Distributed Photovoltaic Grid-Connected Power Fluctuation Suppression Control Method

Abstract

The present invention discloses a control method for suppressing power fluctuation of distributed photovoltaic grid connection, which relates to the technical field of power control. The method includes: connecting to the distributed photovoltaic control terminal and receiving the photovoltaic grid connection plan; predicting the power fluctuation of grid connection to obtain the abnormal photovoltaic units for grid connection and the prediction result of abnormal fluctuation of grid connection; predicting the risk of abnormal spread in grid connection to determine the photovoltaic units affected by abnormal spread in grid connection and the prediction result of risk of abnormal spread; based on the prediction results, optimizing the suppression of power fluctuation of grid connection and optimizing the risk of abnormal spread in grid connection to obtain an optimized photovoltaic grid connection plan, and performing photovoltaic grid connection. The present invention solves the technical problem that the traditional grid connection 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, and achieves the technical effect of ensuring the safe and efficient operation of the photovoltaic system by establishing an accurate power fluctuation prediction model and a risk assessment mechanism to timely identify and handle abnormal fluctuation situations.

Description

Technical Field

[0001] The present invention relates to the technical field of power control, and particularly to a control method for suppressing power fluctuations in distributed photovoltaic grid connection. Background Art

[0002] With the increasing global demand for sustainable development and clean energy, distributed photovoltaic systems are being applied more and more widely. A photovoltaic power generation system consists of multiple grid connection 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 result in its power output being affected by various factors such as weather changes and light intensity, leading to frequent power fluctuation phenomena.

[0003] These power fluctuations not only affect the stability of the power system but may also cause grid connection safety hazards. Therefore, effective fluctuation suppression control methods are needed to address this challenge. However, traditional grid connection control methods often cannot respond in real time to complex dynamic environments and are difficult to effectively predict and suppress abnormal fluctuations. Summary of the Invention

[0004] This application provides a control method for suppressing power fluctuations in distributed photovoltaic grid connection, which is used to solve the technical problem that traditional grid connection control methods in the prior art cannot respond in real time to complex dynamic environments and are difficult to effectively predict and suppress abnormal fluctuations.

[0005] This application provides a control method for suppressing power fluctuations in distributed photovoltaic grid connection. The method includes: connecting to a distributed photovoltaic management and control terminal and receiving a photovoltaic grid connection plan of the distributed photovoltaic system, where the distributed photovoltaic system includes multiple photovoltaic units corresponding to multiple grid connection nodes; based on a grid-connected power fluctuation depth threshold, predicting the grid-connected power fluctuations of the distributed photovoltaic system according to the photovoltaic grid connection plan to obtain grid-connected abnormal photovoltaic units and grid-connected abnormal fluctuation prediction results; based on grid-connected abnormal spread risk constraint conditions, predicting the grid-connected abnormal spread risk of the distributed photovoltaic system according to the grid-connected abnormal photovoltaic units and the grid-connected abnormal fluctuation prediction results to determine grid-connected abnormal spread photovoltaic units and abnormal spread risk prediction results; based on the grid-connected abnormal photovoltaic units and the grid-connected abnormal fluctuation prediction results, optimizing the photovoltaic grid connection plan for grid-connected power fluctuation suppression according to a first grid-connected fluctuation suppression decision module and the grid-connected power fluctuation depth threshold to obtain a first optimized photovoltaic grid connection plan; based on the grid-connected abnormal spread photovoltaic units and the abnormal spread risk prediction results, optimizing the grid-connected abnormal spread risk of the first optimized photovoltaic grid connection plan according to a second grid-connected fluctuation suppression decision module to obtain a second optimized photovoltaic grid connection plan; and the distributed photovoltaic system performs photovoltaic grid connection according to the second optimized photovoltaic grid connection plan.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The distributed photovoltaic grid-connected power fluctuation suppression control method provided in this application relates to the field of power control technology. It receives a photovoltaic grid connection plan by connecting to a photovoltaic control terminal, then performs fluctuation prediction based on the grid-connected power fluctuation depth threshold to identify abnormal photovoltaic units and fluctuations, conducts risk prediction according to the affected risk of the abnormality, determines the abnormal photovoltaic units, and based on these prediction results, optimizes the grid connection plan through a decision-making module, and finally generates an optimized photovoltaic grid connection plan to achieve safe and stable photovoltaic grid connection. It solves the technical problem that traditional grid connection control methods in the prior art cannot respond to complex dynamic environments in real time and are difficult to effectively predict and suppress abnormal fluctuations, and realizes the technical effect of ensuring the safe and efficient operation of the photovoltaic system by establishing an accurate power fluctuation prediction model and a risk assessment mechanism to timely identify and handle abnormal fluctuation situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0009] Figure 1 It is a schematic flowchart of the distributed photovoltaic grid-connected power fluctuation suppression control method provided in the embodiments of this application;

[0010] Figure 2 It is a schematic flowchart of obtaining the first optimized photovoltaic grid connection plan in the distributed photovoltaic grid-connected power fluctuation suppression control method provided in the embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] This application provides a distributed photovoltaic grid-connected power fluctuation suppression control method to solve the technical problem that traditional grid connection control methods in the prior art cannot respond to complex dynamic environments in real time and are difficult to effectively predict and suppress abnormal fluctuations.

[0012] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the protection scope of this application.

[0013] It should be noted that the terms "first", "second", etc. in the description of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0014] Embodiment 1, as Figure 1 shown, the present application provides a distributed photovoltaic grid-connected power fluctuation suppression control method, and the method includes:

[0015] P10: Connect to the distributed photovoltaic control terminal and receive the photovoltaic grid-connected plan of the distributed photovoltaic system, where the distributed photovoltaic system includes a plurality of photovoltaic units corresponding to a plurality of grid-connected nodes.

[0016] 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 the blueprint for the system operation and details how to safely and efficiently integrate the electric energy generated by each photovoltaic unit into the power grid. It should be noted that the photovoltaic grid-connected plan not only includes the macroscopic grid-connected strategy but also specifically the photovoltaic grid-connected decisions to be executed corresponding to 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 grid connection in an optimal state.

[0017] Among them, the distributed photovoltaic system is composed of a plurality of photovoltaic units corresponding to a plurality of grid-connected nodes. Each photovoltaic unit is an independent power generation unit, and they jointly constitute the power generation capacity of the entire system. These photovoltaic units may be distributed in different geographical locations and are connected to the power grid through grid-connected nodes. Therefore, when receiving the photovoltaic grid-connected plan, it is necessary to ensure that the plan can comprehensively cover all photovoltaic units and accurately reflect the grid-connected requirements and conditions of each unit.

[0018] 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 promptly respond to the operating status and environmental changes of each photovoltaic unit, thereby adjusting the grid-connected decisions.

[0019] In the process of receiving a photovoltaic grid connection scheme, the system not only needs to consider the individual characteristics of each photovoltaic unit, but also must analyze the mutual relationships and synergy effects among them. This systematic thinking is crucial for formulating the overall grid connection strategy, helping to optimize the operation efficiency of the photovoltaic system and minimizing the potential power fluctuation risk to the greatest extent.

[0020] P20: Based on the grid connection power fluctuation depth threshold, perform grid connection power fluctuation prediction on the distributed photovoltaic system according to the photovoltaic grid connection scheme, and obtain grid connection abnormal photovoltaic units and grid connection abnormal fluctuation prediction results.

[0021] Further, step P20 of the embodiment of the present application further includes:

[0022] P21: Model according to the distributed photovoltaic system to obtain a distributed photovoltaic model;

[0023] P22: Based on the distributed photovoltaic model, perform grid connection power fluctuation prediction on the multiple photovoltaic units according to the photovoltaic grid connection scheme to obtain multiple grid connection fluctuation prediction results; P23: Perform power fluctuation depth evaluation according to the multiple grid connection fluctuation prediction results to obtain multiple grid connection power fluctuation depth coefficients; P24: Judge whether the multiple grid connection power fluctuation depth coefficients are greater than or equal to the grid connection power fluctuation depth threshold to generate abnormal grid connection power fluctuation depth coefficients; P25: Based on the abnormal grid connection power fluctuation depth coefficients, map the multiple photovoltaic units and the multiple grid connection fluctuation prediction results to generate the grid connection abnormal photovoltaic units and the grid connection abnormal fluctuation prediction results.

[0024] It should be understood that according to the set grid connection power fluctuation depth threshold, power fluctuation prediction is performed on the distributed photovoltaic system to identify grid connection abnormal photovoltaic units and their fluctuation prediction results.

[0025] First, model the distributed photovoltaic system to obtain a comprehensive distributed photovoltaic model. This model is usually based on the characteristics of photovoltaic units, environmental factors (such as light, temperature, etc.) and operation data, and can reflect the dynamic behavior and performance characteristics of the system. Adopting appropriate modeling methods (such as machine learning algorithms or physical models) can effectively capture the response characteristics of photovoltaic units and provide an accurate basis for subsequent fluctuation prediction.

[0026] Next, use the constructed distributed photovoltaic model to perform grid connection power fluctuation prediction on each photovoltaic unit according to the photovoltaic grid connection scheme. This stage involves using prediction algorithms (such as time series analysis or neural networks) to process the historical data and current state of each photovoltaic unit to generate multiple grid connection fluctuation prediction results. The results can reflect the power output changes of each photovoltaic unit under different conditions.

[0027] Further, perform a power fluctuation depth evaluation on multiple grid-connected fluctuation prediction results to calculate multiple grid-connected power fluctuation depth coefficients. These coefficients represent the degree of fluctuation that each photovoltaic unit may encounter during the expected grid connection process and need to be compared with the set grid-connected power fluctuation depth threshold.

[0028] Next, determine whether these fluctuation depth coefficients are greater than or equal to the set threshold to generate 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.

[0029] Finally, based on the identified abnormal grid-connected power fluctuation depth coefficients, map the corresponding photovoltaic units and prediction results. Specifically, mark the photovoltaic units corresponding to the abnormal depth coefficients as grid-connected abnormal photovoltaic units, and record the corresponding fluctuation prediction results as grid-connected abnormal fluctuation prediction results. Through this mapping, it is possible to effectively identify and locate the photovoltaic units that may cause problems during grid connection, providing a basis for subsequent risk management and control strategies.

[0030] Further, step P23 of the embodiment of the present application further includes:

[0031] P23-1: Perform grid-connected power expectation fitting on the multiple photovoltaic units based on the photovoltaic grid connection plan to generate multiple photovoltaic grid-connected power expectation curves; P23-2: Based on the photovoltaic grid connection plan, perform grid-connected power prediction on the multiple photovoltaic units 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 expectation curves, perform grid-connected power fluctuation identification on the multiple photovoltaic grid-connected power prediction curves respectively to generate the multiple grid-connected fluctuation prediction results.

[0032] Optionally, the specific process of performing power fluctuation depth evaluation includes: fitting the expected power, predicting the actual power, and identifying the fluctuations. First, perform grid-connected power expectation fitting on multiple photovoltaic units based on the photovoltaic grid connection plan. The grid-connected power expectation here refers to the power output that the photovoltaic unit should reach under ideal conditions. The fitting process can use data fitting techniques (such as polynomial fitting or curve fitting) to generate multiple photovoltaic grid-connected power expectation curves, which represent the change of the grid-connected power that each photovoltaic unit should generate over time under ideal conditions.

[0033] Next, using the constructed distributed photovoltaic model, according to the photovoltaic grid connection plan, the grid connection power of multiple photovoltaic units is predicted. An actual possible grid connection power curve, that is, a photovoltaic grid connection power prediction curve, is generated. At this time, the multiple generated photovoltaic grid connection power prediction curves reflect the power output of the photovoltaic units under actual operating conditions. This prediction process may use real-time data and model algorithms, such as regression analysis or machine learning techniques, to ensure the accuracy and timeliness of the prediction.

[0034] Finally, based on the obtained multiple expected curves of photovoltaic grid connection power, the fluctuations of the photovoltaic grid connection power prediction curves are identified respectively. The fluctuation identification here involves comparing the difference between the expected power and the actual predicted power. By setting thresholds or using statistical methods (such as standard deviation calculation), the fluctuation situation of the power output is judged, and the prediction curves are smoothed and outliers are detected, thus generating multiple grid connection fluctuation prediction results. These results reflect the possible power fluctuation situations of each photovoltaic unit during the grid connection process, providing an important basis for subsequent control and optimization.

[0035] Through the close combination of the above steps, the expectation and actual performance of the grid connection power can be comprehensively evaluated, and potential power fluctuations can be identified, thus providing data support for subsequent risk management and optimization decisions. This process not only improves the accuracy of grid connection control but also enhances the guarantee of the overall stability of the photovoltaic system.

[0036] P30: Based on the grid connection anomaly spread risk constraint conditions, the grid connection anomaly spread risk of the distributed photovoltaic system is predicted according to the grid connection abnormal photovoltaic unit and the grid connection abnormal fluctuation prediction result, and the grid connection abnormal spread photovoltaic unit and the abnormal spread risk prediction result are determined.

[0037] Furthermore, step P30 of the embodiment of the present application further includes:

[0038] P31: Based on the grid-connected abnormal photovoltaic unit, identify the characteristics of the distributed photovoltaic system to be affected, and determine multiple photovoltaic units to be affected; P32: Based on the distributed photovoltaic model, predict the grid-connected abnormal propagation of the multiple photovoltaic units to be affected according to the grid-connected abnormal fluctuation prediction result, and obtain multiple grid-connected abnormal propagation prediction results; P33: Evaluate the grid-connected abnormal impact risk based on the multiple grid-connected abnormal propagation prediction results, and obtain multiple grid-connected abnormal impact risk coefficients; P34: Determine whether the multiple grid-connected abnormal impact risk coefficients meet the grid-connected abnormal impact risk constraint conditions; P35: If any one of the multiple grid-connected abnormal impact risk coefficients does not meet the grid-connected abnormal impact risk constraint conditions, generate a marked grid-connected abnormal impact risk coefficient; P36: Map the multiple photovoltaic units to be affected and the multiple grid-connected abnormal propagation prediction results based on the marked grid-connected abnormal impact risk coefficient, and obtain the grid-connected abnormal affected photovoltaic units and the abnormal impact risk prediction results.

[0039] It should be understood that, based on the grid-connected abnormal impact risk constraint conditions, using the grid-connected abnormal photovoltaic unit and the grid-connected abnormal fluctuation prediction result, the grid-connected abnormal impact risk of the distributed photovoltaic system is predicted, so as to identify the potentially affected photovoltaic units and their risk levels.

[0040] First, based on the identified grid-connected abnormal photovoltaic unit, identify the characteristics of the distributed photovoltaic system to be affected. Among them, the photovoltaic unit to be affected refers to the photovoltaic unit that is not marked as grid-connected abnormal. Through this identification, all photovoltaic units in the system except the grid-connected abnormal photovoltaic unit are regarded as photovoltaic units to be affected, and these units may be affected by grid-connected abnormalities in the future and need special attention.

[0041] Next, based on the distributed photovoltaic model, use the grid-connected abnormal fluctuation prediction result to predict the grid-connected abnormal propagation of multiple photovoltaic units to be affected, that is, simulate the propagation process of grid-connected abnormalities in the system to evaluate the possible impact on other photovoltaic units. This prediction process can use simulation technology and mathematical models, and combine 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, showing the risk levels of the photovoltaic units to be affected.

[0042] Furthermore, based on the obtained multiple grid connection abnormal propagation prediction results, a grid connection abnormal impact risk assessment is carried out, and multiple grid connection abnormal impact risk coefficients are calculated. Exemplarily, methods such as weighted scoring method, decision tree, Bayesian network, etc. can be used to establish a suitable risk assessment model. This model needs to consider various influencing factors, including the amplitude, frequency, duration, and potential impact range of fluctuations, etc., and analyze each input multiple grid connection abnormal propagation prediction result according to the risk assessment model to calculate the corresponding grid connection abnormal impact risk coefficient. These risk coefficients quantify the possible impact degrees that each to-be-impacted photovoltaic unit may suffer under abnormal conditions, providing a specific assessment of the risk.

[0043] Then, it is judged whether these grid connection abnormal impact risk coefficients meet the preset grid connection abnormal impact risk constraint conditions. Among them, the grid connection abnormal impact risk constraint conditions can be based on historical data, industry standards, or specific operation requirements, aiming to ensure that the system can still operate safely under abnormal conditions. If it is found that any one of the grid connection abnormal impact risk coefficients does not meet the risk constraint conditions, the grid connection abnormal impact risk coefficient is marked. These marked risk coefficients indicate that there are potential risks in the grid connection operation of the relevant photovoltaic units, and measures need to be taken immediately for management.

[0044] Finally, based on the marked grid connection abnormal impact risk coefficients, a mapping is performed on the multiple to-be-impacted photovoltaic units and the grid connection abnormal propagation prediction results, that is, the marked risk coefficients are corresponded to the specific to-be-impacted photovoltaic units to obtain the grid connection abnormal impact photovoltaic units and their abnormal impact risk prediction results.

[0045] Through the above steps, the risk of grid connection abnormal impact can be comprehensively evaluated, the affected photovoltaic units can be identified, providing strong support for subsequent risk control and optimization decisions. This process not only enhances the adaptive ability of the system but also improves the overall stability and security of the photovoltaic system.

[0046] P40: Based on the grid connection abnormal photovoltaic units and the grid connection abnormal fluctuation prediction results, according to the first grid connection fluctuation suppression decision module and the grid connection power fluctuation depth threshold, an optimization search for grid connection power fluctuation suppression of the photovoltaic grid connection scheme is carried out to obtain the first optimized photovoltaic grid connection scheme.

[0047] Furthermore, as Figure 2 shown, step P40 of the embodiment of the present application further includes:

[0048] P41: Identify the grid connection decision of the PV grid connection plan based on the abnormal grid-connected PV unit to obtain the first identified grid connection decision; P42: Input the grid connection abnormal fluctuation prediction result and the first identified grid connection decision into the first grid connection fluctuation suppression decision module to obtain the first optimized grid connection decision; P43: Based on the grid connection power fluctuation depth threshold, perform grid connection suppression verification on the first optimized grid connection decision to obtain the first grid connection suppression verification result; P44: When the first grid connection suppression verification result is passed, optimize the PV grid connection plan according to the first optimized grid connection decision to generate the first optimized PV grid connection plan.

[0049] Optionally, for the identified abnormal grid-connected PV unit and the grid connection abnormal fluctuation prediction result, use the first grid connection fluctuation suppression decision module to optimize the PV grid connection plan to obtain the first optimized PV grid connection plan.

[0050] First, based on the abnormal grid-connected PV unit, identify the grid connection decision of the PV grid connection plan, clarify the specific grid connection decision corresponding to the abnormal grid-connected PV unit in the current PV grid connection plan, and generate the first identified grid connection decision. These decisions may include adjusting the power output, changing the grid connection time, etc., providing basic information for subsequent optimization.

[0051] Next, input the grid connection abnormal fluctuation prediction result and the first identified grid connection decision into the first grid connection fluctuation suppression decision module. This module can be an intelligent algorithm model, which may adopt machine learning or optimization algorithms, aiming to analyze and determine the best fluctuation suppression strategy based on the input data. During this process, the model will evaluate the current PV grid connection plan, consider the impact of abnormal fluctuations, and finally output the first optimized grid connection decision.

[0052] Then, based on the grid connection power fluctuation depth threshold, perform grid connection suppression verification on the first optimized grid connection decision to confirm whether the optimized decision meets the set power fluctuation depth threshold, so as to ensure that there will be no excessive fluctuations during the grid connection operation. The verification result may be "passed" or "not passed".

[0053] Finally, if the first grid connection suppression verification result is "passed", optimize the PV grid connection plan according to the first optimized grid connection decision to generate the first optimized PV grid connection plan. This plan not only considers the impact of the abnormal grid-connected PV unit, but also ensures that the power fluctuation is minimized to the greatest extent during actual operation, improving the stability and efficiency of the system.

[0054] Through the coherent implementation of these steps, the system can effectively identify and handle grid connection abnormalities, improving the reliability of the PV system. This optimization process not only helps to maintain the stability of the power grid, but also provides strong technical support for the efficient operation of the PV system.

[0055] Furthermore, step P43 in the embodiments of the present application further includes:

[0056] P43-1: Based on the grid-connected abnormal photovoltaic unit, predict the depth of grid-connected power fluctuation according to the first optimized grid-connection decision to obtain the first decision-making grid-connection fluctuation depth coefficient; P43-2: Determine whether the first decision-making grid-connection fluctuation depth coefficient is less than the grid-connected power fluctuation depth threshold; P43-3: If the first decision-making grid-connection fluctuation depth coefficient is less than the grid-connected power fluctuation depth threshold, the output first grid-connection suppression verification result is passed; P43-4: If the first decision-making grid-connection fluctuation depth coefficient is greater than or equal to the grid-connected power fluctuation depth threshold, the output first grid-connection suppression verification result is not passed.

[0057] Specifically, further refine the grid-connection suppression verification process to ensure the effectiveness of the first optimized grid-connection decision. Based on the grid-connected abnormal photovoltaic unit, use the first optimized grid-connection decision to predict the depth of grid-connected power fluctuation. Advanced simulation technologies and mathematical models can be adopted to conduct a detailed analysis of the dynamic response of the system to predict the fluctuation of grid-connected power. Through simulation and calculation, the first decision-making grid-connection fluctuation depth coefficient is obtained. This coefficient reflects the depth of power fluctuation after implementing the optimized decision and can provide a preliminary assessment of system stability.

[0058] Next, determine whether the obtained first decision-making grid-connection fluctuation depth coefficient is less than the set grid-connected power fluctuation depth threshold. This threshold is set according to system stability and safety requirements and is used to define the acceptable range of grid-connected power fluctuation depth. By comparing the first decision-making grid-connection fluctuation depth coefficient with the threshold, it can be preliminarily determined whether the first optimized grid-connection decision can meet the system requirements.

[0059] If the first decision-making grid-connection fluctuation depth coefficient is less than the grid-connected power fluctuation depth threshold, it indicates that the first optimized grid-connection decision can effectively reduce the depth of grid-connected power fluctuation and meet the system requirements. Therefore, the output first grid-connection suppression verification result is passed. This result means that the first optimized grid-connection decision is effective and can be used to optimize the photovoltaic grid-connection scheme.

[0060] On the contrary, if the first decision-making grid-connection fluctuation depth coefficient is greater than or equal to the grid-connected power fluctuation depth threshold, it indicates that the first optimized grid-connection decision fails to effectively reduce the depth of grid-connected power fluctuation and does not meet the system requirements. Therefore, the output first grid-connection suppression verification result is not passed. This result means that the first optimized grid-connection decision needs to be further optimized or adjusted to ensure that it can meet the system stability and safety requirements.

[0061] Through the coherent implementation of these steps, the system can accurately evaluate the effectiveness of the optimization decision and ensure the safety and reliability of the PV grid connection operation. This process not only enhances the control ability of grid connection fluctuations but also provides an important basis for subsequent decision optimization, thus supporting the efficient operation of the PV system.

[0062] Furthermore, the embodiment of the present application further includes step P44a, and step P44a further includes:

[0063] P44-1a: When the first grid connection suppression verification result fails, perform grid connection power fluctuation suppression optimization on the first optimized grid connection decision to establish a grid connection decision optimization space; P44-2a: Randomly extract a second optimized grid connection decision according to the grid connection decision optimization space; P44-3a: Perform grid connection suppression verification on the second optimized grid connection decision based on the grid connection power fluctuation depth threshold to obtain a second grid connection suppression verification result; P44-4a: When the second grid connection suppression verification result passes, optimize the PV grid connection scheme according to the second optimized grid connection decision to generate the first optimized PV grid connection scheme.

[0064] In a possible embodiment of the present application, the situation where the first grid connection suppression verification result fails is addressed to ensure that effective optimized grid connection decisions can be continuously searched for and applied, thereby generating a first optimized PV grid connection scheme that meets the requirements and ensuring the safety and stability of the PV grid connection scheme.

[0065] First, when it is detected that the first grid connection suppression verification result fails, that is, the first optimized grid connection decision fails to effectively reduce the grid connection power fluctuation depth below the preset grid connection power fluctuation depth threshold, the system performs grid connection power fluctuation suppression optimization on the first optimized grid connection decision. By adjusting certain parameters or strategies of the grid connection decision, a grid connection decision optimization space is established. This optimization space contains various possible grid connection decision combinations, which means that the system will consider multiple potential decision variables and parameters and explore different combinations to optimize the power output. In this way, the system can evaluate possible adjustment schemes and ensure that available resources are fully utilized during the optimization process.

[0066] Next, according to the established grid connection decision optimization space, a second optimized grid connection decision is randomly extracted. The random extraction method helps to explore different decision options and avoid being limited to a specific scheme, thereby increasing the chance of finding an effective optimization strategy. Then, based on the grid connection power fluctuation depth threshold, grid connection suppression verification is performed on the second optimized grid connection decision. At this time, the system will check whether the new decision can effectively control the power fluctuation, that is, whether it meets the set threshold standard.

[0067] If the second grid connection suppression verification result passes, optimize the PV grid connection plan according to the second optimized grid connection decision to generate the final first optimized PV grid connection plan. This plan will better reflect the optimal operating state of the PV system under the new decision-making framework, effectively reduce the risk of power fluctuations, and improve the reliability of the overall system.

[0068] By implementing these steps, the system can not only adjust in a timely manner when the preliminary decision is unqualified, but also continuously optimize the PV grid connection plan to ensure safety and efficiency under different operating conditions. This optimization process enhances the system's adaptability to the grid connection environment and provides a solid guarantee for the sustainable utilization of PV energy.

[0069] P50: Based on the grid connection anomaly affecting the PV unit and the predicted result of the anomaly impact risk, optimize the grid connection anomaly impact risk of the first optimized PV grid connection plan according to the second grid connection fluctuation suppression decision module to obtain the second optimized PV grid connection plan.

[0070] Furthermore, step P50 of the embodiment of the present application further includes:

[0071] P51: Identify the grid connection decision of the first optimized PV grid connection plan based on the grid connection anomaly affecting the PV unit to obtain the first grid connection decision identification result; P52: Input the first grid connection decision identification result and the predicted result of the anomaly impact risk into the second grid connection fluctuation suppression decision module to obtain the first grid connection decision optimization result; P53: Optimize the first optimized PV grid connection plan according to the first grid connection decision optimization result and output the second optimized PV grid connection plan.

[0072] It should be understood that the grid connection anomaly impact risk of the first optimized PV grid connection plan is further optimized to ensure that while suppressing the grid connection power fluctuation, the risk that may be caused by the grid connection anomaly affecting the PV unit can also be effectively reduced. The core of this step is to use the grid connection anomaly affecting the PV unit and the predicted result of the anomaly impact risk, combined with the second grid connection fluctuation suppression decision module, to intelligently optimize the first optimized PV grid connection plan.

[0073] First, identify the grid connection decision of the first optimized PV grid connection plan based on the grid connection anomaly affecting the PV unit. The goal of this step is to clarify the specific PV grid connection decisions corresponding to the grid connection anomaly affecting the PV unit in the first optimized plan, and these decisions may involve grid connection time, grid connection power, grid connection method, etc. Through this identification, the system can focus on the units that may be affected by the anomaly impact and provide a basis for subsequent optimization.

[0074] Next, input the first grid connection decision recognition result and the abnormal impact risk prediction result into the second grid connection fluctuation suppression decision-making module. This module is a more complex intelligent algorithm or model that can not only optimize the grid connection power fluctuation according to the input grid connection abnormal information and grid connection decision, but also comprehensively consider the abnormal impact risk prediction result to generate a more comprehensive and optimized grid connection decision. This module can be constructed using advanced machine learning techniques, risk assessment models, optimization algorithms, etc. to effectively suppress the risk of abnormal impact on grid connection.

[0075] Then, according to the obtained first grid connection decision optimization result, further optimize the first optimized photovoltaic grid connection scheme. This process may involve adjusting the grid connection strategy of photovoltaic units, modifying the power output or other relevant decisions, and outputting the second optimized photovoltaic grid connection scheme. Based on maintaining the advantages of the first optimized photovoltaic grid connection scheme, by adjusting the grid connection decision, this scheme not only effectively suppresses the grid connection power fluctuation, but also reduces the risk that may be caused by the abnormal impact on photovoltaic units due to grid connection abnormalities, further improving the stability and security of the system.

[0076] Through the above steps, the system can not only identify and cope with the risks of abnormal impact on grid connection, but also continuously optimize the grid connection strategy according to real-time data, ensuring that the photovoltaic system can be safely and effectively grid-connected in a complex operating environment, laying a solid foundation for the efficient utilization of renewable energy.

[0077] P60: The distributed photovoltaic system performs photovoltaic grid connection according to the second optimized photovoltaic grid connection scheme.

[0078] Specifically, the distributed photovoltaic system implements the photovoltaic grid connection operation according to the second optimized photovoltaic grid connection scheme. This process marks the completion of the entire optimization process and ensures that the system can perform actual power grid connection on a stable basis.

[0079] First, according to the specific guidance of the second optimized photovoltaic grid connection scheme, adjust the grid connection parameters and operation strategies of each photovoltaic unit. This scheme has considered the impact of abnormal grid-connected photovoltaic units and abnormal impact risks after the previous fluctuation suppression and risk optimization, ensuring that power fluctuations and potential risks can be effectively reduced during the grid connection process.

[0080] When performing the grid connection operation, the distributed photovoltaic system will dynamically adjust the power output of each photovoltaic unit according to real-time data and the previous optimization results. This real-time adjustment ability is achieved through an efficient data acquisition and processing module, ensuring that the photovoltaic system can respond to external environmental changes (such as weather, load changes, etc.) in a timely manner, thereby optimizing the power generation efficiency.

[0081] Meanwhile, the system also needs to monitor the grid-connected status in real time and continuously evaluate the operation effect of the photovoltaic system through a feedback mechanism. If abnormal situations (such as power fluctuations exceeding the set threshold) are detected, the system can quickly take measures, such as adjusting the grid connection strategy or activating the backup strategy, to ensure the safety and stability of the power grid.

[0082] Through the implementation of these detailed steps, the distributed photovoltaic system can achieve grid connection safely and reliably under the guidance of the optimized scheme, giving full play to the advantages of photovoltaic power generation. This not only improves the stability of the power system but also makes a positive contribution to the efficient utilization of renewable energy and environmental protection.

[0083] In summary, the embodiments of the present application have at least the following technical effects:

[0084] This application receives the photovoltaic grid connection scheme by connecting to the photovoltaic control terminal, then performs fluctuation prediction based on the depth threshold of grid-connected power fluctuations to identify abnormal photovoltaic units and fluctuations, conducts risk prediction according to the risk of abnormal spread, determines the abnormal photovoltaic units, and optimizes the grid connection scheme through the decision-making module based on these prediction results, finally generating an optimized photovoltaic grid connection scheme to achieve safe and stable photovoltaic grid connection.

[0085] It achieves the technical effect of timely identifying and handling abnormal fluctuation situations by establishing an accurate power fluctuation prediction model and a risk assessment mechanism to ensure the safe and efficient operation of the photovoltaic system.

[0086] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification is given. In addition, the processes depicted in the 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 beneficial.

[0087] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0088] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications 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 equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A distributed photovoltaic grid-connected power fluctuation suppression control method, characterized in that, The method includes: Connect to the distributed photovoltaic control terminal and receive the photovoltaic grid connection plan of the distributed photovoltaic system. Among them, the distributed photovoltaic system includes multiple photovoltaic units corresponding to multiple grid connection nodes; Based on the grid connection power fluctuation depth threshold, perform grid connection power fluctuation prediction on the distributed photovoltaic system according to the photovoltaic grid connection plan, and obtain grid connection abnormal photovoltaic units and grid connection abnormal fluctuation prediction results; Based on the grid connection abnormal spread risk constraint conditions, perform grid connection abnormal spread risk prediction on the distributed photovoltaic system according to the grid connection abnormal photovoltaic units and the grid connection abnormal fluctuation prediction results, and determine the grid connection abnormal spread photovoltaic units and the abnormal spread risk prediction results; Based on the grid connection abnormal photovoltaic units and the grid connection abnormal fluctuation prediction results, perform grid connection power fluctuation suppression optimization on the photovoltaic grid connection plan according to the first grid connection fluctuation suppression decision module and the grid connection power fluctuation depth threshold, and obtain the first optimized photovoltaic grid connection plan; Based on the grid connection abnormal spread photovoltaic units and the abnormal spread risk prediction results, perform grid connection abnormal spread risk optimization on the first optimized photovoltaic grid connection plan according to the second grid connection fluctuation suppression decision module, and obtain the second optimized photovoltaic grid connection plan; The distributed photovoltaic system performs photovoltaic grid connection according to the second optimized photovoltaic grid connection plan; Performing grid connection power fluctuation prediction on the distributed photovoltaic system according to the photovoltaic grid connection plan, and obtaining grid connection abnormal photovoltaic units and grid connection abnormal fluctuation prediction results, including: Model the distributed photovoltaic system to obtain a distributed photovoltaic model; Based on the distributed photovoltaic model, perform grid connection power fluctuation prediction on the multiple photovoltaic units according to the photovoltaic grid connection plan, and obtain multiple grid connection fluctuation prediction results; Perform power fluctuation depth evaluation according to the multiple grid connection fluctuation prediction results to obtain multiple grid connection power fluctuation depth coefficients; Judge whether the multiple grid connection power fluctuation depth coefficients are greater than or equal to the grid connection power fluctuation depth threshold, and generate abnormal grid connection power fluctuation depth coefficients; Based on the abnormal grid connection power fluctuation depth coefficients, map the multiple photovoltaic units and the multiple grid connection fluctuation prediction results to generate the grid connection abnormal photovoltaic units and the grid connection abnormal fluctuation prediction results.

2. The method according to claim 1, wherein Based on the distributed photovoltaic model, perform grid connection power fluctuation prediction on the multiple photovoltaic units according to the photovoltaic grid connection plan, and obtain multiple grid connection fluctuation prediction results, including: Perform grid connection power expectation fitting on the multiple photovoltaic units according to the photovoltaic grid connection plan to generate multiple photovoltaic grid connection power expectation curves; Based on the photovoltaic grid connection plan, perform grid connection power prediction on the multiple photovoltaic units according to the distributed photovoltaic model to generate multiple photovoltaic grid connection power prediction curves; Based on the multiple photovoltaic grid connection power expectation curves, perform grid connection power fluctuation identification on the multiple photovoltaic grid connection power prediction curves respectively to generate the multiple grid connection fluctuation prediction results.

3. The method according to claim 1, characterized in that Based on the grid connection anomaly spreading risk constraint conditions, perform grid connection anomaly spreading risk prediction on the distributed photovoltaic system according to the grid connection abnormal photovoltaic unit and the grid connection abnormal fluctuation prediction result, and determine the grid connection abnormal spreading photovoltaic unit and the abnormal spreading risk prediction result, including: Based on the grid connection abnormal photovoltaic unit, perform identification of characteristics to be spread on the distributed photovoltaic system to determine multiple photovoltaic units to be spread; Based on the distributed photovoltaic model, perform grid connection abnormal propagation prediction on the multiple photovoltaic units to be spread according to the grid connection abnormal fluctuation prediction result, and obtain multiple grid connection abnormal propagation prediction results; Perform grid connection abnormal spreading risk evaluation based on the multiple grid connection abnormal propagation prediction results to obtain multiple grid connection abnormal spreading risk coefficients; Judge whether the multiple grid connection abnormal spreading risk coefficients meet the grid connection abnormal spreading risk constraint conditions; If any one of the multiple grid connection abnormal spreading risk coefficients does not meet the grid connection abnormal spreading risk constraint conditions, generate an identified grid connection abnormal spreading risk coefficient; Perform mapping on the multiple photovoltaic units to be spread and the multiple grid connection abnormal propagation prediction results based on the identified grid connection abnormal spreading risk coefficient to obtain the grid connection abnormal spreading photovoltaic unit and the abnormal spreading risk prediction result.

4. The method according to claim 1, wherein Based on the grid connection abnormal photovoltaic unit and the grid connection abnormal fluctuation prediction result, perform optimization of grid connection power fluctuation suppression on the photovoltaic grid connection scheme according to the first grid connection fluctuation suppression decision module and the grid connection power fluctuation depth threshold, and obtain the first optimized photovoltaic grid connection scheme, including: Perform grid connection decision identification on the photovoltaic grid connection scheme based on the grid connection abnormal photovoltaic unit to obtain the first identified grid connection decision; Input the grid connection abnormal fluctuation prediction result and the first identified grid connection decision into the first grid connection fluctuation suppression decision module to obtain the first optimized grid connection decision; Perform grid connection suppression verification on the first optimized grid connection decision based on the grid connection power fluctuation depth threshold to obtain the first grid connection suppression verification result; When the first grid connection suppression verification result is passed, optimize the photovoltaic grid connection scheme according to the first optimized grid connection decision to generate the first optimized photovoltaic grid connection scheme.

5. The method according to claim 4, wherein Perform grid connection suppression verification on the first optimized grid connection decision based on the grid connection power fluctuation depth threshold to obtain the first grid connection suppression verification result, including: Based on the grid connection abnormal photovoltaic unit, perform grid connection power fluctuation depth prediction according to the first optimized grid connection decision to obtain the first decision grid connection fluctuation depth coefficient; Judge whether the first decision grid connection fluctuation depth coefficient is less than the grid connection power fluctuation depth threshold; If the first decision grid connection fluctuation depth coefficient is less than the grid connection power fluctuation depth threshold, the output first grid connection suppression verification result is passed; If the first decision grid connection fluctuation depth coefficient is greater than or equal to the grid connection power fluctuation depth threshold, the output first grid connection suppression verification result is not passed.

6. The method according to claim 4, characterized in that, When the first grid connection suppression verification result is not passed, perform optimization of grid connection power fluctuation suppression on the first optimized grid connection decision to establish a grid connection decision optimization space; Randomly extract a second optimized grid connection decision according to the grid connection decision optimization space; Perform grid connection suppression verification on the second optimized grid connection decision based on the grid connection power fluctuation depth threshold to obtain a second grid connection suppression verification result; When the second grid connection suppression verification result is passed, optimize the photovoltaic grid connection scheme according to the second optimized grid connection decision to generate the first optimized photovoltaic grid connection scheme.

7. The method according to claim 1, wherein Based on the grid connection abnormal affected photovoltaic units and the abnormal affected risk prediction result, optimize the grid connection abnormal affected risk of the first optimized photovoltaic grid connection scheme according to the second grid connection fluctuation suppression decision module to obtain a second optimized photovoltaic grid connection scheme, including: Perform grid connection decision recognition on the first optimized photovoltaic grid connection scheme based on the grid connection abnormal affected photovoltaic units to obtain a first grid connection decision recognition result; Input the first grid connection decision recognition result and the abnormal affected risk prediction result into the second grid connection fluctuation suppression decision module to obtain a first grid connection decision optimization result; Optimize the first optimized photovoltaic grid connection scheme according to the first grid connection decision optimization result and output the second optimized photovoltaic grid connection scheme.

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