operation. This operational strategy is based on a comprehensive consideration of the efficiency and stability of the virtual grid adjustment. In the actual adjustment process, the optimal number of units to be removed and added is determined each time through big data analysis and optimization algorithms. When units need to be removed, units with low power generation efficiency, severely aged equipment, or those that have a significant impact on the stability of the virtual grid are prioritized; when units are added, units with good power generation performance and high synergy with existing units are carefully selected. This allows for rapid adjustment of the virtual grid state while minimizing the impact of the adjustment process on the stability of the virtual grid, improving the efficiency and effectiveness of the virtual grid adjustment.

[0048] In some embodiments, during the step of removing one or more photovoltaic (PV) power generation units from the virtual grid, only one PV power generation unit is removed at a time; and during the step of adding one or more PV power generation units to the virtual grid from outside the virtual grid, only one PV power generation unit is added at a time. This step-by-step adjustment method allows for more precise control over the state changes of the virtual grid. After each adjustment, the adjustment effect is accurately evaluated through real-time monitoring and data analysis, and potential problems are promptly identified and corrected. This approach is particularly suitable for scenarios that are highly sensitive to changes in the virtual grid's state, effectively avoiding instability in the virtual grid's operation caused by large-scale adjustments, and improving the controllability and stability of the virtual grid adjustment process.

[0049] In some embodiments, each time a photovoltaic power generation unit is removed from the virtual grid or a photovoltaic power generation unit to be connected to the grid is added to the virtual grid, it is determined whether the virtual grid meets the first grid connection condition. During the determination process, real-time collected virtual grid status information is used to perform rapid calculation and comparison through a preset algorithm and logic judgment program. Once the first grid connection condition is met, the adjustment operation is immediately stopped to avoid unnecessary unit adjustments, improve the timeliness and efficiency of grid connection operations, and reduce the potential damage to the virtual grid and related equipment caused by excessive adjustments.

[0050] In some embodiments, the second grid connection conditions are recalculated each time a photovoltaic (PV) power generation unit is removed from the virtual grid or a PV power generation unit to be connected to the grid is added to the virtual grid. Since each removal or addition of a unit changes the operating state of the virtual grid, recalculating the second grid connection conditions allows for the development of more reasonable and accurate adjustment strategies based on the latest state information. By continuously updating the second grid connection conditions, it is ensured that subsequent unit adjustment operations always proceed in the direction of making the virtual grid meet the first grid connection conditions, thus improving the scientific rigor and accuracy of the virtual grid adjustment process.

[0051] In some embodiments, in the steps of removing one or more photovoltaic (PV) power generation units from the virtual grid and / or adding one or more PV power generation units from outside the virtual grid to the virtual grid, if the number of PV power generation units in the virtual grid meets a predetermined threshold, removing one or more PV power generation units from the virtual grid is prioritized; otherwise, adding one or more PV power generation units from outside the virtual grid to the virtual grid is prioritized. This prioritization strategy is formulated based on the actual operational needs and optimization objectives of the virtual grid. When the number of units is large, removing some units can optimize the structure and operational performance of the virtual grid and reduce the difficulty of internal coordination and management; when the number of units is insufficient, adding grid-connected units can improve the power generation capacity and stability of the virtual grid, ensuring that it meets the grid connection conditions and the needs of the power system.

[0052] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0053] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the embodiments and scope of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A distributed photovoltaic grid-connected power generation control method based on big data, characterized in that, The aforementioned big data-based distributed photovoltaic grid-connected power generation control method includes: The status information of the virtual power grid is obtained. If the status information of the virtual power grid meets the first grid connection condition, the virtual power grid is connected to the power system. The virtual power grid includes one or more photovoltaic power generation units and an autonomous system. The autonomous system includes a data processing chip, a storage device and a communication module for autonomous control of the virtual power grid. If the virtual power grid does not meet the first grid connection condition, the autonomous system generates a second grid connection condition based on the state information; Based on the second grid connection condition, the autonomous system removes one or more photovoltaic power generation units from the virtual grid and / or adds one or more photovoltaic power generation units outside the virtual grid to the virtual grid until the virtual grid meets the third grid connection condition, wherein the third grid connection condition is higher than the first grid connection condition, where higher means that the grid connection condition requirements are higher, so that the internal scheduling of the autonomous system has a margin. When the number of photovoltaic power generation units in the virtual power grid meets a predetermined threshold, one or more photovoltaic power generation units in the virtual power grid are preferentially removed from the virtual power grid. Otherwise, priority will be given to adding one or more photovoltaic power generation units outside the virtual grid to the virtual grid.

2. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 1, characterized in that, After the virtual grid is integrated into the power system, the method further includes: acquiring real-time operating data of the power system and the virtual grid, using big data analysis to evaluate the impact of distributed photovoltaic grid-connected power generation on the stability and power quality of the power system, and adjusting the operating parameters of the virtual grid based on the evaluation results.

3. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 1, characterized in that, The status information of the photovoltaic power generation unit includes at least one of the following: photovoltaic module temperature, photovoltaic module output voltage, photovoltaic module output current, inverter operating status, and remaining power of the energy storage device.

4. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 1, characterized in that, It also includes: using big data to establish a real-time monitoring and early warning mechanism for virtual power grids, and selecting photovoltaic power generation units to be removed or added when the status information of the virtual power grid exceeds a preset threshold.

5. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 1, characterized in that, After the virtual power grid is integrated into the power system, the method further includes: analyzing the power generation data after the virtual power grid is integrated using big data analysis, and updating the setting parameters of the first and second grid connection conditions.

6. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 1, characterized in that, In the step of removing one or more photovoltaic power generation units from the virtual power grid, the photovoltaic power generation units are removed from the virtual power grid in a manner that maximizes the number of units removed each time; and in the step of adding one or more photovoltaic power generation units to be connected to the grid from outside the virtual power grid, the photovoltaic power generation units to be connected to the grid are added to the virtual power grid in a manner that minimizes the number of units added each time.

7. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 1, characterized in that, In the step of removing one or more photovoltaic power generation units from the virtual power grid, only one photovoltaic power generation unit is removed from the virtual power grid at a time. Furthermore, in the step of adding one or more photovoltaic power generation units outside the virtual grid to the virtual grid, only one photovoltaic power generation unit to be connected to the virtual grid is added to the virtual grid at a time.

8. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 7, characterized in that, Each time a photovoltaic power generation unit is removed from the virtual grid or a photovoltaic power generation unit to be connected to the grid is added to the virtual grid, it is determined whether the virtual grid meets the third grid connection condition.

9. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 7, characterized in that, Each time a photovoltaic power generation unit is removed from the virtual grid or a photovoltaic power generation unit to be connected to the grid is added to the virtual grid, the second grid connection conditions are recalculated.

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