Cooperative photovoltaic power generation grid-connected control method, system, equipment and medium

Abstract

The invention discloses a cooperative photovoltaic power generation grid-connected control method, system and device and a medium, and relates to the field of photovoltaic power generation. The method comprises the following steps: acquiring environmental parameters and historical power generation data of each photovoltaic power station in a cluster, and predicting an optimal power generation state of each photovoltaic power station in future preset time according to the environmental parameters and the historical power generation data; determining an output power target value and a voltage target value of the target photovoltaic power station according to the optimal power generation state; adjusting a maximum power point tracking control parameter of the target photovoltaic power station according to the output power target value and the voltage target value, and controlling the target photovoltaic power station to generate power according to the maximum power point tracking control parameter; predicting the power demand in the future preset time according to the current demand of the power grid, calculating the difference between the overall generating capacity of the cluster and the power demand, and controlling the energy storage system to output or store electric energy according to the difference. By implementing the technical scheme provided by the invention, the effect of improving the photovoltaic power generation efficiency is achieved.

Description

Technical Field

[0001] This application relates to the technical field of photovoltaic power generation, and particularly relates to a control method, system, device, and medium for collaborative photovoltaic power generation grid connection. Background Art

[0002] Photovoltaic power generation is an important part of clean energy and has been widely used globally. With the continuous development of photovoltaic technology, photovoltaic power generation systems have played an important role in improving energy utilization efficiency and reducing environmental pollution. However, photovoltaic power generation systems are affected by various factors, such as weather changes, geographical locations, and equipment performance, which pose challenges to the stability and reliability of photovoltaic power generation systems.

[0003] Currently, in order to address the common volatility problems in photovoltaic power generation systems, a power generation prediction model is usually adopted to improve the stability and efficiency of the system. However, existing power generation prediction models often rely on a single data source and lack comprehensive consideration of multi-dimensional environmental parameters, resulting in low prediction accuracy. In addition, in large-scale photovoltaic power generation clusters, the coordination control mechanism between stations is not perfect, making it difficult to achieve the overall optimal power generation effect.

[0004] Therefore, there is an urgent need for a control method that can effectively coordinate the power generation of each station. Summary of the Invention

[0005] This application provides a control method, system, device, and medium for collaborative photovoltaic power generation grid connection, which can improve power generation efficiency, optimize control strategies, enhance grid stability and response capabilities, and promote the utilization and sustainable development of renewable energy.

[0006] In the first aspect of this application, a control method for collaborative photovoltaic power generation grid connection is provided, which is applied to a photovoltaic power generation control platform. The method includes: Obtain the environmental parameters and historical power generation data of each photovoltaic power generation station in the cluster. The environmental parameters include light intensity, temperature, wind speed, humidity, and cloud amount. Predict the optimal power generation state of each photovoltaic power generation station within a preset future time according to the environmental parameters and the historical power generation data; Determine the output power target value and voltage target value of the target photovoltaic power generation station according to the optimal power generation state. The target photovoltaic power generation station is any one of the photovoltaic power generation stations in the cluster; Adjust the maximum power point tracking control parameters of the target photovoltaic power generation station according to the output power target value and the voltage target value, and control the target photovoltaic power generation station to generate power according to the maximum power point tracking control parameters; Predict the power demand within the future preset time according to the current demand of the power grid, calculate the difference between the overall power generation of the cluster and the power demand, and control the energy storage system to output or store electric energy according to the difference.

[0007] Optionally, the predicting the optimal power generation state of each of the photovoltaic power generation stations within the future preset time according to the environmental parameters and the historical power generation data includes: Determine the preset relationship between the meteorological coefficient and the power generation according to the historical power generation data, and determine the current meteorological coefficient according to the environmental parameters; Determine the current power generation according to the preset relationship and the current meteorological coefficient, and determine the optimal power generation state according to the current power generation.

[0008] Optionally, the determining the preset relationship between the meteorological coefficient and the power generation according to the historical power generation data, and determining the current meteorological coefficient according to the environmental parameters includes: Use a machine learning algorithm to train the historical power generation data and the corresponding meteorological coefficients to establish a preset relationship model between the meteorological coefficient and the power generation; Analyze the historical power generation data and the environmental parameter data, determine the influence degree of each environmental parameter in the environmental parameter data on the power generation, and determine the weight of each environmental parameter according to the influence degree; Normalize each current environmental parameter, and perform weighted summation on the normalized current environmental parameters to obtain the current meteorological coefficient.

[0009] Optionally, the determining the current power generation according to the preset relationship and the current meteorological coefficient, and determining the optimal power generation state according to the current power generation includes: Input the current meteorological coefficient into the preset relationship model to obtain the predicted power generation, and adjust the predicted power generation according to the actual power generation efficiency of the cluster; Determine the optimal power generation state according to the adjusted predicted power generation, the rated power of each photovoltaic power generation site, and the state of the energy storage system.

[0010] Optionally, the adjusting the maximum power point tracking control parameters of the target photovoltaic power generation site according to the output power target value and the voltage target value includes: Real-time monitor the current output power and the current voltage of the target photovoltaic power generation site, make a first comparison between the current output power and the output power target value, and make a second comparison between the current voltage and the voltage target value; Adjust the maximum power point tracking control parameters according to the first comparison result and the second comparison result to change the output power and voltage of the target photovoltaic power generation site, where the maximum power point tracking control parameters include the duty cycle and the voltage adjustment step size.

[0011] Optionally, calculating the difference between the overall power generation of the cluster and the power demand, and controlling the energy storage system to output or store electrical energy according to the difference includes: When the overall power generation of the cluster is greater than the power demand, calculate the difference as the excess power; Control the energy storage system to receive and store the excess power until the energy storage system reaches the storage capacity limit or the excess power is completely stored; When the overall power generation of the cluster is less than the power demand, calculate the difference as the deficit power; Control the energy storage system to release the stored electrical energy to supplement the deficit power until the remaining power of the energy storage system is lower than a preset safety threshold or the power demand is met.

[0012] Optionally, controlling the energy storage system to receive and store the excess power until the energy storage system reaches the storage capacity limit or the excess power is completely stored includes: Calculate the evaluation scores of each photovoltaic power generation site according to the current power generation efficiency, maintenance status and historical contribution degree, and determine a preset number of candidate photovoltaic power generation sites according to the evaluation scores; When the power reserve of the energy storage system reaches the threshold, calculate the total output power amount that needs to be reduced according to the difference and the remaining space of the energy storage power reserve of the energy storage system; Lower the output power of the candidate photovoltaic power generation sites according to the total output power amount that needs to be reduced.

[0013] In the second aspect of the present application, a control system for collaborative photovoltaic power generation and grid connection is provided, including an acquisition module, a target module, an adjustment module and an execution module, where: The acquisition module is configured to obtain the environmental parameters and historical power generation data of each photovoltaic power generation site in the cluster. The environmental parameters include light intensity, temperature, wind speed, humidity and cloud cover, and predict the best power generation state of each photovoltaic power generation station within a preset future time according to the environmental parameters and the historical power generation data; The target module is configured to determine the output power target value and voltage target value of the target photovoltaic power generation site according to the best power generation state, where the target photovoltaic power generation site is any one of the photovoltaic power generation sites in the cluster; An adjustment module, configured to adjust the maximum power point tracking control parameters of the target photovoltaic power generation site according to the output power target value and the voltage target value, and control the target photovoltaic power generation site to generate electricity according to the maximum power point tracking control parameters; An execution module, configured to predict the power demand within the future preset time according to the current demand of the power grid, calculate the difference between the overall power generation of the cluster and the power demand, and control the energy storage system to output or store electric energy according to the difference.

[0014] In a third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, both the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory, so that the electronic device executes the method described in any one of the above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method described in any one of the above is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By obtaining the environmental parameters (such as light intensity, temperature, wind speed, humidity, and cloud cover) and historical power generation data of each photovoltaic power generation site in the cluster, the best power generation state of each photovoltaic power generation station within the future preset time can be predicted more accurately. This prediction helps the photovoltaic power generation station to make preparations for power generation in advance, optimize its operation strategy, and thus improve the overall power generation efficiency; 2. According to the predicted best power generation state, an output power target value and a voltage target value are determined for each target photovoltaic power generation site. This refined control strategy can ensure the more stable operation of the photovoltaic power generation site, reduce the power generation fluctuations caused by environmental fluctuations, and improve the power quality; 3. By adjusting the maximum power point tracking control parameters of the target photovoltaic power generation site, the MPPT control can be optimized according to the real-time situation, so that the photovoltaic power generation site can maintain the best power generation efficiency under different light and temperature conditions. This helps to reduce energy waste and improve the power generation efficiency; 4. Predict the power demand within a preset future time based on the current demand of the power grid, and calculate the difference between the overall power generation of the cluster and the power demand. Such prediction and calculation help the photovoltaic power generation cluster adjust its power generation strategy in advance to meet the power demand of the power grid. When the power generation exceeds the demand, the excess electrical energy can be stored by controlling the energy storage system; when the power generation is insufficient, the energy storage system can release electrical energy to supplement the grid demand. This strategy helps enhance the stability and response ability of the power grid, reducing grid fluctuations and the risk of power outages; 5. Through the coordinated control of multiple photovoltaic power generation sites, the efficient utilization of renewable energy is achieved. By optimizing the power generation strategy, improving the power generation efficiency, and reducing energy waste, it helps promote the sustainable development of renewable energy, reduce the dependence on traditional energy, and lower carbon emissions and environmental pollution. Description of the Drawings

[0017] Figure 1 is a schematic flowchart of the control method for collaborative photovoltaic power generation grid connection disclosed in the embodiments of the present application; Figure 2 is a schematic diagram of the modules of the control system for collaborative photovoltaic power generation grid connection disclosed in the embodiments of the present application; Figure 3 is a schematic diagram of the structure of an electronic device disclosed in the embodiments of the present application.

[0018] Description of the reference numerals: 201, acquisition module; 202, target module; 203, adjustment module; 204, execution module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0019] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0020] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0021] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0022] This embodiment discloses a control method for collaborative photovoltaic power generation and grid connection, which is applied to a photovoltaic power generation control platform. Figure 1 It is a schematic flowchart of the control method for collaborative photovoltaic power generation and grid connection disclosed in the embodiments of the present application, as Figure 1 shown. The method includes the following steps: S110. Obtain the environmental parameters and historical power generation data of each photovoltaic power generation site in the cluster. The environmental parameters include light intensity, temperature, wind speed, humidity, and cloud cover. Predict the optimal power generation state of each photovoltaic power generation station within a preset future time according to the environmental parameters and the historical power generation data; Light intensity is a key factor affecting the power generation efficiency of photovoltaic power generation. The stronger the light, the higher the current and voltage generated by the photovoltaic panels usually are, and thus the higher the power generation efficiency. The power generation efficiency of photovoltaic panels is affected by temperature. Generally speaking, as the temperature rises, the power generation efficiency of photovoltaic panels will decrease. Wind speed may generate certain mechanical stress on the photovoltaic panels, and at the same time, it will also affect the temperature distribution around the photovoltaic power station, thereby affecting the power generation efficiency. Humidity also has a certain impact on the power generation efficiency of photovoltaic panels. Especially in a high-humidity environment, a water film may form on the surface of the photovoltaic panels, affecting the light absorption and conversion efficiency. The amount of cloud cover directly affects the light intensity, thereby affecting the photovoltaic power generation efficiency. The more cloud cover, the weaker the light intensity and the lower the power generation efficiency. Historical power generation data includes key indicators such as the power generation volume and power generation efficiency of each photovoltaic power generation site over a past period of time. These data are of great significance for analyzing the power generation rules of photovoltaic power generation sites and optimizing power generation strategies. Through the collected environmental parameters and historical power generation data, the correlation between the power generation efficiency of photovoltaic power generation sites and environmental parameters can be analyzed. For example, the specific influence degrees of factors such as light intensity, temperature, wind speed, humidity, and cloud cover on the power generation efficiency can be analyzed. At the same time, the power generation rules of photovoltaic power generation sites at different time periods can also be analyzed, such as the daily power generation peak value and the night power generation volume. Based on the collected data and analysis results, a prediction model can be established to predict the optimal power generation state of each photovoltaic power generation site within a future preset time. The prediction model can adopt algorithms such as machine learning and deep learning, and optimize the model parameters by training a large amount of historical data to improve the prediction accuracy. The prediction model will output the prediction results of the optimal power generation state of each photovoltaic power generation site within a future preset time. These results can include key indicators such as power generation volume prediction and power generation efficiency prediction. According to the prediction results, the power generation strategy of the photovoltaic power generation site can be optimized. For example, when it is predicted that the light intensity will be weak in a future period of time, the tilt angle of the photovoltaic panels can be adjusted in advance or other measures can be taken to improve the power generation efficiency. The prediction results can also be used to optimize the operation and maintenance management of photovoltaic power generation sites. For example, when it is predicted that the power generation efficiency of a certain photovoltaic power generation site will drop significantly in a future period of time, maintenance personnel can be arranged in advance for inspection and maintenance to avoid further reduction of the power generation efficiency. By predicting the power generation state within a future preset time, the economic benefits of photovoltaic power generation sites can also be evaluated. For example, the power generation volume and power generation income in a future period of time can be calculated, providing an important reference for the investment decision of photovoltaic power generation sites.

[0023] Optionally, the predicting the optimal power generation state of each of the photovoltaic power generation stations within a future preset time according to the environmental parameters and the historical power generation data includes: Determining a preset relationship between a meteorological coefficient and the power generation volume according to the historical power generation data, and determining the current meteorological coefficient according to the environmental parameters; Determine the current power generation according to the preset relationship and the current meteorological coefficient, and determine the optimal power generation state according to the current power generation.

[0024] Collect the historical power generation data of the photovoltaic power generation site, including the power generation amount and the power generation time, etc. Organize these data in chronological order to form time series data for subsequent analysis. Extract the meteorological coefficient at each time point according to the meteorological conditions in the historical power generation data. The meteorological coefficient is a comprehensive index that can reflect the influence degree of the current meteorological conditions on the photovoltaic power generation efficiency. The specific calculation method of the meteorological coefficient may vary according to the actual situation, but usually multiple factors such as light intensity, temperature, wind speed, humidity, and cloud cover need to be considered. Use statistical methods such as machine learning and regression analysis to establish a preset relationship model between the meteorological coefficient and the power generation amount according to the historical power generation data and the corresponding meteorological coefficients. This model can describe the change of the power generation amount of the photovoltaic power generation site under different meteorological conditions. Calculate the current meteorological coefficient according to the real-time obtained environmental parameters by using the previously determined meteorological coefficient calculation method. Input the current meteorological coefficient into the previously established preset relationship model between the meteorological coefficient and the power generation amount to predict the current power generation amount. According to the predicted current power generation amount, combined with factors such as the rated power of the photovoltaic power generation site, the state of the energy storage system, and the current demand of the power grid, determine the optimal power generation state within a preset future time. The optimal power generation state may include the target value of the power generation amount, the optimization strategy of the power generation efficiency, etc.

[0025] By analyzing historical power generation data and corresponding meteorological coefficients (such as comprehensive indicators of light intensity, temperature, wind speed, etc.), a preset relationship model between meteorological coefficients and power generation can be established. This model can capture the influence law of environmental parameters on the photovoltaic power generation efficiency, so as to more accurately predict the power generation in specific meteorological conditions in the future. When the preset relationship between meteorological coefficients and power generation is established, the current meteorological coefficient can be calculated according to the real-time environmental parameters, and the current power generation can be predicted accordingly. This real-time prediction ability enables the photovoltaic power station to dynamically adjust its power generation strategy according to weather changes, such as adjusting the tilt angle of photovoltaic panels, turning on or off some power generation units, etc., to maximize the power generation efficiency. By real-time monitoring of environmental parameters and quickly calculating the current meteorological coefficient, the system can quickly respond to weather changes and timely adjust the power generation status. This quick response ability helps to reduce the power generation fluctuations caused by sudden weather changes and improve the stability and reliability of the power grid. The prediction results not only provide an important reference for the daily operation of the photovoltaic power station, but also provide data support for higher-level energy management and dispatching decisions. For example, during the peak power demand period, the output power of the photovoltaic power station can be adjusted in advance according to the prediction results to meet the grid demand; during the period of power surplus, the charging strategy of the energy storage system can be optimized to reduce energy waste. By accurately predicting power generation and optimizing the power generation strategy, the photovoltaic power station can more effectively utilize solar energy resources, improve power generation efficiency and economic benefits. At the same time, reducing the power generation fluctuations caused by weather changes also helps to reduce operation and maintenance costs and power losses.

[0026] Optionally, the determining the preset relationship between meteorological coefficients and power generation according to the historical power generation data and determining the current meteorological coefficient according to the environmental parameters includes: Using a machine learning algorithm to train the historical power generation data and the corresponding meteorological coefficients to establish a preset relationship model between meteorological coefficients and power generation; Analyzing the historical power generation data and environmental parameter data, determining the influence degree of each environmental parameter in the environmental parameter data on power generation, and determining the weight of each environmental parameter according to the influence degree; Normalizing each current environmental parameter and performing weighted summation on the normalized current environmental parameters to obtain the current meteorological coefficient.

[0027] Collect historical power generation data and corresponding meteorological coefficients. The historical power generation data includes the power generation records of each photovoltaic power generation site over a past period of time, while the meteorological coefficients are comprehensive indicators calculated based on environmental parameters (such as light intensity, temperature, wind speed, humidity, and cloud cover). Preprocess the historical power generation data and meteorological coefficients, including data cleaning, missing value handling, outlier detection, etc. At the same time, perform feature extraction and transformation on the meteorological coefficients as needed to better reflect the impact of environmental parameters on power generation. Use machine learning algorithms (such as linear regression, decision tree, random forest, neural network, etc.) to train the historical power generation data and meteorological coefficients. During the training process, the algorithm will learn the mapping relationship between the meteorological coefficients and power generation, and construct a preset relationship model. Evaluate the trained model through methods such as cross-validation and holdout method to verify its prediction performance. The evaluation metrics include prediction accuracy, mean squared error (MSE), root mean squared error (RMSE), etc. Analyze the historical power generation data and environmental parameter data, and determine the influence degree of each environmental parameter on power generation through methods such as correlation analysis and feature importance assessment. These analyses can understand the environmental parameters that have a significant impact on power generation and their relative importance. Assign a weight to each current environmental parameter according to the influence degree of the environmental parameter on power generation. The size of the weight reflects the relative importance of the environmental parameter in the calculation of the meteorological coefficient. The determination of the weight can be based on statistical analysis, expert experience, or the results of machine learning algorithms. Normalize each current environmental parameter to eliminate the dimensional difference and numerical range difference between different parameters. The normalization methods include min-max normalization, Z-score normalization, etc. Perform weighted summation on the normalized current environmental parameters to obtain the current meteorological coefficient. The process of weighted summation is to multiply the normalized value of each environmental parameter by its corresponding weight, and then add all the products to get the final result.

[0028] Training with historical power generation data and corresponding meteorological coefficients using machine learning algorithms can automatically capture the complex relationship between the two and establish a more accurate and generalized preset relationship model. Machine learning algorithms can process large amounts of data and learn the underlying patterns and features of the data, thereby improving the accuracy and robustness of the prediction model. By analyzing historical power generation data and environmental parameter data, the influence degree of each environmental parameter on power generation can be determined, and the weight of each environmental parameter can be determined accordingly. The determination of the weight enables the prediction model to pay more attention to the environmental parameters that have a greater impact on power generation, thereby improving the accuracy of the prediction. Normalizing each current environmental parameter can eliminate the differences in units and dimensions between different environmental parameters, enabling them to be compared and calculated on the same scale. The normalization process can also limit the values of the environmental parameters within a certain range, thereby avoiding the influence of extreme values on the prediction results. Weighted summation of the normalized current environmental parameters can obtain a meteorological coefficient that comprehensively reflects the current environmental conditions. This meteorological coefficient can be used as an input variable for the prediction model to predict the power generation within a preset future time. By obtaining environmental parameter data in real time and calculating the current meteorological coefficient, real-time prediction of future power generation can be achieved. According to the prediction results, the photovoltaic power station can timely adjust its power generation strategy, such as adjusting the tilt angle of the photovoltaic panels, turning on or off some power generation units, etc., to maximize the power generation efficiency. Accurate prediction and timely adjustment can reduce the power generation fluctuations caused by weather changes and improve the energy utilization efficiency. This helps to reduce the operation and maintenance costs and power losses and improve the economic benefits of the photovoltaic power station.

[0029] Optionally, the determining the current power generation according to the preset relationship and the current meteorological coefficient, and determining the optimal power generation state according to the current power generation includes: Inputting the current meteorological coefficient into the preset relationship model to obtain the predicted power generation, and adjusting the predicted power generation according to the actual power generation efficiency of the cluster; Determining the optimal power generation state according to the adjusted predicted power generation, the rated power of each photovoltaic power generation site, and the state of the energy storage system.

[0030] The preset relationship model is obtained by training historical power generation data and corresponding meteorological coefficients through machine learning algorithms, and can reflect the complex relationship between meteorological coefficients and power generation. Inputting the current meteorological coefficients into the preset relationship model can obtain a preliminary predicted power generation. This predicted power generation is obtained based on the statistical laws of current environmental parameters and historical data. Due to differences in factors such as the equipment status and operation and maintenance levels of each photovoltaic power generation site in the cluster, the actual power generation efficiency will also vary. To more accurately reflect the actual power generation capacity of the cluster, it is necessary to adjust the predicted power generation according to the actual power generation efficiency of the cluster. This can be achieved by comparing historical power generation data and actual power generation data, calculating an adjustment coefficient, and then using this adjustment coefficient to correct the predicted power generation. Each photovoltaic power generation site has its rated power generation capacity, that is, the maximum power that can be continuously output under design conditions. When determining the optimal power generation state, the rated power of each site needs to be considered to ensure that the power generation allocation does not exceed the carrying capacity of the site, thereby avoiding equipment damage or safety accidents. Energy storage systems (such as battery energy storage systems) play an important role in photovoltaic power generation, capable of smoothing the fluctuations of photovoltaic power generation and improving the stability and reliability of the power system. When determining the optimal power generation state, the current state of the energy storage system needs to be considered, including the energy storage capacity, charge / discharge rate, etc. If the energy storage system is already close to full charge or empty charge state, then the power generation strategy needs to be adjusted to avoid excessive stress on the energy storage system or waste of energy. After considering the predicted power generation, the rated power of each photovoltaic power generation site, and the state of the energy storage system, an optimal power generation state can be comprehensively determined. This optimal power generation state should be able to maximize the power generation efficiency of the cluster while ensuring the safe operation of each photovoltaic power generation site and the energy storage system.

[0031] Inputting the current meteorological coefficient into the preset relationship model can obtain the predicted power generation based on historical data and machine learning algorithms. This method can capture the complex relationship between environmental parameters and power generation, improving the accuracy of prediction. The predicted power generation may be affected by various factors, such as equipment aging, maintenance status, dust accumulation, etc., which will cause a difference between the actual power generation efficiency and the theoretical value. Therefore, adjusting the predicted power generation according to the actual power generation efficiency of the cluster can make the prediction result closer to the actual situation and improve the practicality of the prediction. Determining the optimal power generation state not only depends on the predicted power generation, but also needs to consider the rated power of each photovoltaic power generation site and the state of the energy storage system. By comprehensively considering these factors, a more reasonable power generation strategy and resource allocation plan can be formulated. Accurate power generation prediction and reasonable power generation strategy can enable the photovoltaic power generation station to make full use of solar energy resources and reduce energy waste. At the same time, by optimizing resource allocation, the operation and maintenance costs can be reduced and the economic benefits can be improved. As an important part of the power grid, the optimization of the power generation state of the photovoltaic power generation station helps the dispatching and stable operation of the power grid. By predicting the power generation and adjusting the power generation strategy accordingly, the impact and fluctuation on the power grid can be reduced, and the stability and reliability of the power grid can be improved.

[0032] S120. Determine the target output power value and target voltage value of the target photovoltaic power generation site according to the optimal power generation state, where the target photovoltaic power generation site is any one of the photovoltaic power generation sites in the cluster; The "cluster" mentioned here refers to an overall system composed of multiple photovoltaic power generation sites. Each photovoltaic power generation site has the ability to generate electricity and may be equipped with supporting facilities such as energy storage devices and inverters. The optimal power generation state is the result of a comprehensive evaluation based on a series of complex factors (such as historical power generation data, environmental parameters, actual power generation efficiency, rated power, energy storage system status, etc.), aiming to maximize the power generation efficiency and economic benefits of the entire cluster or individual sites. According to the optimal power generation state, an output power target value is set for each target site. This value should be able to reflect the optimal power generation level under the current environmental conditions, taking into account the actual power generation capacity of the site and the status of the energy storage system. The setting of the output power target value helps to ensure that the site can operate efficiently without overloading. In addition to the output power, the voltage is also an important factor affecting the operation efficiency and safety of photovoltaic power generation sites. Therefore, when setting the output power target value, a voltage target value also needs to be set for each target site. This value should be able to ensure that the site operates in a stable and safe state while meeting the grid's requirements for voltage quality. When the output power target value and the voltage target value are determined, the target site needs to be adjusted accordingly through the control system. This may include adjusting the tilt angle of the photovoltaic panels, regulating the output voltage and current of the inverter, etc. After the adjustment is completed, the actual power generation situation of the target site needs to be monitored and evaluated. By comparing the differences between the actual power generation and the output power target value, and between the actual voltage and the voltage target value, it can be judged whether the adjustment is effective, and further optimization and adjustment can be carried out accordingly. Since the environmental conditions and site status will change continuously, the output power target value and the voltage target value also need to be continuously optimized and adjusted according to the actual situation. This helps to ensure that the photovoltaic power generation site always maintains the optimal power generation state, thereby maximizing its power generation efficiency and economic benefits.

[0033] S130. Adjust the maximum power point tracking control parameters of the target photovoltaic power generation site according to the output power target value and the voltage target value, and control the target photovoltaic power generation site to generate electricity according to the maximum power point tracking control parameters; The output power target value and the voltage target value are obtained based on the analysis of the optimal power generation state of the photovoltaic power generation sites in the cluster, aiming to ensure that the sites can operate under optimal conditions, improve power generation efficiency and economic benefits. Maximum power point tracking (MPPT) control is a commonly used control strategy in photovoltaic power generation systems. It adjusts the operating point of the photovoltaic panels to always work at the maximum power state, thus maximizing the utilization of solar energy resources. The MPPT control parameters refer to various parameters that affect the operating point of the photovoltaic panels, such as voltage, current, power, etc. According to the previously set output power target value and voltage target value, the corresponding MPPT control parameters need to be calculated. These parameters should ensure that the photovoltaic panels operate at the target output power and voltage while maintaining high efficiency. When the MPPT control parameters are determined, the MPPT controller of the target site needs to be adjusted accordingly through the control system. This may include adjusting the voltage reference value, current limit, power limit, etc. After adjusting the MPPT control parameters, the control system will start to control the power generation of the target site according to these parameters. This includes monitoring the output voltage and current of the photovoltaic panels, and adjusting the output power through devices such as inverters to ensure that the site always operates near the set output power target value and voltage target value. During the implementation of power generation control, the control system needs to continuously monitor the actual power generation situation of the site and compare it with the set target value. If a deviation is found between the actual value and the target value, the MPPT control parameters need to be fine-tuned to optimize the power generation effect. This feedback and optimization process is a continuous process, aiming to ensure that the site always maintains the best power generation state.

[0034] Optionally, adjusting the maximum power point tracking control parameters of the target photovoltaic power generation site according to the output power target value and the voltage target value includes: Real-time monitoring of the current output power and current voltage of the target photovoltaic power generation site, making a first comparison between the current output power and the output power target value, and making a second comparison between the current voltage and the voltage target value; Adjusting the maximum power point tracking control parameters according to the first comparison result and the second comparison result to change the output power and voltage of the target photovoltaic power generation site, where the maximum power point tracking control parameters include the duty cycle and the voltage adjustment step size.

[0035] Monitor the current output power and current voltage of the target photovoltaic power generation site in real time. This is usually achieved through sensors or measurement devices installed on the site, which can capture the operation data of the site in real time. Compare the current output power with a preset output power target value. The purpose of this comparison is to determine whether the current output power of the site has reached the target value. If the current output power is lower than the target value, it means that the site needs to increase the output power; if the current output power is higher than the target value, it may be necessary to reduce the output power. Compare the current voltage with a preset voltage target value. The purpose of this comparison is to determine whether the current voltage of the site is stable and meets the grid requirements. If the current voltage deviates from the target value, adjustments may be needed to ensure the voltage quality. The MPPT control parameters include the duty cycle and the voltage adjustment step size, etc. These parameters directly affect the operating point of the photovoltaic panels and the output of the inverter. The duty cycle refers to the ratio of the time when the switch is on to the total cycle time in one cycle, which determines the output voltage and current of the inverter. The voltage adjustment step size refers to the value of the voltage increased or decreased each time the voltage is adjusted. According to the results of the first comparison and the second comparison, it is necessary to adjust the MPPT control parameters to change the output power and voltage of the target photovoltaic power generation site. If the current output power is lower than the target value, the output power can be increased by increasing the duty cycle or adjusting other relevant parameters. If the current voltage deviates from the target value, the voltage can be stabilized by adjusting the voltage adjustment step size or relevant parameters. This adjustment process is a dynamic process and needs to be continuously adjusted and optimized according to the real-time monitoring data. By continuously comparing the current value with the target value and adjusting the MPPT control parameters accordingly, it can be ensured that the site always operates in the optimal state.

[0036] By monitoring the current output power and voltage in real time and comparing them with the target values, the MPPT control parameters can be dynamically adjusted to keep the photovoltaic power generation site operating at the optimal state all the time. This dynamic adjustment can ensure that the photovoltaic panels always operate at the maximum power state, thus significantly improving the power generation efficiency. Precisely controlling the power generation process of the site, maximizing the utilization of solar energy resources, and reducing energy waste. This helps to improve the energy utilization rate of the entire photovoltaic power generation system and increase the power generation. By adjusting MPPT control parameters such as the voltage adjustment step, the output voltage can be stabilized and kept near the preset voltage target value. This helps to ensure that the power quality of the photovoltaic power generation site meets the grid requirements and reduces power quality problems caused by voltage fluctuations. Due to the stable voltage, the corresponding current will also be stable, which helps to protect the subsequent power equipment and grid safety. By precisely adjusting the MPPT control parameters, the overall performance of the photovoltaic power generation system can be optimized, and the stability and reliability of the system can be improved. This helps to reduce the system failure rate and lower the operation and maintenance costs. The stable output voltage and current help to reduce the impact and damage to key equipment such as photovoltaic panels and inverters, thus prolonging the service life of the equipment. By improving the power generation efficiency and reducing energy waste, the operation and maintenance costs of the photovoltaic power generation site can be reduced. At the same time, due to the extension of the equipment life, the frequency of equipment replacement and repair is also reduced, further reducing the costs.

[0037] S140. Predict the power demand within the future preset time according to the current demand of the power grid, calculate the difference between the overall power generation of the cluster and the power demand, and control the energy storage system to output or store electric energy according to the difference.

[0038] Collect the current power demand data of the power grid, which includes information such as real-time power load and power consumption trend. Use prediction algorithms or models (such as time series analysis, machine learning, etc.) to predict the power demand within the future preset time based on the current demand data. This prediction time window can be set according to actual needs, such as several hours, one day or several days. Calculate or predict the overall power generation of all photovoltaic power generation sites in the cluster. This is usually estimated based on historical power generation data, weather forecasts (such as light intensity, temperature, etc.) and the power generation efficiency of the photovoltaic power station. Compare the predicted power demand with the overall power generation of the cluster and calculate the difference between the two. This difference reflects the imbalance degree of power supply and demand within the future preset time. Energy storage systems play an important role in the smart grid. They can release electric energy during peak power demand and store electric energy during low power demand. According to the calculated difference between the power demand and power generation, formulate the control strategy of the energy storage system. If it is predicted that the power demand will exceed the power generation (i.e., the difference is positive), then control the energy storage system to output electric energy to supplement the power supply; if it is predicted that the power generation will exceed the power demand (i.e., the difference is negative), then control the energy storage system to store the excess electric energy for later use.

[0039] Optionally, calculating the difference between the overall power generation of the cluster and the power demand, and controlling the energy storage system to output or store electric energy according to the difference includes: When the overall power generation of the cluster is greater than the power demand, calculating the difference as surplus power; Controlling the energy storage system to receive and store the surplus power until the energy storage system reaches the upper limit of the storage capacity or the surplus power is completely stored; When the overall power generation of the cluster is less than the power demand, calculating the difference as deficit power; Controlling the energy storage system to release the stored electric energy to supplement the deficit power until the remaining power of the energy storage system is lower than a preset safety threshold or the power demand is met.

[0040] Collect the real-time power generation data of each photovoltaic power generation site in the cluster, and combine the current power demand of the power grid to predict the power demand in the next period of time. This is usually based on a comprehensive analysis of factors such as historical data, weather forecasts, and equipment status. Compare the overall power generation of the cluster with the predicted power demand, and calculate the difference between the two. This difference reflects the degree of imbalance between power supply and demand. When the overall power generation of the cluster is greater than the power demand, the difference is the surplus power. This means that the power generated by the cluster exceeds the current demand of the power grid. At this time, it is necessary to control the energy storage system to receive and store this surplus power. The energy storage system can be in various forms such as battery energy storage, pumped-storage energy storage, and compressed-air energy storage. When the energy storage system receives and stores the surplus power, it is necessary to monitor its storage capacity in real time. When the upper limit of the storage capacity is reached or the surplus power is completely stored, the energy storage system will stop receiving new power to avoid overload or damage. When the overall power generation of the cluster is less than the power demand, the difference is the deficit power. This means that the power generated by the cluster cannot meet the current demand of the power grid. At this time, it is necessary to control the energy storage system to release the stored electric energy to supplement the deficit power. The energy storage system converts the stored direct current into alternating current through devices such as inverters and transmits it to the power grid. During the process of the energy storage system releasing electric energy, it is necessary to monitor the remaining power in real time. When the remaining power is lower than the preset safety threshold, the energy storage system will stop discharging to avoid affecting its subsequent use or causing equipment damage due to power exhaustion.

[0041] When the overall power generation of the cluster is greater than the power demand, the excess power is determined by calculating the difference, and the energy storage system is controlled to receive and store this power. This avoids waste of energy and ensures the maximum utilization of renewable energy. When the overall power generation of the cluster is less than the power demand, the energy storage system can release the stored electrical energy to supplement the shortage. This helps to balance the power supply and demand, reduce the dependence on traditional energy sources, and improve the flexibility of energy management. By calculating the difference between the overall power generation of the cluster and the power demand in real time and controlling the output or storage of the energy storage system accordingly, dynamic regulation of the power grid can be achieved. This helps to cope with fluctuations in power demand, reduce the load pressure on the power grid, and improve the stability and reliability of the power grid. In the event of a power grid failure or emergency, the energy storage system can quickly release the stored electrical energy to cope with power shortages and ensure the normal operation of critical equipment and services. By storing excess power and releasing it when needed, the energy storage system can reduce the demand for purchasing traditional energy sources and lower the power cost. At the same time, the use of the energy storage system can also reduce power waste and further improve economic efficiency. By precisely controlling the output or storage of the energy storage system, the value of renewable energy can be maximized, the energy utilization efficiency can be improved, and greater economic benefits can be brought to energy suppliers and consumers.

[0042] Optionally, controlling the energy storage system to receive and store the excess power until the energy storage system reaches the upper limit of its storage capacity or the excess power is completely stored includes: Calculating the evaluation scores of each photovoltaic power generation site according to the current power generation efficiency, maintenance status and historical contribution degree, and determining a preset number of candidate photovoltaic power generation sites according to the evaluation scores; When the power reserve of the energy storage system reaches the threshold, calculating the total amount of output power that needs to be reduced according to the difference and the remaining space of the energy storage power reserve of the energy storage system; Lowering the output power of the candidate photovoltaic power generation sites according to the total amount of output power that needs to be reduced.

[0043] Consider the current power generation efficiency of each photovoltaic power generation site, that is, the electric energy generated per unit time. A site with high power generation efficiency usually means that it can generate more surplus power. Evaluate the maintenance status of the site, including the degree of equipment aging, failure rate, etc. A well-maintained site is more likely to provide power continuously and stably. Consider the contribution of the site to the power grid over a past period, such as the amount of power supplied and stability. Sites with a high historical contribution may be given priority when the energy storage system is in need. Based on the above factors, calculate an evaluation score for each photovoltaic power generation site, which comprehensively reflects the performance, stability and contribution of the site to the power grid. According to the demand and management strategy of the energy storage system, set a preset quantity, which represents how many photovoltaic power generation sites will be considered as candidates when power generation needs to be reduced. Sort the sites according to the evaluation score from high to low, and select the preset quantity of photovoltaic power generation sites with the highest scores as candidates. These sites will be given priority when reducing power generation. Set a threshold for the power reserve of the energy storage system. When the power reserve reaches or exceeds this threshold, it means that the energy storage system is approaching full load and measures need to be taken to reduce the input. According to the current power reserve and total capacity of the energy storage system, calculate the remaining space, that is, how much more electric energy can be stored. When the power reserve of the energy storage system reaches the threshold, combine the current surplus power (that is, the difference between the overall power generation of the cluster and the power demand) and the remaining space of the energy storage system to calculate the total output power reduction required. According to the calculated total output power reduction required, reduce the output power of the candidate photovoltaic power generation sites proportionally or according to other strategies. This can ensure that the energy storage system will not be overloaded and minimize the impact on the power grid.

[0044] By comprehensively considering factors such as the current power generation efficiency, maintenance status and historical contribution, calculating the evaluation score for each photovoltaic power generation site can more comprehensively and objectively reflect the actual operation situation and potential value of each site. Determining a preset quantity of candidate photovoltaic power generation sites based on the evaluation score helps to select sites with excellent performance and high reliability for output power adjustment when the energy storage system needs to receive and store surplus power, thus ensuring the efficient allocation and utilization of energy. When the power reserve of the energy storage system reaches the threshold, determining the total output power reduction required by calculating the difference and the remaining space of the energy storage system can achieve precise control of the power reserve of the energy storage system and avoid overcharging or insufficient energy storage. Dynamically adjusting the output power of the candidate photovoltaic power generation sites according to the calculated total output power reduction required helps to keep the power reserve of the energy storage system within a reasonable range and ensure the stable operation of the photovoltaic power generation sites at the same time. By adjusting the output power of the photovoltaic power generation sites, the power supply and demand can be effectively balanced, the fluctuation of the power grid load can be reduced, and the stability and reliability of the power grid can be improved. In the event of a power grid failure or emergency, the energy storage system can quickly release the stored electric energy to cope with power shortages. At the same time, by adjusting the output power of the photovoltaic power generation sites, the safe operation of the power grid can be further ensured.

[0045] This embodiment also discloses a control system for collaborative photovoltaic power generation and grid connection. Figure 2 It is a schematic diagram of the modules of the control system for collaborative photovoltaic power generation and grid connection disclosed in the embodiments of the present application. As Figure 2 shown, the system includes a collection module 201, a target module 202, an adjustment module 203, and an execution module 204, where: The collection module 201 is configured to obtain the environmental parameters and historical power generation data of each photovoltaic power generation site in the cluster. The environmental parameters include light intensity, temperature, wind speed, humidity, and cloud cover, and predict the optimal power generation state of each photovoltaic power generation station within a preset future time according to the environmental parameters and the historical power generation data; The target module 202 is configured to determine the output power target value and voltage target value of the target photovoltaic power generation site according to the optimal power generation state. The target photovoltaic power generation site is any one of the photovoltaic power generation sites in the cluster; The adjustment module 203 is configured to adjust the maximum power point tracking control parameters of the target photovoltaic power generation site according to the output power target value and the voltage target value, and control the target photovoltaic power generation site to generate power according to the maximum power point tracking control parameters; The execution module 204 is configured to predict the power demand within the preset future time according to the current demand of the power grid, calculate the difference between the overall power generation of the cluster and the power demand, and control the energy storage system to output or store electric energy according to the difference.

[0046] Optionally, the collection module 201 is configured to: Determine the preset relationship between the meteorological coefficient and the power generation according to the historical power generation data, and determine the current meteorological coefficient according to the environmental parameters; Determine the current power generation according to the preset relationship and the current meteorological coefficient, and determine the optimal power generation state according to the current power generation.

[0047] Optionally, the collection module 201 is configured to: Use a machine learning algorithm to train the historical power generation data and the corresponding meteorological coefficients to establish a preset relationship model between the meteorological coefficient and the power generation; Analyze the historical power generation data and environmental parameter data, determine the influence degree of each environmental parameter in the environmental parameter data on the power generation, and determine the weight of each environmental parameter according to the influence degree; Normalize each current environmental parameter, and perform weighted summation on the normalized current environmental parameters to obtain the current meteorological coefficient.

[0048] Optionally, the acquisition module 201 is configured to: Input the current meteorological coefficient into the preset relationship model to obtain the predicted power generation amount, and adjust the predicted power generation amount according to the actual power generation efficiency of the cluster; Determine the optimal power generation state according to the adjusted predicted power generation amount, the rated power of each photovoltaic power generation site, and the state of the energy storage system.

[0049] Optionally, the adjustment module 203 is configured to: Monitor the current output power and current voltage of the target photovoltaic power generation site in real time, make a first comparison between the current output power and the output power target value, and make a second comparison between the current voltage and the voltage target value; Adjust the maximum power point tracking control parameters according to the first comparison result and the second comparison result to change the output power and voltage of the target photovoltaic power generation site. The maximum power point tracking control parameters include the duty ratio and the voltage adjustment step size.

[0050] Optionally, the execution module 204 is configured to: When the overall power generation of the cluster is greater than the power demand, calculate the difference as the excess power; Control the energy storage system to receive and store the excess power until the energy storage system reaches the storage capacity limit or the excess power is completely stored; When the overall power generation of the cluster is less than the power demand, calculate the difference as the deficit power; Control the energy storage system to release the stored electric energy to supplement the deficit power until the remaining power of the energy storage system is lower than the preset safety threshold or the power demand is met.

[0051] Optionally, the execution module 204 is configured to: Calculate the evaluation score of each photovoltaic power generation site according to the current power generation efficiency, maintenance status and historical contribution degree, and determine a preset number of candidate photovoltaic power generation sites according to the evaluation score; When the power reserve of the energy storage system reaches the threshold, calculate the total output power amount that needs to be reduced according to the difference and the remaining space of the energy storage power reserve of the energy storage system; Lower the output power of the candidate photovoltaic power generation sites according to the total output power amount that needs to be reduced.

[0052] It should be noted that: when the device provided in the above embodiment realizes its functions, only the division of the above-mentioned functional modules is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0053] This embodiment also discloses an electronic device. Referring to Figure 3 , the electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0054] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0055] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0056] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0057] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, as well as calling data stored in the memory 305, it executes various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate one or several combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0058] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As Figure 3 shown, in the memory 305 as a computer storage medium, there may be included an operating system, a network communication module, a user interface module, and an application program for the control method of grid-connected collaborative photovoltaic power generation.

[0059] In Figure 3 the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user to obtain the data input by the user; and the processor 301 can be used to call the application program for the control method of grid-connected collaborative photovoltaic power generation stored in the memory 305. When executed by one or more processors 301, the electronic device executes the method in one or more of the above embodiments.

[0060] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0061] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0062] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect couplings or communication connections of devices or units can be in electrical or other forms.

[0063] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of this application. And the aforementioned memory 305 includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0066] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the disclosure of the specification. This application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A control method for cooperative photovoltaic power generation grid connection, characterized in that: Applied to a photovoltaic power generation control platform, the method comprises: Obtaining environmental parameters and historical power generation data of each photovoltaic power generation site in the cluster, wherein the environmental parameters include light intensity, temperature, wind speed, humidity and cloud cover, and predicting the optimal power generation state of each photovoltaic power station within a preset time in the future based on the environmental parameters and the historical power generation data; Determine an output power target value and a voltage target value of a target photovoltaic power generation site according to the optimal power generation state, wherein the target photovoltaic power generation site is any photovoltaic power generation site in the cluster; adjusting a maximum power point tracking control parameter of the target photovoltaic power generation site according to the output power target value and the voltage target value, and controlling the target photovoltaic power generation site to generate electricity according to the maximum power point tracking control parameter; The power demand within the preset future time is predicted based on the current demand of the power grid, the difference between the overall power generation of the cluster and the power demand is calculated, and the energy storage system is controlled to output or store electric energy based on the difference.

2. The control method for cooperative photovoltaic power generation grid connection according to claim 1, characterized in that: The predicting of the optimal power generation state of each photovoltaic power station within a preset future time according to the environmental parameters and the historical power generation data includes: Determine a preset relationship between a meteorological coefficient and power generation according to the historical power generation data, and determine a current meteorological coefficient according to the environmental parameters; The current power generation is determined according to the preset relationship and the current meteorological coefficient, and the optimal power generation state is determined according to the current power generation.

3. The control method for cooperative photovoltaic power generation grid connection according to claim 2, characterized in that: The determining of the preset relationship between the meteorological coefficient and the power generation according to the historical power generation data and the determining of the current meteorological coefficient according to the environmental parameters include: Using a machine learning algorithm to train the historical power generation data and the corresponding meteorological coefficients to establish a preset relationship model between the meteorological coefficients and the power generation; Analyze the historical power generation data and environmental parameter data, determine the influence degree of each environmental parameter in the environmental parameter data on power generation, and determine the weight of each environmental parameter according to the influence degree; Each current environmental parameter is normalized, and the normalized current environmental parameters are weighted summed to obtain a current meteorological coefficient.

4. The control method for cooperative photovoltaic power generation grid connection according to claim 3, characterized in that: The determining of the current power generation according to the preset relationship and the current meteorological coefficient, and determining the optimal power generation state according to the current power generation includes: Inputting the current meteorological coefficient into the preset relationship model to obtain predicted power generation, and adjusting the predicted power generation according to the actual power generation efficiency of the cluster; The optimal power generation state is determined based on the adjusted predicted power generation, the rated power of each photovoltaic power generation site and the state of the energy storage system.

5. The control method for cooperative photovoltaic power generation grid connection according to claim 1, characterized in that: The step of adjusting the maximum power point tracking control parameters of the target photovoltaic power generation site according to the output power target value and the voltage target value comprises: monitoring the current output power and the current voltage of the target photovoltaic power generation site in real time, performing a first comparison between the current output power and the output power target value, and performing a second comparison between the current voltage and the voltage target value; The maximum power point tracking control parameters are adjusted according to the first comparison result and the second comparison result to change the output power and voltage of the target photovoltaic power generation site, and the maximum power point tracking control parameters include a duty cycle and a voltage adjustment step.

6. The control method for cooperative photovoltaic power generation grid connection according to claim 1, characterized in that: The calculating the difference between the overall power generation of the cluster and the power demand, and controlling the energy storage system to output or store electric energy according to the difference comprises: When the overall power generation of the cluster is greater than the power demand, calculating the difference as excess power; Controlling the energy storage system to receive and store the excess electricity until the energy storage system reaches an upper storage capacity limit or the excess electricity is completely stored; When the overall power generation of the cluster is less than the power demand, the difference is calculated as the power shortage; The energy storage system is controlled to release stored electric energy to supplement the power shortage until the remaining power of the energy storage system is lower than a preset safety threshold or the power demand is met.

7. The control method for cooperative photovoltaic power generation grid connection according to claim 6, characterized in that: The controlling the energy storage system to receive and store the excess electricity until the energy storage system reaches an upper storage capacity limit or the excess electricity is completely stored includes: Calculate the evaluation score of each photovoltaic power generation site according to the current power generation efficiency, maintenance status and historical contribution, and determine a preset number of candidate photovoltaic power generation sites according to the evaluation score; When the power reserve of the energy storage system reaches a threshold, calculating the total output power amount that needs to be reduced according to the difference and the remaining space of the energy storage power reserve of the energy storage system; The output power of the candidate photovoltaic power generation site is lowered according to the amount of total output power that needs to be reduced.

8. A control system for cooperative photovoltaic power generation and grid connection, characterized in that: It includes acquisition module, target module, adjustment module and execution module, among which: A collection module configured to obtain environmental parameters and historical power generation data of each photovoltaic power generation site in the cluster, wherein the environmental parameters include light intensity, temperature, wind speed, humidity and cloud cover, and predict the optimal power generation state of each photovoltaic power station within a preset time in the future based on the environmental parameters and the historical power generation data; a target module configured to determine an output power target value and a voltage target value of a target photovoltaic power generation site according to the optimal power generation state, wherein the target photovoltaic power generation site is any photovoltaic power generation site in the cluster; an adjustment module, configured to adjust a maximum power point tracking control parameter of the target photovoltaic power generation site according to the output power target value and the voltage target value, and control the target photovoltaic power generation site to generate electricity according to the maximum power point tracking control parameter; The execution module is configured to predict the power demand within the preset future time according to the current demand of the power grid, calculate the difference between the overall power generation of the cluster and the power demand, and control the energy storage system to output or store electric energy according to the difference.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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