Distributed photovoltaic grid-connected power fluctuation suppression control method

Abstract

The invention discloses a distributed photovoltaic grid-connected power fluctuation suppression control method, and relates to the technical field of electric power control, and the method comprises the steps: connecting a distributed photovoltaic management and control end, and receiving a photovoltaic grid-connected scheme; performing grid-connected power fluctuation prediction to obtain a grid-connected abnormal photovoltaic unit and a grid-connected abnormal fluctuation prediction result; performing grid-connected abnormal wave and risk prediction, and determining a grid-connected abnormal wave and photovoltaic unit and an abnormal wave and risk prediction result; and on the basis of the prediction result, grid-connected power fluctuation suppression optimization and grid-connected abnormal wave and risk optimization are carried out, an optimized photovoltaic grid-connected scheme is obtained, and photovoltaic grid connection is carried out. The technical problems that in the prior art, a traditional grid-connected control method cannot respond to a complex dynamic environment in real time, and abnormal fluctuation is difficult to effectively predict and restrain are solved, the abnormal fluctuation situation is recognized and processed in time by establishing an accurate power fluctuation prediction model and a risk assessment mechanism, and the reliability of the grid-connected control system is improved. And safe and efficient operation of the photovoltaic system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power control, and in particular to a distributed photovoltaic grid-connected power fluctuation suppression control method. Background Art

[0002] As the global demand for sustainable development and clean energy increases, the application of distributed photovoltaic systems is becoming more and more widespread. The photovoltaic power generation system consists of multiple grid-connected nodes and photovoltaic units, which can effectively convert solar energy into electricity and directly connect to the power grid to support local and regional energy supply. However, the characteristics of photovoltaic power generation cause its power output to be affected by multiple factors such as weather changes and light intensity, resulting in frequent power fluctuations.

[0003] These power fluctuations not only affect the stability of the power system, but may also cause grid-connected safety risks, so effective fluctuation suppression control methods are needed to meet this challenge. However, traditional grid-connected control methods often cannot respond to complex dynamic environments in real time, and it is difficult to effectively predict and suppress abnormal fluctuations. Summary of the invention

[0004] The present application provides a distributed photovoltaic grid-connected power fluctuation suppression control method for solving the technical problems in the prior art that the traditional grid-connected control method cannot respond to complex dynamic environments in real time and is difficult to effectively predict and suppress abnormal fluctuations.

[0005] The present application provides a distributed photovoltaic grid-connected power fluctuation suppression control method, the method comprising: connecting to a distributed photovoltaic control terminal, receiving a photovoltaic grid-connected scheme of a distributed photovoltaic system, wherein the distributed photovoltaic system comprises a plurality of photovoltaic units corresponding to a plurality of grid-connected nodes; based on a grid-connected power fluctuation depth threshold, predicting the grid-connected power fluctuation of the distributed photovoltaic system according to the photovoltaic grid-connected scheme, obtaining grid-connected abnormal photovoltaic units and grid-connected abnormal fluctuation prediction results; based on a grid-connected abnormal wave and risk constraint condition, predicting the grid-connected abnormal wave and risk of the distributed photovoltaic system according to the grid-connected abnormal photovoltaic units and the grid-connected abnormal fluctuation prediction results, and determining The grid-connected abnormal photovoltaic unit and the abnormal wave risk prediction results; based on the grid-connected abnormal photovoltaic unit and the grid-connected abnormal fluctuation prediction results, the photovoltaic grid-connected scheme is optimized for grid-connected power fluctuation suppression according to the first grid-connected fluctuation suppression decision module and the grid-connected power fluctuation depth threshold to obtain a first optimized photovoltaic grid-connected scheme; based on the grid-connected abnormal photovoltaic unit and the abnormal wave risk prediction results, the first optimized photovoltaic grid-connected scheme is optimized for grid-connected abnormal wave risk according to the second grid-connected fluctuation suppression decision module to obtain a second optimized photovoltaic grid-connected scheme; the distributed photovoltaic system is connected to the photovoltaic grid according to the second optimized photovoltaic grid-connected scheme.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The distributed photovoltaic grid-connected power fluctuation suppression control method provided by the present application relates to the field of power control technology. The photovoltaic grid-connected scheme is received by connecting to the photovoltaic control end, and then fluctuation prediction is performed based on the grid-connected power fluctuation depth threshold to identify abnormal photovoltaic units and fluctuations. Risk prediction is performed based on the abnormal impact risk, and abnormal photovoltaic units are determined. Based on these prediction results, the grid-connected scheme is optimized through the decision-making module, and finally an optimized photovoltaic grid-connected scheme is generated to achieve safe and stable photovoltaic grid connection. The technical problem that the traditional grid-connected control method in the prior art cannot respond to complex dynamic environments in real time and is difficult to effectively predict and suppress abnormal fluctuations is solved, and the technical effect of timely identifying and handling abnormal fluctuations and ensuring the safe and efficient operation of the photovoltaic system is achieved by establishing an accurate power fluctuation prediction model and risk assessment mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic diagram of a distributed photovoltaic grid-connected power fluctuation suppression control method provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for obtaining a first optimized photovoltaic grid-connected scheme in the distributed photovoltaic grid-connected power fluctuation suppression control method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The present application provides a distributed photovoltaic grid-connected power fluctuation suppression control method for solving the technical problems in the prior art that the traditional grid-connected control method cannot respond to complex dynamic environments in real time and is difficult to effectively predict and suppress abnormal fluctuations.

[0010] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0011] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0012] Embodiment 1, as Figure 1 As shown, the present application provides a distributed photovoltaic grid-connected power fluctuation suppression control method, the method comprising: P10: Connecting to the distributed photovoltaic control terminal, receiving the photovoltaic grid-connected solution of the distributed photovoltaic system, wherein the distributed photovoltaic system includes multiple photovoltaic units corresponding to multiple grid-connected nodes.

[0013] Specifically, first establish a connection with the distributed photovoltaic control terminal to receive and manage the photovoltaic grid-connected plan of the distributed photovoltaic system. This plan is a blueprint for the operation of the system, and details how to safely and efficiently connect the electricity generated by each photovoltaic unit to the grid. It is worth noting that the photovoltaic grid-connected plan not only includes macro grid-connected strategies, but also specific photovoltaic grid-connected decisions to be executed for each photovoltaic unit. These decisions may involve multiple aspects such as grid-connected time, grid-connected power, and grid-connected method, aiming to ensure that each photovoltaic unit can participate in the grid connection in the best state.

[0014] The distributed photovoltaic system is composed of multiple photovoltaic units corresponding to multiple grid-connected nodes. Each photovoltaic unit is an independent power generation unit, which together constitute the power generation capacity of the entire system. These photovoltaic units may be distributed in different geographical locations and connected to the power grid through grid-connected nodes. Therefore, when receiving a photovoltaic grid-connected solution, it is necessary to ensure that the solution can fully cover all photovoltaic units and accurately reflect the grid-connected requirements and conditions of each unit.

[0015] In order to ensure the stability of the connection and the accuracy of the data, the system usually adopts advanced communication protocols such as MQTT or TCP / IP to ensure efficient and low-latency data transmission. In addition, the system may also be equipped with monitoring and control modules to respond to the operating status and environmental changes of each photovoltaic unit in a timely manner, thereby adjusting the grid-connected decision.

[0016] In the process of receiving the photovoltaic grid-connected solution, the system needs to not only take into account the individual characteristics of each photovoltaic unit, but also analyze the interrelationships and synergies between them. This systematic thinking is essential for formulating an overall grid-connected strategy, which helps optimize the operating efficiency of the photovoltaic system and minimize the potential risk of power fluctuations.

[0017] P20: Based on the grid-connected power fluctuation depth threshold, the distributed photovoltaic system is predicted to have grid-connected power fluctuations according to the photovoltaic grid-connected scheme, and the prediction results of grid-connected abnormal photovoltaic units and grid-connected abnormal fluctuations are obtained.

[0018] Furthermore, step P20 of the embodiment of the present application further includes: P21: Modeling the distributed photovoltaic system to obtain a distributed photovoltaic model; P22: Based on the distributed photovoltaic model, the grid-connected power fluctuations of the multiple photovoltaic units are predicted according to the photovoltaic grid-connected scheme to obtain multiple grid-connected fluctuation prediction results; P23: Power fluctuation depth is evaluated according to the multiple grid-connected fluctuation prediction results to obtain multiple grid-connected power fluctuation depth coefficients; P24: Determine whether the multiple grid-connected power fluctuation depth coefficients are greater than or equal to the grid-connected power fluctuation depth threshold, and generate an abnormal grid-connected power fluctuation depth coefficient; P25: Based on the abnormal grid-connected power fluctuation depth coefficient, the multiple photovoltaic units and the multiple grid-connected fluctuation prediction results are mapped to generate the grid-connected abnormal photovoltaic units and the grid-connected abnormal fluctuation prediction results.

[0019] It should be understood that, according to the set grid-connected power fluctuation depth threshold, power fluctuation prediction is performed on the distributed photovoltaic system to identify abnormal grid-connected photovoltaic units and their fluctuation prediction results.

[0020] First, a distributed photovoltaic system is modeled to obtain a comprehensive distributed photovoltaic model. This model is usually based on the characteristics of the photovoltaic unit, environmental factors (such as light, temperature, etc.) and operating data, and can reflect the dynamic behavior and performance characteristics of the system. The use of appropriate modeling methods (such as machine learning algorithms or physical models) can effectively capture the response characteristics of the photovoltaic unit and provide an accurate basis for subsequent fluctuation predictions.

[0021] Next, the constructed distributed photovoltaic model is used to predict the grid-connected power fluctuation of each photovoltaic unit according to the photovoltaic grid-connected scheme. This stage involves using prediction algorithms (such as time series analysis or neural networks) to process the historical data and current status of each photovoltaic unit to generate multiple grid-connected fluctuation prediction results. The results can reflect the power output changes of each photovoltaic unit under different conditions.

[0022] Furthermore, the power fluctuation depth is evaluated for multiple grid-connected fluctuation prediction results, and multiple grid-connected power fluctuation depth coefficients are calculated. These coefficients represent the degree of fluctuation that each photovoltaic unit may encounter during the expected grid-connected process, and need to be compared with the set grid-connected power fluctuation depth threshold.

[0023] Next, it is determined whether these fluctuation depth coefficients are greater than or equal to the set threshold, thereby generating abnormal grid-connected power fluctuation depth coefficients. The purpose of this step is to screen out those photovoltaic units that may pose a threat to system stability.

[0024] Finally, based on the identified abnormal grid-connected power fluctuation depth coefficient, the corresponding photovoltaic units and prediction results are mapped. Specifically, the photovoltaic units corresponding to the abnormal depth coefficient are marked as grid-connected abnormal photovoltaic units, and the corresponding fluctuation prediction results are recorded as grid-connected abnormal fluctuation prediction results. Through this mapping, photovoltaic units that may cause problems in the grid-connected process can be effectively identified and located, providing a basis for subsequent risk management and control strategies.

[0025] Furthermore, step P23 of the embodiment of the present application also includes: P23-1: Based on the photovoltaic grid-connected scheme, the expected grid-connected power of the multiple photovoltaic units is fitted to generate multiple photovoltaic grid-connected power expected curves; P23-2: Based on the photovoltaic grid-connected scheme, the grid-connected power of the multiple photovoltaic units is predicted according to the distributed photovoltaic model to generate multiple photovoltaic grid-connected power prediction curves; P23-3: Based on the multiple photovoltaic grid-connected power expected curves, the grid power fluctuations of the multiple photovoltaic grid-connected power prediction curves are identified respectively to generate the multiple grid-connected fluctuation prediction results.

[0026] Optionally, the specific process of performing a power fluctuation depth evaluation includes: fitting expected power, predicting actual power, and identifying fluctuations. First, the expected grid-connected power of multiple photovoltaic units is fitted based on the photovoltaic grid-connected scheme. The grid-connected power expectation here refers to the power output that the photovoltaic unit should achieve under ideal conditions. The fitting process can use data fitting technology (such as polynomial fitting or curve fitting) to generate multiple photovoltaic grid-connected power expectation curves, which represent the changes in the grid-connected power that each photovoltaic unit should generate over time under ideal conditions.

[0027] Next, the constructed distributed photovoltaic model is used to predict the grid-connected power of multiple photovoltaic units according to the photovoltaic grid-connected scheme. The actual possible grid-connected power curve, i.e., the photovoltaic grid-connected power prediction curve, is generated. At this point, the generated multiple photovoltaic grid-connected power prediction curves reflect the power output of the photovoltaic unit under actual operating conditions. The prediction process may use real-time data and model algorithms, such as regression analysis or machine learning technology, to ensure the accuracy and timeliness of the prediction.

[0028] Finally, based on the obtained multiple photovoltaic grid-connected power expectation curves, the photovoltaic grid-connected power prediction curves are respectively subjected to fluctuation identification. The fluctuation identification here involves comparing the difference between the expected power and the actual predicted power, judging the fluctuation of the power output by setting a threshold or using statistical methods (such as standard deviation calculation), smoothing the prediction curves and detecting outliers, thereby generating multiple grid-connected fluctuation prediction results. These results reflect the power fluctuations that may occur in each photovoltaic unit during the grid-connected process, providing an important basis for subsequent control and optimization.

[0029] Through the close combination of the above steps, the expected and actual performance of grid-connected power can be fully evaluated, and potential power fluctuations can be identified, thereby providing data support for subsequent risk management and optimization decisions. This process not only improves the accuracy of grid-connected control, but also enhances the overall stability of the photovoltaic system.

[0030] P30: Based on the grid-connected abnormal wave risk constraint conditions, the distributed photovoltaic system is predicted for the grid-connected abnormal wave risk according to the grid-connected abnormal photovoltaic unit and the grid-connected abnormal fluctuation prediction result, and the grid-connected abnormal wave photovoltaic unit and the abnormal wave risk prediction result are determined.

[0031] Furthermore, step P30 of the embodiment of the present application further includes: P31: Based on the grid-connected abnormal photovoltaic unit, the characteristics of the distributed photovoltaic system to be affected are identified to determine multiple photovoltaic units to be affected; P32: Based on the distributed photovoltaic model, the grid-connected abnormal propagation prediction is performed on the multiple photovoltaic units to be affected according to the grid-connected abnormal fluctuation prediction result, and multiple grid-connected abnormal propagation prediction results are obtained; P33: Based on the multiple grid-connected abnormal propagation prediction results, a grid-connected abnormal propagation risk assessment is performed to obtain multiple grid-connected abnormal propagation risk coefficients; P34: Determine whether the multiple grid-connected abnormal propagation risk coefficients meet the grid-connected abnormal propagation risk constraint conditions; P35: If any of the multiple grid-connected abnormal propagation risk coefficients does not meet the grid-connected abnormal propagation risk constraint conditions, generate an identified grid-connected abnormal propagation risk coefficient; P36: Based on the identified grid-connected abnormal propagation risk coefficient, the multiple photovoltaic units to be affected and the multiple grid-connected abnormal propagation prediction results are mapped to obtain the grid-connected abnormal propagation photovoltaic units and the abnormal propagation risk prediction results.

[0032] It should be understood that based on the grid-connected abnormality risk constraint conditions, the grid-connected abnormality photovoltaic units and grid-connected abnormal fluctuation prediction results are used to predict the grid-connected abnormality risk of the distributed photovoltaic system, thereby identifying the photovoltaic units that may be affected and their risk levels.

[0033] First, based on the identified abnormal grid-connected photovoltaic units, the distributed photovoltaic system is identified for the characteristics of the affected photovoltaic units. The affected photovoltaic units refer to photovoltaic units that are not marked as abnormal grid-connected. Through this identification, all photovoltaic units in the system except the abnormal grid-connected photovoltaic units are regarded as affected photovoltaic units. These units may be affected by the abnormal grid-connected photovoltaic units in the future and need special attention.

[0034] Next, based on the distributed photovoltaic model, the grid-connected abnormal fluctuation prediction results are used to predict the grid-connected abnormal propagation of multiple photovoltaic units to be affected, that is, to simulate the propagation process of the grid-connected abnormality in the system to evaluate its possible impact on other photovoltaic units. This prediction process can use simulation technology and mathematical models, combined with the propagation mechanism of abnormal fluctuations, to evaluate how the performance of these photovoltaic units to be affected changes when an abnormality occurs. Through this analysis, multiple grid-connected abnormal propagation prediction results are generated to show the risk level of the photovoltaic units to be affected.

[0035] Furthermore, based on the obtained multiple grid-connected abnormal propagation prediction results, a grid-connected abnormality risk assessment is performed, and multiple grid-connected abnormality risk coefficients are calculated. Exemplarily, a weighted scoring method, decision tree, Bayesian network, etc. can be used to establish a suitable risk assessment model, which needs to consider various influencing factors, including the amplitude, frequency, duration and potential impact range of fluctuations, and analyze each input multiple grid-connected abnormality propagation prediction results according to the risk assessment model, and calculate the corresponding grid-connected abnormality risk coefficient. These risk coefficients quantify the degree of impact that each photovoltaic unit to be affected may suffer under abnormal conditions, and provide a specific assessment of the risk.

[0036] Then, it is determined whether these grid-connected abnormal wave risk coefficients meet the preset grid-connected abnormal wave risk constraint conditions. Among them, the grid-connected abnormal wave risk constraint conditions can be based on historical data, industry standards or specific operating requirements, aiming to ensure that the system can still operate safely under abnormal conditions. If it is found that any grid-connected abnormal wave risk coefficient does not meet the risk constraint conditions, an identified grid-connected abnormal wave risk coefficient is generated. These identified risk coefficients indicate that there are potential risks in the grid-connected operation of the relevant photovoltaic units, and immediate measures need to be taken to manage them.

[0037] Finally, based on the identified grid-connected abnormality risk coefficient, multiple photovoltaic units to be affected and grid-connected abnormality propagation prediction results are mapped, that is, the identified risk coefficient is mapped to the specific photovoltaic unit to be affected, and the grid-connected abnormality photovoltaic unit and its abnormality risk prediction result are obtained.

[0038] Through the above steps, the risks of grid-connected anomalies can be fully assessed, and the affected photovoltaic units can be identified, providing strong support for subsequent risk control and optimization decisions. This process not only enhances the system's adaptive capabilities, but also improves the overall stability and safety of the photovoltaic system.

[0039] P40: Based on the grid-connected abnormal photovoltaic unit and the grid-connected abnormal fluctuation prediction result, the photovoltaic grid-connected scheme is optimized for grid-connected power fluctuation suppression according to the first grid-connected fluctuation suppression decision module and the grid-connected power fluctuation depth threshold to obtain a first optimized photovoltaic grid-connected scheme.

[0040] Further, such as Figure 2 As shown, step P40 of the embodiment of the present application also includes: P41: Based on the grid-connected abnormal photovoltaic unit, the photovoltaic grid-connected scheme is identified in the grid-connected decision to obtain a first identified grid-connected decision; P42: The grid-connected abnormal fluctuation prediction result and the first identified grid-connected decision are input into the first grid-connected fluctuation suppression decision module to obtain a first optimized grid-connected decision; P43: Based on the grid-connected power fluctuation depth threshold, the first optimized grid-connected decision is verified in the grid-connected suppression to obtain a first grid-connected suppression verification result; P44: When the first grid-connected suppression verification result is passed, the photovoltaic grid-connected scheme is optimized according to the first optimized grid-connected decision to generate the first optimized photovoltaic grid-connected scheme.

[0041] Optionally, for the identified abnormal grid-connected photovoltaic units and the abnormal grid-connected fluctuation prediction results, the photovoltaic grid-connected scheme is optimized using the first grid-connected fluctuation suppression decision module to obtain a first optimized photovoltaic grid-connected scheme.

[0042] First, based on the abnormal photovoltaic units connected to the grid, the photovoltaic grid-connected scheme is identified for grid-connected decisions, and the specific grid-connected decisions corresponding to the abnormal photovoltaic units connected to the grid in the current photovoltaic grid-connected scheme are clarified to generate the first identified grid-connected decisions. These decisions may include adjusting power output, changing grid-connected time, etc., providing basic information for subsequent optimization.

[0043] Next, the grid-connected abnormal fluctuation prediction results and the first identified grid-connected decision are input into the first grid-connected fluctuation suppression decision module. This module can be an intelligent algorithm model, which may use machine learning or optimization algorithms, and aims to analyze and determine the best fluctuation suppression strategy based on the input data. In this process, the model will evaluate the current photovoltaic grid-connected solution, consider the impact of abnormal fluctuations, and finally output the first optimized grid-connected decision.

[0044] Then, based on the grid-connected power fluctuation depth threshold, the first optimized grid-connected decision is subjected to grid-connected suppression verification to confirm whether the optimized decision meets the set power fluctuation depth threshold, thereby ensuring that no excessive fluctuations will occur during the grid-connected operation. The verification result may be "pass" or "fail".

[0045] Finally, if the result of the first grid-connection suppression check is "passed", the photovoltaic grid-connection plan is optimized according to the first optimized grid-connection decision to generate the first optimized photovoltaic grid-connection plan. This plan not only takes into account the impact of abnormal photovoltaic units connected to the grid, but also ensures that power fluctuations are minimized to the greatest extent in actual operation, thereby improving the stability and efficiency of the system.

[0046] Through the consistent implementation of these steps, the system can effectively identify and handle grid-connected anomalies and improve the reliability of the photovoltaic system. This optimization process not only helps maintain the stability of the power grid, but also provides strong technical support for the efficient operation of the photovoltaic system.

[0047] Furthermore, step P43 of the embodiment of the present application also includes: P43-1: Based on the grid-connected abnormal photovoltaic unit, the grid-connected power fluctuation depth is predicted according to the first optimized grid-connected decision, and the first decision grid-connected fluctuation depth coefficient is obtained; P43-2: Determine whether the first decision grid-connected fluctuation depth coefficient is less than the grid-connected power fluctuation depth threshold; P43-3: If the first decision grid-connected fluctuation depth coefficient is less than the grid-connected power fluctuation depth threshold, the output result of the first grid-connected suppression verification is passed; P43-4: If the first decision grid-connected fluctuation depth coefficient is greater than or equal to the grid-connected power fluctuation depth threshold, the output result of the first grid-connected suppression verification is failed.

[0048] Specifically, the grid-connected suppression verification process is further refined to ensure the effectiveness of the first optimized grid-connected decision. Based on the abnormal grid-connected photovoltaic units, the first optimized grid-connected decision is used to predict the grid-connected power fluctuation depth. Advanced simulation technology and mathematical models can be used to analyze the dynamic response of the system in detail to predict the fluctuation of the grid-connected power. Through simulation and calculation, the first decision grid-connected fluctuation depth coefficient is obtained. This coefficient reflects the power fluctuation depth after the implementation of the optimization decision and can provide a preliminary assessment of the system stability.

[0049] Next, it is determined whether the obtained first decision grid-connected fluctuation depth coefficient is less than the set grid-connected power fluctuation depth threshold. This threshold is set according to the system stability and safety requirements and is used to define the acceptable range of grid-connected power fluctuation depth. By comparing the first decision grid-connected fluctuation depth coefficient and the threshold, it can be preliminarily determined whether the first optimized grid-connected decision can meet the system requirements.

[0050] If the first decision grid-connected fluctuation depth coefficient is less than the grid-connected power fluctuation depth threshold, it means that the first optimized grid-connected decision can effectively reduce the grid-connected power fluctuation depth and meet the system requirements. Therefore, the output first grid-connected suppression verification result is passed. This result means that the first optimized grid-connected decision is effective and can be used to optimize the photovoltaic grid-connected solution.

[0051] On the contrary, if the first decision grid-connected fluctuation depth coefficient is greater than or equal to the grid-connected power fluctuation depth threshold, it means that the first optimized grid-connected decision fails to effectively reduce the grid-connected power fluctuation depth and does not meet the system requirements. Therefore, the output first grid-connected suppression verification result is failed. This result means that the first optimized grid-connected decision needs to be further optimized or adjusted to ensure that it can meet the stability and safety requirements of the system.

[0052] Through the coherent implementation of these steps, the system can accurately evaluate the effectiveness of optimization decisions and ensure the safety and reliability of photovoltaic grid-connected operations. This process not only enhances the control ability of grid-connected fluctuations, but also provides an important basis for subsequent decision-making optimization, thereby supporting the efficient operation of photovoltaic systems.

[0053] Furthermore, the embodiment of the present application further includes step P44a, and step P44a further includes: P44-1a: When the result of the first grid-connected suppression verification is failed, the first optimized grid-connected decision is optimized for grid-connected power fluctuation suppression, and a grid-connected decision optimization space is established; P44-2a: According to the grid-connected decision optimization space, a second optimized grid-connected decision is randomly extracted; P44-3a: Based on the grid-connected power fluctuation depth threshold, a grid-connected suppression verification is performed on the second optimized grid-connected decision to obtain a second grid-connected suppression verification result; P44-4a: When the result of the second grid-connected suppression verification is passed, the photovoltaic grid-connected scheme is optimized according to the second optimized grid-connected decision to generate the first optimized photovoltaic grid-connected scheme.

[0054] In a possible embodiment of the present application, a method for dealing with a situation where the first grid-connected suppression verification result fails is added to ensure that effective optimized grid-connected decisions can be continuously found and applied, thereby generating a first optimized photovoltaic grid-connected scheme that meets the requirements and ensuring the safety and stability of the photovoltaic grid-connected scheme.

[0055] First, when it is detected that the result of the first grid-connected suppression check fails, that is, the first optimized grid-connected decision fails to effectively reduce the grid-connected power fluctuation depth to below the preset grid-connected power fluctuation depth threshold, the system optimizes the grid-connected power fluctuation suppression for the first optimized grid-connected decision. By adjusting certain parameters or strategies of the grid-connected decision, a grid-connected decision optimization space is established. This optimization space contains a variety of possible grid-connected decision combinations, which means that the system will consider multiple potential decision variables and parameters and explore different combinations to optimize power output. In this way, the system can evaluate possible adjustment plans and ensure that available resources are fully utilized during the optimization process.

[0056] Next, based on the established grid-connected decision optimization space, the second optimized grid-connected decision is randomly extracted. The random extraction method helps to explore different decision options and avoid being limited to a specific solution, thereby increasing the chance of finding an effective optimization strategy. Then, based on the grid-connected power fluctuation depth threshold, the second optimized grid-connected decision is checked for grid suppression. At this point, the system will check whether the new decision can effectively control power fluctuations, that is, whether it meets the set threshold standard.

[0057] If the second grid-connection suppression check result is passed, the photovoltaic grid-connection plan is optimized according to the second optimized grid-connection decision to generate the final first optimized photovoltaic grid-connection plan. This plan will better reflect the optimal operating state of the photovoltaic system under the new decision-making framework, effectively reduce the risk of power fluctuations, and improve the reliability of the overall system.

[0058] Through the implementation of these steps, the system can not only make timely adjustments when the initial decision is not qualified, but also continuously optimize the photovoltaic grid-connected plan to ensure safety and efficiency under different operating conditions. This optimization process enhances the system's adaptability to the grid-connected environment and provides a solid guarantee for the sustainable use of photovoltaic energy.

[0059] P50: Based on the grid-connected abnormal impact photovoltaic unit and the abnormal impact risk prediction result, the first optimized photovoltaic grid-connected scheme is optimized for grid-connected abnormal impact risk according to the second grid-connected fluctuation suppression decision module to obtain a second optimized photovoltaic grid-connected scheme.

[0060] Furthermore, step P50 of the embodiment of the present application also includes: P51: Based on the grid-connected abnormal wave and photovoltaic unit, the first optimized photovoltaic grid-connected scheme is identified in the grid-connected decision to obtain a first grid-connected decision identification result; P52: The first grid-connected decision identification result and the abnormal wave and risk prediction result are input into the second grid-connected fluctuation suppression decision module to obtain a first grid-connected decision optimization result; P53: According to the first grid-connected decision optimization result, the first optimized photovoltaic grid-connected scheme is optimized, and the second optimized photovoltaic grid-connected scheme is output.

[0061] It should be understood that the first optimized photovoltaic grid-connected scheme is further optimized for grid-connected abnormality and risk, so as to ensure that while suppressing grid-connected power fluctuations, the risks that may be caused by grid-connected abnormality and photovoltaic units can also be effectively reduced. The core of this step is to use the grid-connected abnormality and photovoltaic units and abnormality and risk prediction results, combined with the second grid-connected fluctuation suppression decision module, to intelligently optimize the first optimized photovoltaic grid-connected scheme.

[0062] First, based on the photovoltaic units affected by the grid-connected anomaly, the grid-connected decision of the first optimized photovoltaic grid-connected scheme is identified. The goal of this step is to clarify the specific photovoltaic grid-connected decisions corresponding to the photovoltaic units affected by the grid-connected anomaly in the first optimization scheme. These decisions may involve grid-connected time, grid-connected power, grid-connected mode, etc. Through this identification, the system can focus on those units that may be affected by the abnormal impact, providing a basis for subsequent optimization.

[0063] Next, the first grid-connected decision identification result and the abnormal wave risk prediction result are input into the second grid-connected fluctuation suppression decision module. This module is a more complex intelligent algorithm or model, which can not only optimize the grid-connected power fluctuation according to the input grid-connected abnormal information and grid-connected decision, but also comprehensively consider the abnormal wave risk prediction results to generate a more comprehensive and optimized grid-connected decision. This module can be constructed using advanced machine learning technology, risk assessment models, optimization algorithms, etc. to achieve effective suppression of grid-connected abnormal wave risks.

[0064] Then, based on the obtained optimization result of the first grid-connected decision, the first optimized photovoltaic grid-connected scheme is further optimized. This process may involve adjusting the grid-connected strategy of the photovoltaic unit, modifying the power output or other related decisions, and outputting the second optimized photovoltaic grid-connected scheme. This scheme not only effectively suppresses the grid-connected power fluctuation by adjusting the grid-connected decision, but also reduces the risk that may be caused by the abnormal grid connection affecting the photovoltaic unit, and further improves the stability and safety of the system.

[0065] Through the above steps, the system can not only identify and respond to the risks of grid-connected anomalies, but also continuously optimize the grid-connected strategy based on real-time data, ensuring that the photovoltaic system can be safely and effectively connected to the grid in a complex operating environment, laying a solid foundation for the efficient use of renewable energy.

[0066] P60: The distributed photovoltaic system is connected to the photovoltaic grid according to the second optimized photovoltaic grid-connected plan.

[0067] Specifically, the distributed photovoltaic system implements photovoltaic grid-connected operation according to the second optimized photovoltaic grid-connected scheme. This process marks the completion of the entire optimization process and ensures that the system can actually connect to the power grid on a stable basis.

[0068] First, according to the specific guidance of the second optimized photovoltaic grid-connected solution, the grid-connected parameters and operation strategies of each photovoltaic unit are adjusted. After the previous fluctuation suppression and risk optimization, the solution has taken into account the impact of abnormal photovoltaic units connected to the grid and the risk of abnormal spread, ensuring that power fluctuations and potential risks can be effectively reduced during the grid-connected process.

[0069] When performing grid-connected operations, the distributed photovoltaic system will dynamically adjust the power output of each photovoltaic unit based on real-time data and previous optimization results. This real-time adjustment capability is achieved through efficient data acquisition and processing modules, ensuring that the photovoltaic system can respond to external environmental changes (such as weather, load changes, etc.) in a timely manner, thereby optimizing power generation efficiency.

[0070] At the same time, the system also needs to monitor the grid-connected status in real time and continuously evaluate the operating effect of the photovoltaic system through a feedback mechanism. If an abnormal situation is detected (such as power fluctuations exceeding the set threshold), the system can quickly take measures, such as adjusting the grid-connected strategy or activating the backup strategy, to ensure the safety and stability of the power grid.

[0071] Through the implementation of these detailed steps, the distributed photovoltaic system can be safely and reliably connected to the grid under the guidance of the optimized plan, giving full play to the advantages of photovoltaic power generation. This not only improves the stability of the power system, but also makes positive contributions to the efficient use of renewable energy and environmental protection.

[0072] In summary, the embodiments of the present application have at least the following technical effects: This application receives the photovoltaic grid-connected plan by connecting to the photovoltaic control end, and then performs fluctuation prediction based on the grid-connected power fluctuation depth threshold to identify abnormal photovoltaic units and fluctuations, and performs risk prediction based on the abnormal impact risk to determine the abnormal photovoltaic units. Based on these prediction results, the grid-connected plan is optimized through the decision-making module, and finally an optimized photovoltaic grid-connected plan is generated to achieve safe and stable photovoltaic grid-connected.

[0073] The technical effect of establishing an accurate power fluctuation prediction model and risk assessment mechanism to promptly identify and handle abnormal fluctuations and ensure the safe and efficient operation of the photovoltaic system has been achieved.

[0074] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0076] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A distributed photovoltaic grid-connected power fluctuation suppression control method, characterized in that: The method comprises: Connecting to a distributed photovoltaic control terminal to receive a photovoltaic grid-connected solution of a distributed photovoltaic system, wherein the distributed photovoltaic system includes a plurality of photovoltaic units corresponding to a plurality of grid-connected nodes; Based on the grid-connected power fluctuation depth threshold, predict the grid-connected power fluctuation of the distributed photovoltaic system according to the photovoltaic grid-connected scheme, and obtain the prediction results of grid-connected abnormal photovoltaic units and grid-connected abnormal fluctuations; Based on the grid-connected abnormal wave risk constraint condition, the distributed photovoltaic system is predicted for the grid-connected abnormal wave risk according to the grid-connected abnormal photovoltaic unit and the grid-connected abnormal fluctuation prediction result, and the grid-connected abnormal wave photovoltaic unit and the abnormal wave risk prediction result are determined; Based on the abnormal grid-connected photovoltaic unit and the abnormal grid-connected fluctuation prediction result, the photovoltaic grid-connected scheme is optimized for grid-connected power fluctuation suppression according to the first grid-connected fluctuation suppression decision module and the grid-connected power fluctuation depth threshold, to obtain a first optimized photovoltaic grid-connected scheme; Based on the grid-connected abnormal impact photovoltaic unit and the abnormal impact risk prediction result, optimizing the grid-connected abnormal impact risk of the first optimized photovoltaic grid-connected scheme according to the second grid-connected fluctuation suppression decision module to obtain a second optimized photovoltaic grid-connected scheme; The distributed photovoltaic system is photovoltaically connected to the grid according to the second optimized photovoltaic grid-connected scheme.

2. The method according to claim 1, characterized in that Based on the grid-connected power fluctuation depth threshold, the distributed photovoltaic system is predicted for grid-connected power fluctuation according to the photovoltaic grid-connected scheme, and the prediction results of grid-connected abnormal photovoltaic units and grid-connected abnormal fluctuations are obtained, including: Modeling is performed according to the distributed photovoltaic system to obtain a distributed photovoltaic model; Based on the distributed photovoltaic model, predicting grid-connected power fluctuations of the plurality of photovoltaic units according to the photovoltaic grid-connected scheme, and obtaining a plurality of grid-connected fluctuation prediction results; Performing power fluctuation depth evaluation according to the plurality of grid-connected fluctuation prediction results to obtain a plurality of grid-connected power fluctuation depth coefficients; Determining whether the plurality of grid-connected power fluctuation depth coefficients are greater than or equal to the grid-connected power fluctuation depth threshold, and generating an abnormal grid-connected power fluctuation depth coefficient; Based on the abnormal grid-connected power fluctuation depth coefficient, the plurality of photovoltaic units and the plurality of grid-connected fluctuation prediction results are mapped to generate the grid-connected abnormal photovoltaic unit and the grid-connected abnormal fluctuation prediction result.

3. The method according to claim 2, characterized in that Based on the distributed photovoltaic model, grid-connected power fluctuation prediction is performed on the plurality of photovoltaic units according to the photovoltaic grid-connected scheme to obtain a plurality of grid-connected fluctuation prediction results, including: Based on the photovoltaic grid-connected scheme, the plurality of photovoltaic units are subjected to expected grid-connected power fitting to generate a plurality of photovoltaic grid-connected power expected curves; Based on the photovoltaic grid-connected scheme, the grid-connected power of the plurality of photovoltaic units is predicted according to the distributed photovoltaic model to generate a plurality of photovoltaic grid-connected power prediction curves; Based on the multiple photovoltaic grid-connected power expectation curves, grid-connected power fluctuation identification is performed on the multiple photovoltaic grid-connected power prediction curves respectively to generate the multiple grid-connected power fluctuation prediction results.

4. The method according to claim 1, characterized in that Based on the grid-connected abnormal wave risk constraint condition, the distributed photovoltaic system is predicted for the grid-connected abnormal wave risk according to the grid-connected abnormal photovoltaic unit and the grid-connected abnormal fluctuation prediction result, and the grid-connected abnormal wave photovoltaic unit and the abnormal wave risk prediction result are determined, including: Based on the abnormally grid-connected photovoltaic unit, identifying the characteristics of the distributed photovoltaic system to be affected, and determining a plurality of photovoltaic units to be affected; Based on the distributed photovoltaic model, a grid-connected abnormality propagation prediction is performed on the multiple photovoltaic units to be affected according to the grid-connected abnormal fluctuation prediction result to obtain multiple grid-connected abnormality propagation prediction results; Based on the multiple grid-connection abnormality propagation prediction results, a grid-connection abnormality risk assessment is performed to obtain multiple grid-connection abnormality risk coefficients; Determining whether the plurality of grid-connection abnormality risk coefficients meet the grid-connection abnormality risk constraint condition; If any one of the plurality of grid-connection abnormality risk coefficients does not satisfy the grid-connection abnormality risk constraint condition, generating an identified grid-connection abnormality risk coefficient; Based on the identified grid-connected abnormality risk coefficient, the multiple photovoltaic units to be affected and the multiple grid-connected abnormality propagation prediction results are mapped to obtain the grid-connected abnormality affected photovoltaic units and the abnormality risk prediction results.

5. The method according to claim 1, characterized in that Based on the abnormal grid-connected photovoltaic unit and the abnormal grid-connected fluctuation prediction result, the photovoltaic grid-connected scheme is optimized for grid-connected power fluctuation suppression according to the first grid-connected fluctuation suppression decision module and the grid-connected power fluctuation depth threshold, to obtain a first optimized photovoltaic grid-connected scheme, including: Based on the abnormally grid-connected photovoltaic unit, the photovoltaic grid-connected scheme is identified for a grid-connected decision to obtain a first identified grid-connected decision; Inputting the grid-connected abnormal fluctuation prediction result and the first identified grid-connected decision into the first grid-connected fluctuation suppression decision module to obtain a first optimized grid-connected decision; Performing a grid connection inhibition check on the first optimized grid connection decision based on the grid connection power fluctuation depth threshold to obtain a first grid connection inhibition check result; When the first grid-connection inhibition verification result is passed, the photovoltaic grid-connection scheme is optimized according to the first optimized grid-connection decision to generate the first optimized photovoltaic grid-connection scheme.

6. The method according to claim 5, characterized in that Performing a grid connection suppression check on the first optimized grid connection decision based on the grid connection power fluctuation depth threshold to obtain a first grid connection suppression check result includes: Based on the abnormally grid-connected photovoltaic unit, predict the grid-connected power fluctuation depth according to the first optimized grid-connected decision to obtain the first decision grid-connected fluctuation depth coefficient; Determining whether the first decision grid-connected fluctuation depth coefficient is less than the grid-connected power fluctuation depth threshold; If the first decision grid-connected fluctuation depth coefficient is less than the grid-connected power fluctuation depth threshold, the first grid-connected suppression verification result outputted is passed; If the first decision grid-connected fluctuation depth coefficient is greater than or equal to the grid-connected power fluctuation depth threshold, the output first grid-connected suppression verification result is failure.

7. The method according to claim 5, characterized in that When the first grid-connection suppression verification result is failed, optimizing the grid-connection power fluctuation suppression for the first optimized grid-connection decision, and establishing a grid-connection decision optimization space; According to the grid connection decision optimization space, randomly extracting a second optimized grid connection decision; Performing a grid connection inhibition check on the second optimized grid connection decision based on the grid connection power fluctuation depth threshold to obtain a second grid connection inhibition check result; When the second grid-connection inhibition verification result is passed, the photovoltaic grid-connection scheme is optimized according to the second optimized grid-connection decision to generate the first optimized photovoltaic grid-connection scheme.

8. The method according to claim 1, characterized in that Based on the grid-connected abnormal wave photovoltaic unit and the abnormal wave risk prediction result, the first optimized photovoltaic grid-connected scheme is optimized for grid-connected abnormal wave risk according to the second grid-connected fluctuation suppression decision module to obtain a second optimized photovoltaic grid-connected scheme, including: Based on the grid-connected abnormal wave and the photovoltaic unit, the first optimized photovoltaic grid-connected scheme is identified for grid-connected decision, and a first grid-connected decision identification result is obtained; Inputting the first grid connection decision identification result and the abnormal impact risk prediction result into the second grid connection fluctuation suppression decision module to obtain the first grid connection decision optimization result; The first optimized photovoltaic grid-connected scheme is optimized according to the first grid-connected decision optimization result, and the second optimized photovoltaic grid-connected scheme is output.

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