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

Abstract

A distributed photovoltaic grid-connected power generation control method based on big data relates to the technical field of big data, and is characterized in that state information of a virtual power grid is acquired, if the state information of the virtual power grid satisfies a first grid-connected condition, the virtual power grid is connected to a power system, and the virtual power grid comprises one or more photovoltaic power generation units; if the virtual power grid does not meet the grid connection condition, generating a second grid connection condition based on the state information; and based on a second grid connection condition, one or more photovoltaic power generation units in the virtual power grid are moved out of the virtual power grid, and / or one or more power generation units to be connected with the grid outside the virtual power grid are added into the virtual power grid until the virtual power grid meets a third grid connection condition. The distributed photovoltaic power generation units are simulated into the centralized photovoltaic power generation system through the virtual networking technology by using the big data technology, so that the reliability and stability of grid connection are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data, and particularly relates to a distributed photovoltaic grid-connected power generation control method based on big data. Background Art

[0002] Big data technology refers to the technology and ability to quickly obtain valuable information from various types of data. It covers multiple links such as data collection, storage, processing, analysis, and visualization.

[0003] In a distributed photovoltaic power generation grid-connected control system, big data technology can collect a large amount of operation data of the distributed photovoltaic power generation system in real time, including power generation, voltage, current, temperature, etc., providing basic data support for the stable operation of the system. By analyzing historical operation data, potential fault hazards can be discovered in advance using big data algorithms, and early warnings and processing can be carried out in a timely manner to reduce system downtime and improve system reliability. The photovoltaic power generation grid-connected control system based on big data can analyze power generation data at different times and under different weather conditions, optimize the operation strategy of the photovoltaic power generation system, such as adjusting the output power of the inverter, optimizing the charge and discharge strategy of the battery energy storage system, etc., to improve the power generation efficiency and economic benefits of the system, integrate relevant data of the distributed photovoltaic power generation system and the power grid, realize the energy management and scheduling of the entire power system, and improve the stability of the power grid and the consumption capacity of renewable energy. In related technologies, the integration of distributed photovoltaic power generation systems still cannot reach an ideal level. In a distributed photovoltaic power generation system, the capacity of a single photovoltaic power generation unit is small, the number is large, and grid-connected operations are frequent. Frequent grid-connected operations threaten the stability of the power system. Summary of the Invention

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

[0005] The present invention provides a distributed photovoltaic grid-connected power generation control method based on big data. The method includes: obtaining the status information of a virtual power grid; if the status information of the virtual power grid meets the first grid-connected condition, then connecting the virtual power grid to the power system, where the virtual power grid includes one or more photovoltaic power generation units; if the virtual power grid does not meet the grid-connected condition, then generating a second grid-connected condition based on the status information; based on the second grid-connected condition, moving one or more photovoltaic power generation units in the virtual power grid out of the virtual power grid, and / or adding one or more to-be-grid-connected power generation units outside the virtual power grid to the virtual power grid until the virtual power grid meets the first grid-connected 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 degree among photovoltaic power generation units.

[0007] In some embodiments, the first grid connection condition includes: the voltage is within the range of ±5% of the rated voltage, the frequency is stable at 50 ± 0.2 Hz, the active power and reactive power meet the access requirements of the power system, and the deviation of the power balance degree among photovoltaic power generation units does not exceed 10%.

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

[0009] In some embodiments, 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 to be grid-connected outside the virtual power grid to the virtual power grid includes: according to the second grid connection condition, evaluating the impact of each photovoltaic power generation unit and power generation unit to be grid-connected on the status of the virtual power grid through big data, and selecting the photovoltaic power generation units to be removed or added.

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

[0011] In some embodiments, after connecting the virtual power grid to the power system, it further includes: obtaining the operation data of the power system and the virtual power grid in real time, using big data to analyze and evaluate the impact of distributed photovoltaic grid-connected power generation on the stability and power quality of the power system, and adjusting the operation parameters of the virtual power grid according to the evaluation results.

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

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

[0014] In some embodiments, after connecting the virtual power grid to the power system, it further includes: analyzing the power generation data after the virtual power grid is grid-connected through big data, 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 within the virtual power grid from the virtual power grid, the photovoltaic power generation units are removed from the virtual power grid in a way that maximizes the quantity each time; and in the step of adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid, the photovoltaic power generation units to be grid-connected are added to the virtual power grid in a way that minimizes the quantity each time.

[0016] In some embodiments, in the step of removing one or more photovoltaic power generation units within the virtual power grid from the virtual power grid, only 1 photovoltaic power generation unit is removed from the virtual power grid each time; and in the step of adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid, only 1 photovoltaic power generation unit to be grid-connected is added to the virtual power grid each time.

[0017] In some embodiments, each time 1 photovoltaic power generation unit is removed from the virtual power grid or 1 photovoltaic power generation unit to be grid-connected is added to the virtual power grid, it is determined whether the virtual power grid meets the first grid connection condition.

[0018] In some embodiments, each time 1 photovoltaic power generation unit is removed from the virtual power grid or 1 photovoltaic power generation unit to be grid-connected is added to the virtual power grid, the second grid connection condition is recalculated.

[0019] In some embodiments, in the step of removing one or more photovoltaic power generation units within the virtual power grid from the virtual power grid, and / or adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid, when the quantity of photovoltaic power generation units in the virtual power grid meets a predetermined threshold, the step of removing one or more photovoltaic power generation units within the virtual power grid from the virtual power grid is preferentially executed; otherwise, the step of adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid is preferentially executed.

[0020] The present 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 networking have an autonomous system and have a certain adjustment margin, improving the reliability and stability of grid connection and reducing the difficulty of grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0023] Figure 3 is a schematic diagram of the autonomous system in some embodiments of the present application.

[0024] Figure 4 It is a flow chart of a distributed photovoltaic grid-connected power generation control method based on big data in some embodiments of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Distributed photovoltaic power generation refers to photovoltaic power generation facilities that are built near the user's site, and the operation mode is characterized by self-generation and self-use on the user side, excess electricity being connected to the grid, and balanced regulation in the distribution system. Compared with centralized photovoltaic power generation, distributed photovoltaic power generation is usually relatively small in scale, can be flexibly constructed according to user needs and site conditions, and has a relatively low initial investment, which is suitable for individuals, enterprises or small groups to invest and build. For example, some families can install a small distributed photovoltaic system on their roofs, and achieve self-sufficient power generation with an investment of tens of thousands of yuan. Its installation is relatively flexible, and does not require large-scale site leveling, infrastructure construction and other work like centralized photovoltaic power generation. In general, small distributed photovoltaic power generation projects can be installed and debugged within a few weeks and put into use quickly. Distributed photovoltaic power generation systems are mostly installed near users, such as factory roofs, commercial building roofs or residential roofs, and the generated electricity can be used directly locally, reducing the loss during power transmission. It is estimated that compared with centralized photovoltaic power generation, distributed photovoltaic power generation can reduce transmission losses by about 10%-20%. Distributed photovoltaic power generation can realize the mode of self-generation and self-use, and the surplus power is connected to the grid. Users can give priority to using the electricity generated by their own photovoltaic power generation system to meet their own electricity needs, and then transmit the surplus electricity to the grid, which improves the efficiency of energy utilization. It can make use of a large number of idle roofs, open spaces and other resources in cities and rural areas without occupying a large area of ​​land, which is especially suitable for development in densely populated areas with tight land resources. In addition, the requirements of distributed photovoltaic power generation systems on natural conditions such as terrain and landforms are relatively low, and they can also be well applied in some complex terrain areas.

[0027] Although the land area occupied by a single distributed photovoltaic power generation project is small, if it is to achieve a power generation scale comparable to that of centralized photovoltaic power generation, the overall land area will be relatively large. Because distributed photovoltaic power generation needs to be installed in multiple different locations, it is difficult to centrally utilize land resources like centralized photovoltaic power generation. A distributed photovoltaic power generation system usually consists of multiple photovoltaic modules with relatively small power. The power generation capacity of a single system generally ranges from a few kW to dozens of MW. Compared with the power generation capacity of centralized photovoltaic power generation, which is often dozens of MW or even hundreds of MW, the power generation capacity of a single distributed photovoltaic power generation project is significantly lower. Due to the relatively dispersed distribution and large number of distributed photovoltaic power generation projects, the difficulty and cost of operation and maintenance management are relatively high. Regular inspections, maintenance, and troubleshooting need to be carried out for each project, consuming a large amount of human and material resources. When a distributed photovoltaic power generation system is connected to the distribution network, it may have a certain impact on the voltage, frequency, power quality, etc. of the power grid. If the access scale is large and the management is improper, it may lead to unstable operation of the power grid, and corresponding upgrades and transformations of the power grid are required to meet the requirements of distributed photovoltaic power generation access.

[0028] The access method, voltage level, etc. of the distributed photovoltaic power generation system need to comply with the access regulations of the power grid. For distributed photovoltaic projects planned to be connected to 10 kV and above, the requirements for collecting, monitoring, and executing the instructions of the power dispatching agency need to be met. At the same time, the system should be equipped with corresponding protection devices and safety automatic devices to ensure the safe operation of the power grid and the photovoltaic system, such as overcurrent protection, overvoltage protection, and leakage protection.

[0029] When grid-connected power generation, the power quality generated needs to comply with relevant standards, including indicators such as voltage deviation, frequency deviation, harmonic content, and three-phase unbalance degree. For example, the voltage deviation should generally be within the range of ±5% of the rated voltage, and the harmonic content should meet the limit values specified by national standards to avoid adverse effects on the power grid and other electrical equipment.

[0030] Figure 1 is a schematic diagram of the power system architecture in some embodiments of the present application; Figure 2 is a schematic diagram of the virtual power grid in some embodiments of the present application; Figure 3 is a schematic diagram of the autonomous system in some embodiments of the present application; Figure 4 is a schematic flowchart of the distributed photovoltaic grid-connected power generation control method based on big data in some embodiments of the present application; As Figures 1-4 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, through data acquisition devices distributed in each photovoltaic power generation unit and the virtual power grid, the status information of the virtual power grid is obtained 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, through the logic judgment module preset in the control algorithm for condition comparison, when all indicators meet the requirements, the virtual power grid will be 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 achieve data interaction and coordinated control through a communication network.

[0033] If the virtual power grid does not meet the grid connection conditions, based on the status information, the powerful computing power and algorithm model of the big data analysis platform are used to generate the second grid connection condition. Specifically, the big data analysis platform will perform processing such as correlation analysis and trend prediction on the collected massive real-time data and historical data.

[0034] Based on the second grid connection condition, one or more photovoltaic power generation units within the virtual power grid are removed from the virtual power grid through the intelligent decision-making module, and / or one or more power generation units to be grid-connected outside the virtual power grid are added to the virtual power grid until the virtual power grid meets the third grid connection condition, and the third grid connection condition is higher than the first grid connection condition. During this process, the intelligent decision-making module will comprehensively consider multiple factors, such as the power generation efficiency of each power generation unit and the health status of the equipment. Through the above operations, the precise control of distributed photovoltaic grid-connected power generation is realized, effectively improving the efficiency of the virtual power grid to meet the grid connection conditions, ensuring the smooth progress of the subsequent grid connection process, and avoiding grid connection failures or impacts on the power system caused by non-compliance. The third grid connection condition is higher than the first grid connection condition, which can leave a margin for the internal scheduling of the autonomous system and improve the stability of the system. In some embodiments, the third grid connection condition is more than 30% higher than the first grid connection condition as a whole. 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 some other grid connection conditions are equal. Therefore, the third grid connection condition being higher than the first grid connection condition does not require all specific conditions to be higher than the first grid connection condition, but some conditions that are more critical for system regulation are higher, such as the capacity information of the virtual power grid. Some conditions can be equal, such as voltage, and some conditions are lower in value for the third grid connection condition, such as the grid connection frequency fluctuation range. Those skilled in the art can understand that a lower numerical value for the frequency fluctuation range is a higher requirement.

[0035] Such as Figures 2-3As shown, the virtual power grid includes an autonomous system, which integrates high-performance data processing chips, large-capacity storage devices, and dedicated communication modules. A specially designed software program is installed in the autonomous system to calculate the state information of the virtual power grid, grid connection conditions, and the connection and disconnection operations of photovoltaic power generation units. During the specific calculation process, advanced numerical calculation algorithms and optimization algorithms are adopted, which can quickly and accurately process complex power system parameters. By setting up this autonomous system, a large amount of distributed photovoltaic grid connection-related data and decision-making tasks that originally needed to be processed by the power system dispatching center can be processed locally by the autonomous system, greatly reducing the pressure on the power system dispatching center, alleviating the data transmission and processing burden of the dispatching center, and avoiding processing delays or system crashes caused by excessive data volume. This autonomous system can be regarded as a micro-dispatching center based on big data technology. The setting of the autonomous system also improves the stability of the power system when dealing with the access of distributed power sources through the autonomous regulation of the virtual power grid, and reduces the adverse effects on the power grid caused by the fluctuations of distributed photovoltaic power generation.

[0036] The hardware devices of the autonomous system can be deployed in traditional power stations, such as hydroelectric power stations, thermal power stations, nuclear power stations, etc. In a hydroelectric power station, with the help of its stable power supply and existing communication network infrastructure, the hardware devices of the autonomous system can be connected to achieve collaborative work with the control system of the hydroelectric power station and share some data resources and computing resources; in a thermal power station, with its mature operation and maintenance system and professional technical personnel, it is convenient to carry out daily maintenance and fault troubleshooting of the hardware devices of the autonomous system. A centralized photovoltaic power generation station can also be selected. The centralized photovoltaic power generation station has a large site space and perfect power facilities, which can provide a good operating environment for the hardware devices of the autonomous system, and rich photovoltaic power generation data can be directly obtained for analysis and decision-making. Of course, distributed photovoltaic power generation stations with larger capacity and better hardware conditions can also be selected. These distributed power stations are often closer to the load center. Setting the autonomous system here can respond more quickly to local power demand changes and improve the real-time performance and effectiveness of virtual power grid control.

[0037] The present invention uses big data technology to simulate distributed photovoltaic power generation units as a centralized photovoltaic power generation system through virtual networking technology. The third grid connection condition is higher than the first grid connection condition, so that the combined virtual power grid has a certain adjustment margin. The autonomous system can share the computing power pressure of the dispatching center. The multiple photovoltaic power generation units through virtual networking have an autonomous system and set an adjustment margin, improving the reliability and stability of grid connection and reducing the difficulty of grid connection.

[0038] 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 degree among photovoltaic power generation units. To obtain this status information, high-precision measuring devices are installed at each node of the virtual power grid and the output terminals of photovoltaic power generation units. Taking voltage measurement as an example, a high-precision voltage transformer is used, and its measurement error can be controlled within ±0.5%. For the calculation of the power balance degree, the output power data of each photovoltaic power generation unit are collected in real time, and a specific power balance degree algorithm is used for calculation. These detailed methods for obtaining and calculating status information provide a rich and reliable data basis for accurately judging 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: the voltage is within the range of ±5% of the rated voltage, the frequency is stable at 50 ± 0.2 Hz, the active power and reactive power meet the access requirements of the power system, and the deviation of the power balance degree among photovoltaic power generation units does not exceed 10%. These first grid connection conditions are determined after a large number of theoretical studies, simulation experiments, and verification with actual operation data. In practical applications, when the parameters of the virtual power grid meet these conditions, it can ensure a safe and stable grid connection between the virtual power grid and the power system, reduce the impact on key indicators such as the voltage and frequency of the power system during grid connection, and ensure the stable operation of the power system and power quality.

[0040] In some embodiments, generating the second grid connection conditions based on the status information includes: using big data analysis to analyze the differences between the status information and the first grid connection conditions, and combining historical grid connection data and prediction models to determine the second grid connection conditions. During the big data analysis process, data mining algorithms are used to extract key factors and patterns affecting grid connection conditions from a large amount of historical grid connection data, and machine learning algorithms are used to train and optimize the prediction models so that they can accurately predict future operation trends based on the current status information. For example, when it is found that the voltage of the virtual power grid is low, by analyzing successful grid connection cases in similar situations in historical data and combining the prediction of the voltage change trend by the prediction model, the adjustment strategies and parameter requirements for photovoltaic power generation units in the second grid connection conditions are determined, thereby providing a scientific basis for subsequent adjustment operations.

[0041] In some embodiments, 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 to be grid-connected outside the virtual power grid to the virtual power grid includes: according to the second grid connection condition, evaluating the impact of each photovoltaic power generation unit and power generation unit to be grid-connected on the state of the virtual power grid through big data, and selecting the photovoltaic power generation units to be removed or added. During the evaluation process, a multi-dimensional evaluation index system is constructed, covering factors such as the power generation efficiency of the power generation unit, the remaining life of the equipment, the geographical location, and the coordination with other units. Using big data analysis technology to quantitatively analyze and comprehensively evaluate these indicators, and screening out the most suitable units to be removed or added from numerous candidate units through intelligent decision-making algorithms, ensuring that the virtual power grid can meet the first grid connection condition at the lowest cost during the adjustment process and improving the optimization and adjustment efficiency of the virtual power grid.

[0042] In some embodiments, before obtaining the state information of the virtual power grid, it further includes: collecting and analyzing the historical operation data of each photovoltaic power generation unit through big data to construct an initial model of the virtual power grid. The collected data includes but is not limited to power generation power, environmental temperature, light intensity, equipment operation parameters, etc. Using data cleaning and preprocessing technologies to process the original data, removing noise and abnormal data. Adopting advanced modeling algorithms, such as neural network algorithms, genetic algorithms, etc., to construct an initial model of the virtual power grid according to the processed data, which can accurately reflect the operation characteristics and laws of the virtual power grid under different working conditions. It provides a reliable basis for accurately obtaining the state information of the virtual power grid and making effective control decisions subsequently, and improves the accuracy and reliability of the entire control method.

[0043] In some embodiments, after connecting the virtual power grid to the power system, it further includes: obtaining the operation data of the power system and the virtual power grid in real time, 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 operation parameters of the virtual power grid according to the evaluation results. In terms of data acquisition, real-time data transmission is achieved through a high-speed communication network; during the analysis and evaluation process, power system stability analysis algorithms and power quality evaluation index systems are used to deeply analyze the collected data. For example, when it is found that the voltage fluctuation of the power system exceeds the allowable range after grid connection, the operation parameters such as the output power of each photovoltaic power generation unit in the virtual power grid and the parameters of the reactive power compensation device are adjusted in a timely manner according to the evaluation results to ensure the stable operation of the power system and the power quality meets the requirements.

[0044] In some embodiments, the status information of the photovoltaic power generation unit includes at least one of the photovoltaic module temperature, the photovoltaic module output voltage, the photovoltaic module output current, the inverter operating status, and the 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 temperature sensor. The temperature sensor uses a high-precision thermistor and can quickly and accurately sense temperature changes. The inverter operating status is detected by a built-in monitoring module, and information such as the working mode and fault code of the inverter can be obtained in real time. These detailed status information can comprehensively reflect the operating conditions of the photovoltaic power generation unit and provide strong support for the 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, the autonomous system of the virtual power grid is activated to select the photovoltaic power generation units to be removed or added. In the real-time monitoring and early warning mechanism, multiple levels of thresholds and corresponding early warning strategies are set. Through big data analysis, historical data is learned to determine a reasonable threshold range. When the status information exceeds the threshold, the autonomous system will respond quickly and, according to the preset decision logic and algorithms, quickly select the appropriate photovoltaic power generation units to be removed or added, realizing the rapid adjustment and restoration 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 the virtual power grid is incorporated into the power system, it further includes: updating the set parameters of the first grid connection condition and the second grid connection condition by analyzing the power generation data of the virtual power grid after grid connection using big data. During the analysis process, data statistical analysis methods and machine learning algorithms are used to deeply mine the power generation data after grid connection. For example, when it is found that the original first grid connection condition is no longer applicable in some cases over time and with environmental changes, the parameters in the first grid connection condition are adjusted and optimized according to the analysis results to make it more in line with the actual operation requirements. By continuously updating the grid connection condition parameters, the changes in the power system and the virtual power grid operating environment can be adapted, and the adaptability and effectiveness of the control method are improved.

[0047] In some embodiments, in the step of removing one or more photovoltaic power generation units from the virtual power grid, each time the photovoltaic power generation units are removed from the virtual power grid in a manner that maximizes the quantity; and in the step of adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid, each time the photovoltaic power generation units to be grid-connected are added to the virtual power grid in a manner that minimizes the quantity. The so-called manner of maximizing the quantity means that under the condition that conditions such as capacity are determined, the scheme with the largest number of photovoltaic power generation units is selected for each operation; conversely, the manner of minimizing the quantity means that under the condition that conditions such as capacity are determined, the scheme with the smallest number of photovoltaic power generation units is selected for each operation. This operation strategy is based on a comprehensive consideration of the adjustment efficiency and stability of the virtual power grid. During the actual adjustment process, through big data analysis and optimization algorithms, the optimal number of units to be removed and added each time is determined. When units need to be removed, the units with low power generation efficiency, serious equipment aging, or a greater impact on the stability of the virtual power grid are preferentially selected; when adding units, the photovoltaic power generation units to be grid-connected with good power generation performance and high synergy with existing units are carefully selected. In this way, while quickly adjusting the state of the virtual power grid, the impact of the adjustment process on the stability of the virtual power grid can be minimized, and the efficiency and effect of the virtual power grid adjustment are improved.

[0048] In some embodiments, in the step of removing one or more photovoltaic power generation units from the virtual power grid, each time only 1 photovoltaic power generation unit is removed from the virtual power grid; and in the step of adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid, each time only 1 photovoltaic power generation unit to be grid-connected is added to the virtual power grid. This step-by-step adjustment method can more finely control the state change of the virtual power grid. After each adjustment, through real-time monitoring and data analysis, the adjustment effect is accurately evaluated, and possible problems are promptly discovered and corrected. It is particularly suitable for scenarios that are more sensitive to the state change of the virtual power grid, can effectively avoid the unstable operation of the virtual power grid caused by large-scale adjustment, and improves the controllability and stability of the virtual power grid adjustment process.

[0049] In some embodiments, each time 1 photovoltaic power generation unit is removed from the virtual power grid or 1 photovoltaic power generation unit to be grid-connected is added to the virtual power grid, it is judged whether the virtual power grid meets the first grid connection condition. During the judgment process, using the real-time collected virtual power grid state information, rapid calculation and comparison are carried out through a preset algorithm and logical judgment program. Once the first grid connection condition is met, the adjustment operation is immediately stopped, unnecessary unit adjustments are avoided, the timeliness and efficiency of the grid connection operation are improved, and at the same time, the potential damage to the virtual power grid and related equipment caused by excessive adjustment is reduced.

[0050] In some embodiments, every time one photovoltaic power generation unit is removed from the virtual power grid or one photovoltaic power generation unit to be grid-connected joins the virtual power grid, the second grid-connection condition is recalculated. Since the removal or addition of each unit will change the operating state of the virtual power grid, recalculating the second grid-connection condition can formulate a more reasonable and accurate adjustment strategy based on the latest state information. By continuously updating the second grid-connection condition, it is ensured that subsequent unit adjustment operations always move in the direction of making the virtual power grid meet the first grid-connection condition, improving the scientificity and accuracy of the virtual power grid adjustment process.

[0051] In some embodiments, in the step of removing one or more photovoltaic power generation units within the virtual power grid from the virtual power grid and / or adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid, when the number of photovoltaic power generation units in the virtual power grid meets a predetermined threshold, the operation of removing one or more photovoltaic power generation units within the virtual power grid from the virtual power grid is preferentially executed; otherwise, the operation of adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid is preferentially executed. This priority strategy is formulated according to the actual operation requirements and optimization objectives of the virtual power grid. When the number of units is large, the structure and operating performance of the virtual power grid can be optimized by removing some units, reducing 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 power grid, ensuring that it meets the grid-connection conditions and the requirements of the power system.

[0052] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

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

Claims

1. A distributed photovoltaic grid-connected power generation control method based on big data, characterized in that: Obtain the status information of the virtual power grid. If the status information of the virtual power grid meets the first grid-connection condition, then connect the virtual power grid to the power system. The virtual power grid includes one or more photovoltaic power generation units and an autonomous system; If the virtual power grid does not meet the grid-connection condition, then the autonomous system generates a second grid-connection condition based on the status information; Based on the second grid-connection condition, the autonomous system removes one or more photovoltaic power generation units within the virtual power grid from the virtual power grid, and / or adds one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid until the virtual power grid meets the third grid-connection condition, where the third grid-connection condition is higher than the first grid-connection condition.

2. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 1, characterized in that After connecting the virtual power grid to the power system, it further includes: obtaining the operation data of the power system and the virtual power grid in real time, 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 operation parameters of the virtual power grid according to 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 temperature of the photovoltaic module, the output voltage of the photovoltaic module, the output current of the photovoltaic module, the operating status of the inverter, and the 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 further includes: establishing a real-time monitoring and early warning mechanism for the virtual power grid using big data, and selecting the photovoltaic power generation units to be removed or added when the status information of the virtual power grid exceeds the preset threshold.

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

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

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 within the virtual power grid from the virtual power grid, each time only 1 photovoltaic power generation unit is removed from the virtual power grid; and in the step of adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid, each time only 1 photovoltaic power generation unit to be grid-connected is added to the virtual power grid.

8. The distributed photovoltaic grid-connected power generation control method based on big data according to claim 7, characterized in that Each time 1 photovoltaic power generation unit is removed from the virtual power grid or 1 photovoltaic power generation unit to be grid-connected is added to the virtual power grid, determine whether the virtual power grid meets the first 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 1 photovoltaic power generation unit is removed from the virtual power grid or 1 photovoltaic power generation unit to be grid-connected is added to the virtual power grid, recalculate the second grid-connection condition.

10. 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 within the virtual power grid from the virtual power grid, and / or adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid, when the number of photovoltaic power generation units in the virtual power grid meets a predetermined threshold, the operation of removing one or more photovoltaic power generation units within the virtual power grid from the virtual power grid is preferentially executed; otherwise, the operation of adding one or more photovoltaic power generation units to be grid-connected outside the virtual power grid to the virtual power grid is preferentially executed.

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