Optimized scheduling method for water-wind-light complementary power generation system based on wind-light prediction

Abstract

The invention, which relates to the technical field of intelligent power dispatching, discloses an optimal dispatching method for a water-wind-light complementary power generation system based on wind-light prediction, and the method comprises the steps: collecting historical meteorological data of a target power generation field, carrying out meteorological clustering, and generating a plurality of weather scene categories; collecting real-time wind and light meteorological data and carrying out periodic prediction to obtain a target weather scene change sequence; generating an initial power supply strategy based on the sequence; analyzing the incidence relation between the wind and light environment indexes and the power generation modules, extracting key influence factors and generating a multi-dimensional power generation evaluation network; and optimizing the initial power supply strategy by using the evaluation network to form a power supply optimization strategy, and implementing dynamic power generation scheduling. The technical problems of low power generation efficiency and low reliability caused by difficulty in realizing flexible power generation proportion adjustment of an existing water-wind-light complementary power generation system are solved, and the technical effects of dynamically optimizing and dispatching the power supply proportion of the water-wind-light complementary power generation system and improving the power generation efficiency and the reliability of the system are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent power dispatching, and in particular to an optimization dispatching method for a water-wind-solar complementary power generation system based on wind-solar prediction. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, the water-wind-solar complementary power generation system has gradually become an important clean energy solution, combining the advantages of hydropower, wind power and photovoltaic power generation, and effectively improving energy utilization efficiency. However, the uncertainty of weather changes has brought challenges to the stability and economy of power generation. In the existing technology, there is a lack of effective methods that can dynamically adjust the proportion of water, wind and solar power generation based on accurate meteorological forecasts, resulting in low power generation efficiency and increased costs. Therefore, how to use historical meteorological data and real-time wind and solar forecasts to achieve system optimization and scheduling has become a key requirement for improving the performance of renewable energy systems.

[0003] In the current relevant technologies, there is a technical problem that the water-wind-solar complementary power generation system is difficult to achieve flexible adjustment of the power generation ratio, resulting in low power generation efficiency and low reliability. Summary of the invention

[0004] The present application provides an optimization scheduling method for a water-wind-solar complementary power generation system based on wind-solar prediction. The method collects historical meteorological data of a target power plant and uses a probability distribution network to perform meteorological clustering to generate multiple weather scene categories. Then, real-time wind-solar meteorological data is collected and periodic predictions are performed, and a target weather scene change sequence is obtained by matching weather scenes. Next, an initial power supply strategy is generated based on the sequence, including multiple power generation ratio switching nodes. Subsequently, the correlation between wind-solar environmental indicators and power generation modules is analyzed, key influencing factors are extracted, and a multidimensional power generation evaluation network is generated. Finally, the evaluation network is used to optimize the initial power supply strategy, form a power supply optimization strategy, and implement dynamic power generation scheduling, thereby achieving the technical effect of dynamically optimizing the power supply ratio of the water-wind-solar complementary power generation system and improving the power generation efficiency and reliability of the system through the combination of real-time meteorological data collection and wind-solar prediction.

[0005] The present application provides a method for optimizing the scheduling of a water-wind-solar complementary power generation system based on wind-solar forecasting, including:

[0006] Collect historical meteorological data of the target power plant, use probability distribution network to perform meteorological clustering, and generate multiple weather scene categories; collect real-time wind and solar meteorological data, perform periodic wind and solar forecasts, and traverse the acquired wind and solar forecast data through the multiple weather scene categories to match the weather scenes, and obtain the target weather scene change sequence; based on the target weather scene change sequence, generate an initial power supply strategy for the water-wind-solar complementary power generation system, and the initial power supply strategy includes multiple power generation ratio switching nodes; analyze the correlation between wind and solar environmental indicators and water-wind-solar power generation terminals, extract key influencing factors, and generate a multidimensional power generation evaluation network based on the key influencing factors; based on the multidimensional power generation evaluation network, evaluate and optimize the initial power supply strategy, generate a power supply optimization strategy, and use the power supply optimization strategy for dynamic power generation scheduling.

[0007] The method for optimizing and scheduling a water-wind-solar complementary power generation system based on wind-solar prediction proposed in this application first collects historical meteorological data of the target power plant and uses a probability distribution network to perform meteorological clustering to generate multiple weather scene categories; then, real-time wind-solar meteorological data is collected and periodic predictions are performed, and a target weather scene change sequence is obtained by matching weather scenes; then, an initial power supply strategy is generated based on the sequence, including multiple power generation ratio switching nodes; subsequently, the correlation between wind-solar environmental indicators and power generation modules is analyzed, key influencing factors are extracted, and a multidimensional power generation evaluation network is generated; finally, the evaluation network is used to optimize the initial power supply strategy, form a power supply optimization strategy, and implement dynamic power generation scheduling, thereby achieving the technical effect of dynamically optimizing the power supply ratio of the water-wind-solar complementary power generation system and improving the power generation efficiency and reliability of the system through the combination of real-time meteorological data collection and wind-solar prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0009] Figure 1 A schematic flow chart of a method for optimizing the scheduling of a water-wind-solar complementary power generation system based on wind-solar prediction provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of a multi-dimensional power generation assessment network process for generating a method for optimizing scheduling of a water-wind-solar complementary power generation system based on wind-solar prediction provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0013] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0014] The embodiment of the present application provides a method for optimizing the scheduling of a water-wind-solar complementary power generation system based on wind-solar prediction, such as Figure 1 As shown, the method includes:

[0015] Step S100, collect historical meteorological data of the target power plant, use probability distribution network to perform meteorological clustering, and generate multiple weather scene categories. Specifically, in the process of generating weather scene categories based on probability distribution network, firstly, widely collect historical meteorological data of the target power plant, including long-term observation records of meteorological departments (such as temperature, air pressure, humidity, wind speed, etc., hourly data for many years), satellite meteorological data (such as cloud cover, radiation intensity, etc.) and monitoring data of the power plant itself (such as local wind speed, temperature difference, etc.). Then, organize and preprocess the collected data, unify the format and use methods such as 3σ criterion to remove outliers. When processing missing values, use interpolation method (such as linear interpolation or spline interpolation) or regression filling method to fill in missing values ​​according to the time series relationship of data or the correlation between meteorological parameters to ensure the integrity of data. Then, select appropriate probability distribution models for different meteorological parameters, such as Gaussian distribution for temperature and Weibull distribution for wind speed, and estimate model parameters. Each meteorological parameter can be described by an appropriate probability distribution model. Then, the probability distribution network is used to calculate the probability value of each data point under different distribution models. The calculated probability value is used as the input feature of subsequent clustering, providing contextual information of probability distribution for each meteorological data point. Next, similar data points are classified into one category through clustering algorithms (such as density-based clustering algorithms) to generate multiple weather scene categories such as sunny, cloudy, and rainy days. Finally, the statistical characteristics of each category of meteorological parameters are calculated to describe it, providing a basis for wind and solar forecasting and power generation scheduling.

[0016] Step S200, collect real-time wind and solar meteorological data, perform periodic wind and solar forecasts, and traverse the obtained wind and solar forecast data through the multiple weather scene categories to match the weather scenes, and obtain the target weather scene change sequence. Specifically, first build a real-time wind and solar meteorological data acquisition system including a variety of sensors and data transmission equipment, install wind speed, light, temperature, humidity and other sensors at the power plant, the sensor transmits the data to the data acquisition terminal for preliminary processing and storage, the data is collected and timestamped at a determined frequency, and transmitted to the data center for storage through a wired or wireless communication network. Analyze the change rules based on the historical meteorological data of the target power plant, consider various factors to establish a mathematical or empirical model, and generate a list of wind and solar forecast cycles containing different time scales based on this, and then use corresponding prediction models such as autoregressive models, machine learning algorithms, etc. to generate wind and solar forecast data, and continuously optimize and adjust the model. According to the wind and solar forecast data, the meteorological characteristic curve is generated and smoothed, and a weather scene classifier including a scene segmentation unit and a category matching unit is constructed. The scene segmentation unit performs scene segmentation on the curve according to the threshold table. After fusion analysis, the input is input into the category matching unit for comparison and matching with the weather scene category, so as to obtain the target weather scene change sequence and provide weather scene information for power generation system scheduling.

[0017] In a possible implementation, real-time wind and solar meteorological data are collected to perform periodic wind and solar forecasts, and the acquired wind and solar forecast data are traversed through the multiple weather scene categories to perform weather scene matching to obtain a target weather scene change sequence, and step S200 further includes step S210, performing weather change law analysis based on historical meteorological data of the target power plant, and generating a wind and solar forecast period list based on the analysis results. Specifically, historical meteorological data of the target power plant for many years is collected, covering multiple parameters such as temperature, humidity, air pressure, wind speed, and light. Organize the data and remove errors and missing values. For example, compare the data with the adjacent time periods or use mean filling and other methods to handle missing values. Analyze the changing trends of various meteorological parameters over time, such as seasonal changes in wind speed and daily changes in light intensity. Calculate relevant statistical indicators, such as mean, standard deviation, kurtosis, etc., to understand the data distribution characteristics. Study the correlation between different meteorological parameters, such as the correlation between wind speed and light intensity under certain weather conditions. According to the analysis results, determine different forecast periods. For short-term forecasts, take into account the real-time dispatch needs of electricity, such as 15 minutes to 1 hour, to quickly respond to the impact of changes in meteorological conditions on power generation. The medium-term forecast period may be from a few hours to a day, which is used for equipment start-up and shutdown and power trading planning. The long-term forecast period can reach several days to a week, which provides a reference for resource planning and operation strategy formulation. Consider various factors comprehensively, generate a list of forecast periods with different time scales, and meet the needs of different levels of the power generation system.

[0018] Step S220, based on the wind and light forecast cycle list, continuously collect meteorological data to obtain real-time wind and light meteorological data. Specifically, adjust the collection system according to the wind and light forecast cycle list, increase the sensor collection frequency for short-term forecasts, such as once every 5-10 minutes, and reduce the frequency accordingly for medium- and long-term forecasts, but ensure data continuity, reasonably arrange the collection equipment, avoid interference from factors such as terrain, such as setting monitoring points in open areas and near equipment, and calibrate equipment regularly to ensure measurement accuracy, such as calibrating wind speed sensors every month. The collection system continuously collects meteorological data according to the set cycle, including real-time wind speed, light, temperature, etc., and transmits the data to the data processing center in a timely manner through wired or wireless communication. Encryption and verification measures are used to ensure data security and integrity. The data processing center performs real-time storage and preliminary quality control of the received data, such as checking the rationality of the data range and marking or correcting abnormal data.

[0019] Step S230, the real-time wind and solar meteorological data is subjected to time series analysis to generate the wind and solar forecast data. Specifically, the real-time data is subjected to a stationarity test and seasonal adjustment. If the data is not stationary, differential processing is performed, and a suitable model is selected according to the data characteristics and the forecast period, and automatic adjustment is performed based on model evaluation indicators (such as AIC, BIC, forecast error, etc.). For example, for short-term forecasts (such as wind speed forecasts for the next hour), if the data exhibits strong autoregressive characteristics, the ARMA model is used for forecasting; for medium-term forecasts (such as meteorological data forecasts for the next 7 days), if the data exhibits seasonal fluctuations, the SARIMA model is used to capture seasonal and trend changes; for long-term forecasts or complex situations (such as long-term forecasts of wind and solar power generation), a more flexible machine learning model, such as LSTM (Long Short-Term Memory Network), is used to handle long-term time dependencies and complex nonlinear relationships. In actual operation, the selection of models should be dynamically adjusted based on data characteristics. For example, for short-term predictions, if the data is highly volatile and has no obvious seasonal trend, the ARMA model is preferred; in the case of significant seasonality, the SARIMA model is used to capture seasonal changes. If the data exhibits strong nonlinear characteristics or long-term dependence, the LSTM model is selected to improve prediction accuracy. During each model training process, model evaluation indicators such as AIC and BIC are used, combined with prediction errors (such as root mean square error RMSE and mean absolute error MAE) to evaluate and select the model. If the model's prediction error is large, the system should analyze the reasons and consider whether there are meteorological mutations, data anomalies, or external influencing factors. The model can be readjusted or more relevant factors can be added for training, such as meteorological mutations, historical events, etc., to ensure the adaptability and prediction accuracy of the model. Through dynamic model selection and adaptive adjustment mechanisms based on data characteristics, the problem of mismatch between model and data can be effectively avoided, and the accuracy and stability of wind and solar forecasts can be improved.

[0020] In one possible implementation, real-time wind and solar meteorological data is collected to perform periodic wind and solar forecasts, and the acquired wind and solar forecast data is traversed through the multiple weather scene categories to perform weather scene matching to obtain a target weather scene change sequence. Step S200 further includes step S240, generating multiple meteorological characteristic curves based on the wind and solar forecast data. Specifically, according to the wind and light forecast data, each meteorological parameter is processed separately. For example, for the wind speed forecast data, a curve showing the change of wind speed over time is drawn with time as the horizontal axis and wind speed value as the vertical axis. Corresponding curves are also drawn for parameters such as light intensity, temperature, and humidity. When drawing the curve, ensure that the time sequence of the data is correct and perform appropriate smoothing on the data to remove noise interference. Simple smoothing algorithms such as the moving average method can be used to make the curve more reflective of the basic change trend of the meteorological parameters. For example, for the wind speed curve, the 5-point moving average method is used, that is, the average wind speed value of 5 consecutive time points is taken as the new wind speed value to draw the curve. This can reduce the impact of short-term fluctuations in wind speed data on the shape of the curve and more clearly show the overall change of wind speed. The generated multiple meteorological characteristic curves represent the changes in different meteorological parameters within the forecast time range, providing a basis for subsequent scenario analysis.

[0021] Step S250, construct a weather scene classifier, the weather scene classifier includes a scene segmentation unit and a category matching unit, wherein the scene segmentation unit includes a meteorological feature threshold table of multiple meteorological features, the meteorological feature threshold table includes multiple level thresholds corresponding to the meteorological features, and the category matching unit is embedded with the multiple weather scene categories. Specifically, a meteorological feature threshold table is formulated for different meteorological features. For example, for wind speed, multiple level thresholds such as low wind speed threshold, medium wind speed threshold and high wind speed threshold are set to divide different wind speed intervals. The threshold is determined based on historical meteorological data and analysis of different weather scenes. By statistics of a large amount of historical data, the typical value range of wind speed under different weather conditions is understood, so as to determine a reasonable threshold. For temperature, a low temperature threshold, a normal temperature threshold and a high temperature threshold are set; for humidity, a low humidity threshold, a medium humidity threshold and a high humidity threshold are set. Each meteorological feature threshold table contains the corresponding climate. Multiple level thresholds of image features are used to divide meteorological data into different intervals for scene segmentation. Among the multiple embedded weather scene categories, each category has its own specific meteorological feature combination definition. The weather scene category is determined by cluster analysis of historical meteorological data and expert experience. For example, different weather scene categories such as sunny, cloudy, overcast, light rain, heavy rain, etc. Each category corresponds to a set of typical meteorological parameter ranges and characteristics. The role of the category matching unit is to match the processed meteorological data with these pre-defined weather scene categories to determine the most suitable weather scene.

[0022] Step S260, based on the scene segmentation unit, the multiple meteorological characteristic curves are scene segmented, and the multiple meteorological characteristic curves after segmentation are fused and analyzed to generate a wind and light prediction curve. Specifically, the meteorological characteristic threshold table in the scene segmentation unit is used to perform scene segmentation on the multiple meteorological characteristic curves. For each curve, it is divided into different segments according to the corresponding threshold value. For example, for the wind speed curve, when the wind speed value is higher than the high wind speed threshold, the time period is marked as a high wind speed scene segment; when the wind speed value is between the medium wind speed threshold and the high wind speed threshold, it is marked as a medium wind speed scene segment, etc. Similar scene segmentation operations are also performed on the temperature curve, humidity curve, etc. Each meteorological characteristic curve is divided into multiple different scene segments according to the threshold value. These segments represent different meteorological state intervals. The multiple meteorological curves after segmentation are divided into different scene segments according to the threshold value. The meteorological characteristic curve is fused and analyzed, and the combination of scene segments of various meteorological parameters at different time points is comprehensively considered, and their mutual relationship and coordinated changes are analyzed. For example, at a certain time point, the wind speed is in a high wind speed scene segment, the temperature is in a high temperature scene segment, and the humidity is in a low humidity scene segment. The comprehensive information is used to determine the overall meteorological scene characteristics at that time point. Through the comprehensive analysis of each time point, a wind and light forecast curve is generated. The curve not only contains the change information of each meteorological parameter, but also integrates the correlation and synergy between different meteorological parameters, and more comprehensively reflects the trend of meteorological scene changes in the future period of time.

[0023] Step S270: input the wind and light prediction curve into the category matching unit for weather scene matching, and generate a target weather scene change sequence according to the scene matching result. Specifically, the generated wind and solar forecast curve is input into the category matching unit, which compares and matches the meteorological features in the wind and solar forecast curve with multiple embedded weather scene categories in detail, analyzes the combination of meteorological features at each time point in the curve, and finds the part that best matches the definition of each weather scene category. According to the matching result, the weather scene category to which each time point belongs is determined. As time goes by, the weather scene matching situation at different time points is recorded to generate a target weather scene change sequence. The sequence shows the dynamic change process of the weather scene in the future, such as gradually changing from sunny to cloudy, and then to overcast, etc. The change sequence is very important for the scheduling decision of the water-wind-solar complementary power generation system, because under different weather scenarios, the power generation efficiency and capacity of hydropower, wind power and photovoltaics will be different. It is necessary to adjust the power generation strategy in time according to the changes in the weather scene to achieve the optimal power generation effect and energy utilization efficiency. The generation of the change sequence from wind and solar forecast data to the target weather scene is realized, which provides key weather scene information support for the optimized operation of the water-wind-solar complementary power generation system.

[0024] Step S300, based on the target weather scene change sequence, generate an initial power supply strategy for the water-wind-solar complementary power generation system, the initial power supply strategy includes multiple power generation ratio switching nodes. Specifically, firstly, extract multiple predicted weather scenes from the target weather scene change sequence and determine their duration and scene switching nodes, and at the same time, eliminate instantaneous scenes with little impact on power generation scheduling by setting a time threshold, and obtain the weather scene to be adjusted. Then, for hydropower generation, consider the influence of reservoir water level, water flow velocity, water temperature and other factors on power generation under different weather scenes, and analyze the hydropower generation characteristics; for wind power generation, analyze its power generation characteristics according to wind speed, stability and wind direction changes combined with wind turbine performance curve; for photovoltaic power generation, analyze its power generation characteristics under different weather scenes according to light intensity, temperature and photovoltaic cell performance parameters. Then, according to the power generation characteristics analysis results, determine the optimal power generation ratio of hydropower, wind power and photovoltaic for each weather scene to be adjusted, and use the scene switching node as the power generation ratio switching node. Finally, the initial power supply strategy is formed by integrating the power generation ratio and switching node of each scene, clarifying the power generation ratio arrangement and switching time of each energy source in different time periods, providing preliminary guidance for the operation of the power generation system, and subsequent optimization and adjustment can be made.

[0025] In a possible implementation, based on the target weather scene change sequence, an initial power supply strategy for the water-wind-solar complementary power generation system is generated, the initial power supply strategy includes multiple power generation ratio switching nodes, and step S300 further includes step S310, based on the target weather scene change sequence, extracting multiple predicted weather scenes, and multiple scene durations and scene switching nodes corresponding to the multiple predicted weather scenes. Specifically, the target weather scene change sequence is carefully analyzed, and the sequence is obtained by matching the wind and solar forecast data with the weather scene category. Different predicted weather scenes, such as sunny, cloudy, overcast, etc., are identified according to the time sequence and changes in meteorological characteristics. By recording the start and end time of each scene, the corresponding scene duration is accurately calculated. The scene switching node is determined according to the conversion time of the adjacent scenes. The node marks an important change point of the weather conditions and plays a key indicative role in the adjustment of the power generation strategy.

[0026] Step S320, according to the duration of the multiple scenes, instantaneous scenes are eliminated, multiple weather scenes to be adjusted are generated, and the corresponding scene switching nodes are used as power generation ratio switching nodes. Specifically, the duration of the multiple scenes obtained is compared with the preset time threshold. If the scene duration is less than the threshold, such as some minor scene changes caused by short-term meteorological fluctuations, it is determined as an instantaneous scene and eliminated to avoid unnecessary interference to the system caused by frequent adjustment of the power generation ratio. The condition for eliminating instantaneous scenes depends only on the time threshold, and there is a risk of ignoring important information. For example, in areas where wind speed changes frequently, even if the wind speed scene duration is short, it may have a greater impact on wind power. It is necessary to introduce an impact assessment mechanism to consider the potential impact of short-term scenes on power generation. In order to avoid ignoring short-term scenes that have a greater impact on power generation efficiency or power, when eliminating instantaneous scenes, the actual impact of the scene can be evaluated in combination with the response characteristics of wind power generation or the changing trends of other key meteorological parameters (such as temperature, humidity, etc.). Specifically, for short-term scenarios, if they have a significant impact on the power generation capacity (for example, power fluctuations caused by drastic changes in wind speed), the scenario will not be eliminated and will still be processed as a valid weather scenario. The power change threshold or the instantaneous fluctuation amplitude of the meteorological parameters will be used to determine whether to retain the scenario. The conditions for eliminating instantaneous scenarios no longer rely solely on the time threshold, but should be combined with the degree of influence of meteorological parameters and power fluctuations. After screening, the remaining scenarios with actual impact are the weather scenarios to be adjusted, and the switching nodes between scenarios are determined as power generation ratio switching nodes, which are used to adjust the power generation ratio in time under different weather conditions. By introducing the impact assessment mechanism and power fluctuation judgment, it is possible to more effectively avoid eliminating short-term scenarios that may affect power generation, thereby ensuring the accuracy of power generation scheduling and the efficient operation of the system.

[0027] Step S330, analyze the power generation characteristics for multiple weather scenarios to be adjusted, determine the optimal power generation ratio of hydropower, wind power and photovoltaic power according to the results of the power generation characteristics analysis, and generate the initial power supply strategy. Specifically, analyze the hydropower generation characteristics for different situations in the weather scenarios to be adjusted. In scenarios with more precipitation, consider the increase in water flow and power generation due to rising reservoir water levels; in drought scenarios, pay attention to the restrictions on power generation due to falling water levels, and study the changes in turbine efficiency under different water temperatures and other conditions to fully understand the power generation capacity and stability of hydropower in various scenarios, provide a basis for determining its proportion in the power supply strategy, analyze the changes in wind speed in different weather scenarios, and in high wind scenarios, consider the rated power limit and safe operation requirements of the wind turbine; in low wind scenarios, evaluate the reduction in wind power output power, and combine the wind The impact of directional changes on wind turbine power generation efficiency and the performance curve of the wind turbine are used to determine the power generation characteristics and output power range of wind power in different weather scenarios to be adjusted, providing a reference for the reasonable allocation of power generation ratio. For photovoltaic power generation, in addition to analyzing the negative impact of temperature increase on photovoltaic cell efficiency in sunny and strong light scenarios; in cloudy and weak light scenarios, the degree of power reduction is analyzed, and the impact of seasonal differences on photovoltaic output needs to be considered. For example, in summer, the light intensity is usually high, but the increase in temperature may cause the efficiency of photovoltaic cells to decrease, especially when the temperature exceeds 35°C, the conversion efficiency of photovoltaic cells may decrease. The attenuation of photovoltaic cell efficiency is obvious, so it is necessary to correct the attenuation of photovoltaic cell efficiency under high temperature environment. The high temperature effect is mainly reflected in the increase of the working temperature of the photovoltaic module, which will lead to the increase of the internal resistance of the battery, thereby reducing the power generation efficiency. In winter, although the light is weak and the sunshine time is short, the low temperature condition is conducive to improving the efficiency of photovoltaic cells, and usually can maintain a high conversion efficiency. Especially in sunny weather with strong sunlight in winter, the low temperature environment has a positive effect on photovoltaic power generation. Photovoltaic cells can maintain a high power generation efficiency at low temperatures. Therefore, in winter, the proportion of photovoltaic power generation should be appropriately increased, especially on sunny days. During the day and early morning hours, even when illumination is low, the efficiency of photovoltaic power generation is still high. In order to more accurately arrange the proportion of photovoltaic power generation, it is necessary to comprehensively consider factors such as seasonal differences, light intensity and temperature changes. Specifically, under high temperature conditions in summer, the proportion of photovoltaic power generation should be appropriately reduced to avoid the negative impact of high temperature on the efficiency of photovoltaic cells; under low temperature conditions in winter, the proportion of photovoltaic power generation can be increased to utilize the potential for efficient power generation under low temperature conditions. Under weak light and cloudy conditions, the efficiency of photovoltaic power generation will drop significantly regardless of the season. At this time, priority should be given to relying on other power generation methods (such as hydropower or wind power) for supplementation.The power generation efficiency and power changes of photovoltaic power generation in various weather scenarios to be adjusted are determined by comprehensively considering the light intensity, temperature and performance parameters of photovoltaic cells. On the other hand, adjusting the power generation ratio based only on weather scenarios cannot fully adapt to the actual needs and load fluctuations of the power grid. The load fluctuations and real-time needs of the power grid are key factors affecting the feasibility and stability of the power supply strategy. During peak load periods, the demand on the power grid is usually large. At this time, more stable and adjustable power sources (such as hydropower and wind power) are needed to ensure the balance of power supply; during low load periods, especially at night, the contribution of photovoltaic power generation is low, and the regulating role of wind power and hydropower becomes more important. To ensure power supply The strategy can achieve efficient and stable operation while meeting the needs of the power grid. It needs to dynamically adjust the power generation ratio and comprehensively consider the load fluctuation and real-time demand of the power grid. The load forecasting model and real-time power grid dispatching should be combined with the results of weather scenario analysis to optimize the power supply strategy. For example, when the power grid load is high, hydropower and wind power are dispatched first, and their proportions are appropriately increased to ensure the stable operation of the power grid; in low-load periods, photovoltaic power generation can be appropriately increased to reduce dependence on traditional energy. When the load is large or there is a sudden demand, the power generation ratio can be automatically adjusted through the intelligent dispatching system, and it can be adjusted according to the power grid load dynamics to ensure power supply stability and optimal utilization of power resources. Combined with the actual needs of the power grid and the analysis of power generation characteristics, the generated initial power supply strategy will comprehensively consider factors such as weather scenarios, load fluctuations, and power demand, and determine the optimal power generation ratio of hydropower, wind power, and photovoltaics for each weather scenario to be adjusted, ensuring the efficient and stable operation of water-wind-solar complementary power generation under different weather conditions, while meeting the real-time needs of the power grid.

[0028] Step S400, analyze the correlation between wind and solar environment indicators and water, wind and solar power generation terminals, extract key influencing factors, and generate a multi-dimensional power generation evaluation network based on the key influencing factors. Specifically, first collect wind and solar environment indicator data related to water, wind and solar power generation, including wind speed, wind direction, air density, etc. for wind power generation, light intensity, solar radiation angle, temperature, etc. for photovoltaic power generation, and macro-meteorological data, and then pre-process these data to remove abnormal values, unify time and format, and fill in missing values. Then use the correlation analysis method to analyze its correlation with the water, wind and solar power generation terminal, such as analyzing the correlation between water level, water flow velocity, etc. and power generation in hydropower generation, the relationship between wind speed, wind direction, etc. and wind turbine output power in wind power generation, and the relationship between light intensity, etc. and power generation efficiency in photovoltaic power generation. According to the results, determine the key influencing factors and exclude redundant variables. Then, a multi-dimensional power generation evaluation network structure is designed, including an input layer to receive key factors, an intermediate layer to perform nonlinear transformation and feature extraction, and an output layer to output power generation evaluation indicators. The network is trained using historical data, and appropriate optimization algorithms, learning rates, and hyperparameters are selected. The network is evaluated and verified through a test set, and the situations with large errors are analyzed and improved. Finally, the optimized network is integrated into the power generation system dispatching and management platform to receive real-time data to predict power generation output, providing decision support for optimized dispatching, power trading, and energy storage management.

[0029] In one possible implementation, Figure 2 As shown, the correlation between the wind and solar environment index and the water, wind and solar power generation end is analyzed, the key influencing factors are extracted, and a multi-dimensional power generation evaluation network is generated according to the key influencing factors. Step S400 further includes step S410, and the water, wind and solar complementary power generation system includes a hydropower generation module, a wind power generation module and a photovoltaic power generation module. Specifically, the water, wind and solar complementary power generation system is composed of a hydropower generation module, a wind power generation module and a photovoltaic power generation module. The hydropower generation module converts the energy of the water flow into electrical energy, which usually involves equipment such as reservoirs and turbines. The wind power generation module relies on wind power to drive the wind turbine blades to rotate, thereby driving the generator to generate electricity. The photovoltaic power generation module converts solar energy into electrical energy through photovoltaic cells. Understanding the basic working principles and characteristics of each module lays the foundation for the subsequent analysis of its correlation with wind and solar environment indicators. For example, the power generation of hydropower is closely related to the water level of the reservoir, the water flow speed, etc.; the output power of wind power generation is affected by factors such as wind speed and wind direction; the efficiency of photovoltaic power generation depends on conditions such as light intensity and temperature.

[0030] Step S420, collect wind and solar environment indicators, and analyze the correlation between the wind and solar environment indicators and the hydroelectric power generation module, wind power generation module and photovoltaic power generation module through correlation analysis. Specifically, collect a variety of wind and solar environment indicators, including but not limited to wind speed, wind direction, light intensity, temperature, humidity, air pressure, etc. The indicators can be obtained through various sensors installed in the power plant, and historical data can also be obtained from relevant agencies such as meteorological departments. For example, wind speed sensors measure real-time wind speed, and light intensity sensors record light conditions to ensure the accuracy and completeness of the data. Preliminary sorting and screening of the data are performed to remove obviously abnormal data points. The correlation analysis method is used to analyze the correlation between wind and solar environment indicators and each power generation module. For the hydroelectric power generation module, the correlation between water level, water flow velocity, etc. and power generation is analyzed. By calculating statistical indicators such as correlation coefficients, we can determine whether there is a linear or nonlinear relationship between them. For example, studies have found that the higher the water level in the reservoir, the greater the water head of the turbine, and the power generation capacity will usually increase accordingly. There may be a positive correlation between the two. For wind power generation modules, we analyze the relationship between wind speed, wind direction and wind turbine output power. Wind speed is the key factor. Generally, the greater the wind speed, the higher the wind turbine power generation capacity, but changes in wind direction will also affect the efficiency of the wind turbine. For photovoltaic power generation modules, we analyze the relationship between light intensity, temperature and photovoltaic power generation efficiency. The stronger the light intensity, the greater the photovoltaic power generation capacity, but excessively high temperature may cause the efficiency of photovoltaic cells to decrease.

[0031] Step S430, according to the results of the correlation analysis, identify the key influencing factors of each power generation module, exclude redundant variables, and obtain hydropower influencing factors, wind power operation factors and photovoltaic influencing factors. Specifically, according to the results of the correlation analysis, identify the key factors that have an important impact on each power generation module. In hydropower generation, water level, water flow velocity, etc. may be determined as key influencing factors; in wind power generation, wind speed and wind direction may be key factors; in photovoltaic power generation, light intensity, temperature, etc. may be key influencing factors. Key factors have a significant impact on the output power and efficiency of the power generation module. For example, when the wind speed changes within a certain range, the power generation of the wind turbine will fluctuate significantly. Therefore, wind speed can be used as one of the key operating factors of wind power generation. In order to improve the efficiency and accuracy of subsequent analysis and models, redundant variables need to be excluded. Redundant variables are usually variables that are highly correlated with other key factors and have relatively small impacts on the power generation module. Specifically, redundant variables can be determined by calculating the correlation coefficient between variables (such as the Pearson correlation coefficient). Which variables have a strong correlation. The commonly used standard is that if the correlation coefficient between two variables exceeds a certain threshold (such as 0.8 or 0.9), they can be regarded as highly correlated variables. For example, temperature and humidity may have a high correlation. If the Pearson correlation coefficient between humidity and temperature is higher than 0.8, humidity can be considered a redundant variable. It can also be judged by analyzing the degree of influence of each variable on key outputs such as power generation and efficiency. For example, regression analysis, variance analysis or feature importance evaluation methods (such as feature importance based on decision trees or random forests) can be used to measure the contribution of each factor to the power generation effect. If the contribution of a variable is less than a certain set threshold (such as 0.05), it can be regarded as having little impact on the power generation output. The elimination of redundant variables avoids unnecessary data interference and improves the accuracy and efficiency of subsequent models. Through dimensionality reduction methods such as principal component analysis, comprehensive analysis of related variables and extraction of main independent variables as key influencing factors can reduce the dimension of data and the complexity of the model, while improving the prediction and generalization capabilities of the model.

[0032] Step S440, based on the hydropower influencing factor, wind power operation factor and photovoltaic influencing factor, fit and generate the multidimensional power generation evaluation network. Specifically, the data of the hydropower influencing factor, wind power operation factor and photovoltaic influencing factor are sorted out and used as input variables, and the corresponding historical power generation data, such as the actual power generation of hydropower, wind power and photovoltaic power generation, power generation efficiency, etc. are collected as output variables. Select a suitable fitting method, such as a neural network, a multivariate regression model, etc. to construct a multidimensional power generation evaluation network. For example, when a neural network model is used, the structure of the network is determined, including the number and connection mode of neurons in the input layer, hidden layer and output layer, and the model is trained using the prepared data. By adjusting the parameters of the model, the model can accurately fit the relationship between the input factor and the output power generation data. During the training process, cross-validation and other techniques are used to prevent overfitting and improve the generalization ability of the model. For example, the data is divided into a training set, a validation set and a test set, the model is trained on the training set, the model parameters are adjusted through the validation set, and finally the performance of the model is evaluated on the test set, and the structure and parameters of the model are continuously optimized until the model achieves a good fitting effect and prediction accuracy. For example, the model parameters are adjusted. The learning rate, number of layers and other parameters of the entire neural network are adjusted to improve the accuracy and stability of the model. After training and optimization, a fitted multidimensional power generation evaluation network is obtained. The network can predict the power generation of each power generation module in the water-wind-solar complementary power generation system according to the input hydropower influencing factors, wind power operating factors and photovoltaic influencing factors. The generated network is verified, and new data is used for prediction, which is compared with the actual power generation data to evaluate the accuracy and reliability of the network. If there is a large deviation between the predicted result and the actual situation, the cause is further analyzed, and the model is adjusted and optimized until it meets the requirements of practical applications. Finally, a multidimensional power generation evaluation network that can accurately evaluate the operation of the hydro-wind-solar power generation system is obtained, which provides strong support for the optimal scheduling and management of the power generation system.

[0033] Step S500, based on the multi-dimensional power generation evaluation network, the initial power supply strategy is evaluated and optimized, a power supply optimization strategy is generated, and the power supply optimization strategy is used for dynamic power generation scheduling. Specifically, the multi-dimensional power generation evaluation network is first checked to ensure that it functions normally and its parameters are accurate, and at the same time, the real-time data of wind and solar environment indicators and the information related to the initial power supply strategy are prepared for access and pre-processing. Based on this network, multi-objective evaluation indicators such as power generation, power generation efficiency, and power generation cost are set to evaluate the initial power supply strategy. After the relevant data is input into the network, the evaluation results of hydropower, wind power, and photovoltaic power generation are obtained and analyzed in depth, such as comparing power generation with demand, exploring the reasons for low power generation efficiency, evaluating the power generation cost structure, and paying attention to stability and reliability. Then, the optimization goal and direction are determined according to the evaluation results. The power generation ratio adjustment unit is used to adjust the power generation ratio to generate multiple alternative power supply strategies. The comprehensive objective function is fitted and substituted into the alternative strategy for calculation and evaluation. The strategy with the best comprehensive performance is selected as the power supply optimization strategy according to the function value to ensure the stable and efficient operation of the water-wind-solar complementary power generation system. The evaluation results of the initial power supply strategy of the multi-dimensional power generation evaluation network are comprehensively analyzed, including detailed data of hydropower, wind power, and photovoltaic power generation, to find out problems and deficiencies, such as unreasonable power generation ratio and high cost. Then, according to the optimization direction and goal, the power generation ratio adjustment unit is used to generate multiple alternative power supply strategies, and the power supply optimization strategy is selected according to the objective function value by fitting the comprehensive objective function. In dynamic power generation scheduling, a real-time data monitoring system is established to collect wind and solar meteorological and equipment operation data and analyze and process them to determine whether the actual situation is consistent with expectations, and to activate the emergency mechanism in time when an abnormality occurs. According to the power supply optimization strategy, dispatching instructions are issued to each power generation module to adjust the operating parameters of the hydropower, wind power, and photovoltaic power generation modules, and continuous fine-tuning is performed according to real-time data feedback to ensure the efficient and stable operation of the power generation system, reduce costs and environmental impacts, and improve power supply reliability and sustainability.

[0034] In one possible implementation, based on the multidimensional power generation evaluation network, the initial power supply strategy is evaluated and optimized, a power supply optimization strategy is generated, and the power supply optimization strategy is used for dynamic power generation scheduling, step S500 further includes step S510, the multidimensional power generation evaluation network includes a multidimensional power generation evaluation unit and a power generation ratio adjustment unit, wherein the multidimensional power generation evaluation unit includes a hydropower evaluation unit, a wind power evaluation unit and a photovoltaic power generation evaluation unit. Specifically, the multidimensional power generation evaluation network consists of a multidimensional power generation evaluation unit and a power generation ratio adjustment unit. The multidimensional power generation evaluation unit is further subdivided into a hydropower evaluation unit, a wind power evaluation unit and a photovoltaic power generation evaluation unit. The hydropower evaluation unit focuses on evaluating the relevant performance of the hydropower generation module. It considers the impact of factors such as reservoir water level, water flow velocity, and turbine efficiency on hydropower generation. The wind power evaluation unit analyzes factors related to wind power generation such as wind speed, wind direction, air density, and wind turbine performance for the wind power generation module. The photovoltaic power generation evaluation unit mainly focuses on the effects of light intensity, solar radiation angle, temperature, and photovoltaic cell characteristics on photovoltaic power generation. The power generation ratio adjustment unit is the key part for adjusting the power generation ratio in the initial power supply strategy according to the evaluation results.

[0035] Step S520, based on the multi-dimensional power generation evaluation unit, a multi-objective evaluation is performed on the initial power supply strategy, including but not limited to the evaluation of power generation, power generation efficiency, and power generation cost, and a hydropower evaluation result, a wind power evaluation result, and a photovoltaic power generation evaluation result are obtained. Specifically, based on the multi-dimensional power generation evaluation unit, a multi-objective evaluation is performed on the initial power supply strategy. The hydropower evaluation unit is responsible for evaluating the power generation of hydropower, and predicting the power generation of hydropower under the initial power supply strategy by analyzing the water inflow of the reservoir, the operating status of the turbine, etc. At the same time, the power generation efficiency of hydropower is evaluated, and factors such as the energy conversion efficiency of the turbine and the operating efficiency of the entire hydropower generation system are considered. The cost of hydropower generation will also be evaluated, including equipment depreciation, water resource costs, maintenance costs, etc. The wind power evaluation unit will conduct similar evaluations on wind power, evaluating the power generation, power generation efficiency and cost of wind power based on wind speed distribution, wind turbine performance, etc. The cost includes wind turbine equipment investment, maintenance costs, power transmission losses, etc. The photovoltaic power generation evaluation unit will evaluate the power generation, power generation efficiency and cost of photovoltaic power generation based on lighting conditions, photovoltaic cell performance, etc. The cost covers the purchase and replacement costs of photovoltaic cells, maintenance costs, etc. Through these evaluations, the hydropower evaluation results, wind power evaluation results and photovoltaic power generation evaluation results are obtained, and the performance of each power generation module under the initial power supply strategy is fully understood.

[0036] Step S530, according to the hydropower evaluation result, the wind power evaluation result and the photovoltaic power generation evaluation result, the power generation ratio adjustment unit is used to adjust the power generation ratio of the initial power supply strategy to generate a power supply optimization strategy. Specifically, according to the evaluation results of hydropower, wind power and photovoltaic power generation, the advantages and disadvantages of each power generation module are analyzed. If the hydropower evaluation results show that the power generation has the potential to increase and the cost is relatively low in certain periods, and the proportion of hydropower generation in the initial power supply strategy has not fully exerted its advantages, then it is necessary to consider increasing the proportion of hydropower generation. Similar analysis is also performed for wind power and photovoltaic power generation. The power generation ratio adjustment unit adjusts the power generation ratio in the initial power supply strategy according to these analysis results, and achieves the change of power generation ratio by adjusting the opening of the turbine, the blade angle of the wind turbine and the working state of the photovoltaic cell. For example, in periods with high wind speed and large power demand, the power generation power of the wind turbine is increased, and the power allocation of other power generation modules is correspondingly reduced. After multiple adjustments and simulations, a power supply optimization strategy is generated. This strategy, based on comprehensive consideration of multiple objectives such as power generation, power generation efficiency and power generation cost, enables the water-wind-solar complementary power generation system to operate more efficiently and economically, improves the overall energy utilization efficiency and power supply reliability, and uses each unit of the multi-dimensional power generation evaluation network to achieve a comprehensive evaluation and optimization of the initial power supply strategy, providing strong support for the good operation of the water-wind-solar complementary power generation system.

[0037] In one possible implementation, based on the hydropower evaluation results, wind power evaluation results and photovoltaic power generation evaluation results, the power generation ratio adjustment unit is used to adjust the power generation ratio of the initial power supply strategy to generate a power supply optimization strategy, and step S530 further includes step S531, determining the power supply adjustment direction based on the hydropower evaluation results, wind power evaluation results and photovoltaic power generation evaluation results. Specifically, carefully study the hydropower assessment results to see whether the hydropower generation meets the demand, whether the power generation efficiency is at a reasonable level, and the composition of the power generation cost. If the hydropower assessment results show that the power generation is low in certain periods, it may be due to insufficient reservoir water level or low turbine efficiency. At this time, it is necessary to consider increasing the regulation direction of hydropower generation, such as optimizing reservoir scheduling or maintaining turbine equipment. For power generation costs, if they are too high, it may be necessary to find ways to reduce costs, such as adjusting water resource utilization methods or optimizing equipment operation modes. Analyze the wind power assessment results and pay attention to the relationship between wind speed and wind power generation. If wind resources are abundant in some areas but wind power generation does not meet expectations, it may be necessary to adjust the operating parameters or layout of the wind turbines to improve wind energy capture efficiency. This is the regulation direction of wind power. At the same time, consider wind power. Factors such as wind turbine maintenance costs and power transmission losses are included in the cost to determine whether optimization is needed in these aspects. The evaluation results of photovoltaic power generation are studied to observe the impact of light intensity and temperature on photovoltaic power generation. If the photovoltaic power generation is not ideal during periods of sufficient sunlight, it may be necessary to check the performance or cleanliness of the photovoltaic cells, as well as adjust the angle of the photovoltaic array, etc. This is one of the adjustment directions of photovoltaic power generation. Factors such as the purchase and replacement costs of photovoltaic cells in the cost of photovoltaic power generation are analyzed, and measures to reduce costs are considered. The evaluation results of hydropower, wind power and photovoltaic power generation are comprehensively compared to determine the overall power supply adjustment direction. For example, if it is found that both wind power and photovoltaic power generation are low in a certain period of time, and hydropower has a certain adjustment potential, then the possible adjustment direction is to appropriately increase the proportion of hydropower generation, while finding ways to improve the efficiency of wind power and photovoltaic power generation.

[0038] Step S532, using the power generation ratio adjustment unit, according to the adjustment direction and the preset step length, the initial power supply strategy is adjusted to generate multiple alternative power supply strategies. Specifically, the power generation ratio adjustment unit is used to adjust the initial power supply strategy according to the determined adjustment direction and the preset step length. Assuming that the preset step length is 5%, if the adjustment direction is to increase the hydropower generation ratio, then starting from the hydropower generation ratio in the initial power supply strategy, each time it is increased by 5%, and at the same time, the wind power and photovoltaic power generation ratios are correspondingly reduced (keeping the total power generation ratio at 100%), to generate a series of different power generation ratio combinations, that is, multiple alternative power supply strategies, for each alternative power supply strategy, record the specific values ​​of its hydropower, wind power and photovoltaic power generation ratios, for example, one alternative power supply strategy may be a hydropower generation ratio of 40%, a wind power generation ratio of 30%, and a photovoltaic power generation ratio of 30%; another may be a hydropower generation ratio of 45%, a wind power generation ratio of 27%, a photovoltaic power generation ratio of 28%, etc., to generate multiple alternative power supply strategies with different power generation ratio combinations, providing a basis for subsequent screening and optimization.

[0039] Step S533, fit and generate a comprehensive objective function, screen and optimize the multiple alternative power supply strategies, and obtain the power supply optimization strategy. Specifically, fit and generate a comprehensive objective function, such as Minimize Z = x1·C+c2·E+c3·R (where C is the power generation cost, E is the environmental impact index, R is the power supply reliability, c1, c2, c3 are weight coefficients), and determine the weight coefficients according to the actual situation and decision-making preferences. For example, in the case of focusing on economic benefits, a larger value may be set for the weight coefficient c1 of the power generation cost; in areas that emphasize environmental protection, the weight coefficient c2 of the environmental impact index may be increased. Substitute the generated multiple alternative power supply strategies into the comprehensive objective function for calculation. For each alternative strategy, calculate the corresponding power generation cost C, environmental impact index E and power supply reliability R according to its power generation ratio and related parameters, and then substitute them into the function The objective function value Z is calculated and compared with the objective function values ​​of various alternative power supply strategies. The smaller the objective function value, the better the strategy performs in terms of comprehensive consideration of power generation cost, environmental impact and power supply reliability. The alternative power supply strategy with the smallest objective function value is selected as the power supply optimization strategy. The strategy achieves the optimal balance while meeting multiple objectives. For example, if an alternative power supply strategy has lower power generation cost and smaller environmental impact while ensuring a certain power supply reliability, and its objective function value is the smallest, then it is determined as the final power supply optimization strategy, which is used to guide the actual operation of the water-wind-solar complementary power generation system to improve the overall performance and benefits of the system.

[0040] In one possible implementation, a comprehensive objective function is generated by fitting, and the multiple alternative power supply strategies are screened and optimized to obtain the power supply optimization strategy. Step S533 further includes step S5331, obtaining the optimization target, and the optimization target includes but is not limited to power generation cost, environmental impact index and power supply reliability. Specifically, various factors related to the water-wind-solar complementary power generation system are collected to clarify the optimization target. For the power generation cost, various cost components including equipment purchase, installation, maintenance costs, and energy acquisition (such as the cost of utilizing hydropower resources, the equipment operation and maintenance costs of wind and photovoltaic power generation, etc.) are analyzed in detail. For example, the depreciation cost of hydropower equipment, the regular maintenance cost of wind turbines, and the cleaning and replacement cost of photovoltaic panels are calculated, and these costs are comprehensively considered as evaluation factors for power generation cost. Environmental impact indicators are considered, and attention is paid to the various impacts on the environment during the power generation process. In terms of hydropower generation, the changes in the ecological environment caused by reservoir construction and the impact of water flow changes on aquatic organisms are evaluated; for wind power generation, consider The impact of wind turbine operation on bird migration, noise pollution and other aspects; photovoltaic power generation focuses on environmental pollution during the production and waste treatment of photovoltaic panels. Through quantitative analysis of environmental factors, the specific content and evaluation methods of environmental impact indicators are determined, such as formulating corresponding ecological impact scoring standards, noise pollution levels, etc., analyzing power supply reliability, and evaluating the power supply capacity of the power generation system under different weather conditions and load demands from the perspective of power supply stability and continuity, including the failure rate of power generation equipment, the availability of backup power supplies, and the reliability of the power transmission network. For example, statistics are made on the number and duration of power outages caused by equipment failures in the past period of time, and the loss and stability of power transmission lines are analyzed to determine quantitative indicators of power supply reliability.

[0041] Step S5332, according to the optimization goal, fit and generate a comprehensive objective function: Minimize Z = c1·C + c2·E + c3·R; where C is the power generation cost, E is the environmental impact index, R is the power supply reliability, and c1, c2, and c3 are weight coefficients. Specifically, according to the determined optimization goal, construct a comprehensive objective function: Minimize Z = c1·C + c2·E + c3·R, with the goal of minimizing the total target value, that is, Minimize Z, where Z is the overall target value after comprehensive consideration of power generation cost, environmental impact index and power supply reliability. Power generation cost C is an important variable in the function. It is multiplied by the weight coefficient c1 to indicate the relative importance of power generation cost in the overall target. For example, if you want to pay more attention to reducing power generation cost in the optimization process, you can increase the value of c1 appropriately. The environmental impact index E is multiplied by the weight coefficient c2, which reflects the weight of environmental factors in the optimization decision. For areas or projects with high environmental protection requirements, the value of c2 may be relatively large to emphasize the importance of reducing environmental impact. For example, if the power generation project is located near an ecological protection area, the environmental impact index will be given a higher weight in the function, prompting more consideration of environmental protection measures in the optimization strategy to reduce the adverse impact on the ecological environment. The power supply reliability R is multiplied by the weight coefficient c3, which reflects the status of power supply stability in the optimization target. In some scenarios with extremely high requirements for power supply stability, such as hospitals, important communication facilities, etc., the value of c3 will increase accordingly to ensure that power supply reliability is prioritized in the optimization process. When considering the impact index E, the environmental impact will be calculated based on the scores of various environmental factors, taking into account the impact of different power generation methods (hydropower, wind power, photovoltaic power) on the environment. Specifically, the environmental impact of hydropower is mainly reflected in the ecological damage of the reservoir, the impact of flow changes on the downstream ecology and noise pollution. Wind power mainly considers wind turbine noise, bird impact and landscape damage, while the impact of photovoltaic power generation includes land use, light pollution, recycling and waste issues. In order to quantify the relevant factors, the ecological impact index can be used for scoring according to the impact of water flow changes on the downstream ecology, noise pollution, etc. (the quantification method includes analyzing the water flow change rate, noise intensity and noise impact range). 1 point means almost no impact, 5 points means great impact. The impact on the surrounding environment is scored through noise pollution (noise intensity and noise impact range), bird impact (bird mortality rate) and landscape damage (tourist flow change). 1 point means low impact and 5 points means high impact. Considering factors such as land use (land occupation area and land change) and light pollution (reflected light intensity), similar scoring standards are used, 1 point means low impact and 5 points means high impact.By reasonably adjusting the three weight coefficients c1, c2, and c3, we can flexibly construct a comprehensive objective function according to actual needs and focus, achieve comprehensive optimization of power generation costs, environmental impacts, and power supply reliability, provide a scientific basis for decision-making on hydro-wind-solar complementary power generation systems, accurately obtain optimization targets, and reasonably fit and generate comprehensive objective functions, providing effective mathematical models and decision-making tools for subsequent power supply strategy optimization and power generation system management.

[0042] The embodiment of the present application collects historical meteorological data of the target power plant and uses a probability distribution network to perform meteorological clustering to generate multiple weather scene categories; then, real-time wind and solar meteorological data are collected and periodic predictions are performed, and a target weather scene change sequence is obtained by matching weather scenes; then, an initial power supply strategy is generated based on the sequence, including multiple power generation ratio switching nodes; then, the correlation between wind and solar environmental indicators and power generation modules is analyzed, key influencing factors are extracted and a multidimensional power generation evaluation network is generated; finally, the evaluation network is used to optimize the initial power supply strategy, form a power supply optimization strategy, and implement dynamic power generation scheduling, so as to achieve the technical effect of dynamically optimizing the power supply ratio of the water-wind-solar complementary power generation system and improving the power generation efficiency and reliability of the system through the combination of real-time meteorological data collection and wind and solar prediction.

[0043] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An optimization scheduling method for a water-wind-solar hybrid power generation system based on wind-solar forecasting, characterized in that: The method comprises: Collect historical meteorological data of the target power plant, use probability distribution network to perform meteorological clustering, and generate multiple weather scene categories; Collecting real-time wind and solar meteorological data, performing periodic wind and solar forecasting, and traversing the acquired wind and solar forecasting data across the multiple weather scene categories to perform weather scene matching to obtain a target weather scene change sequence; Based on the target weather scene change sequence, generating an initial power supply strategy for the hydro-wind-solar complementary power generation system, wherein the initial power supply strategy includes multiple power generation ratio switching nodes; Analyze the correlation between wind and solar environmental indicators and hydropower generation terminals, extract key influencing factors, and generate a multi-dimensional power generation evaluation network based on the key influencing factors; Based on the multi-dimensional power generation evaluation network, the initial power supply strategy is evaluated and optimized, a power supply optimization strategy is generated, and the power supply optimization strategy is used to perform dynamic power generation scheduling.

2. The method for optimizing the scheduling of a water-wind-solar hybrid power generation system based on wind-solar forecasting according to claim 1, characterized in that: Collect real-time wind and solar meteorological data and conduct periodic wind and solar forecasts, including: According to the historical meteorological data of the target power plant, analyze the meteorological change rules and generate a list of wind and solar forecast cycles based on the analysis results; Based on the wind and solar forecast period list, continuously collect meteorological data to obtain real-time wind and solar meteorological data; The real-time wind and solar meteorological data are subjected to time series analysis to generate the wind and solar forecast data.

3. The method for optimizing the scheduling of a water-wind-solar complementary power generation system based on wind-solar forecasting according to claim 1, characterized in that: The acquired wind and light forecast data is traversed through the multiple weather scene categories to perform weather scene matching to obtain a target weather scene change sequence, including: generating a plurality of meteorological characteristic curves according to the wind and light forecast data; Constructing a weather scene classifier, the weather scene classifier comprising a scene segmentation unit and a category matching unit, wherein the scene segmentation unit comprises a meteorological feature threshold table of a plurality of meteorological features, the meteorological feature threshold table comprises a plurality of level thresholds corresponding to the meteorological features; the category matching unit is embedded with the plurality of weather scene categories; Based on the scene segmentation unit, the multiple meteorological characteristic curves are scene segmented, and the multiple meteorological characteristic curves after segmentation are merged and analyzed to generate a wind and light prediction curve; The wind and light forecast curve is input into the category matching unit for weather scene matching, and a target weather scene change sequence is generated according to the scene matching result.

4. The method for optimizing the scheduling of a water-wind-solar hybrid power generation system based on wind-solar forecasting according to claim 1, characterized in that: Based on the target weather scene change sequence, an initial power supply strategy for the hydro-wind-solar complementary power generation system is generated, including: Extracting a plurality of predicted weather scenes, and a plurality of scene durations and scene switching nodes corresponding to the plurality of predicted weather scenes based on the target weather scene change sequence; According to the duration of the multiple scenes, instantaneous scenes are eliminated to generate multiple weather scenes to be adjusted, and the corresponding scene switching nodes are used as power generation ratio switching nodes; For multiple weather scenarios to be adjusted, power generation characteristics analysis is performed, and based on the power generation characteristics analysis results, the optimal power generation ratio of hydropower, wind power and photovoltaic power is determined to generate the initial power supply strategy.

5. The method for optimizing the scheduling of a water-wind-solar complementary power generation system based on wind-solar forecasting according to claim 1, characterized in that: Analyze the correlation between wind and solar environmental indicators and hydropower generation terminals, extract key influencing factors, and generate a multi-dimensional power generation evaluation network based on the key influencing factors, including: The water-wind-solar complementary power generation system comprises a hydropower generation module, a wind power generation module and a photovoltaic power generation module; Collecting wind and solar environment indicators, and analyzing the correlation between the wind and solar environment indicators and the hydropower generation module, the wind power generation module and the photovoltaic power generation module through correlation analysis; According to the results of correlation analysis, the key influencing factors of each power generation module are identified, redundant variables are eliminated, and the hydropower influencing factors, wind power operation factors and photovoltaic influencing factors are obtained; Based on the hydropower impact factor, wind power operation factor and photovoltaic impact factor, the multi-dimensional power generation evaluation network is fitted and generated.

6. The method for optimizing the scheduling of a water-wind-solar complementary power generation system based on wind-solar prediction according to claim 1, characterized in that: Based on the multi-dimensional power generation evaluation network, the initial power supply strategy is evaluated and optimized, including: The multidimensional power generation assessment network includes a multidimensional power generation assessment unit and a power generation ratio adjustment unit, wherein the multidimensional power generation assessment unit includes a hydropower assessment unit, a wind power assessment unit and a photovoltaic power generation assessment unit; Based on the multi-dimensional power generation evaluation unit, a multi-objective evaluation is performed on the initial power supply strategy, including but not limited to the evaluation of power generation, power generation efficiency, and power generation cost, to obtain a hydropower evaluation result, a wind power evaluation result, and a photovoltaic power generation evaluation result; According to the hydropower evaluation results, wind power evaluation results and photovoltaic power generation evaluation results, the power generation ratio adjustment unit is used to adjust the power generation ratio of the initial power supply strategy to generate a power supply optimization strategy.

7. The method for optimizing the scheduling of a water-wind-solar complementary power generation system based on wind-solar forecasting according to claim 6, characterized in that: The power generation ratio adjustment unit is used to adjust the power generation ratio of the initial power supply strategy to generate a power supply optimization strategy, including: Determine the power supply adjustment direction according to the hydropower assessment results, wind power assessment results and photovoltaic power generation assessment results; Adopting the power generation ratio adjustment unit, adjusting the initial power supply strategy according to the adjustment direction and the preset step size, and generating a plurality of alternative power supply strategies; A comprehensive objective function is generated by fitting, and the multiple alternative power supply strategies are screened and optimized to obtain the power supply optimization strategy.

8. The method for optimizing the scheduling of a water-wind-solar complementary power generation system based on wind-solar forecasting according to claim 7, characterized in that: Fitting generates a comprehensive objective function, including: Obtaining optimization objectives, wherein the optimization objectives include but are not limited to power generation cost, environmental impact index and power supply reliability; According to the optimization goal, a comprehensive objective function is generated by fitting: Minimize Z=c1·C+c2·E+c3·R; Among them, C is the power generation cost, E is the environmental impact index, R is the power supply reliability, and c1, c2, and x3 are weight coefficients.

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