Wind-light-water complementary scheduling method and system based on cascade hydroelectric active regulation and control

Abstract

The invention discloses a wind-light-water complementary scheduling method and system based on cascade hydroelectric active regulation and control, and relates to the technical field of electric energy scheduling, and the method comprises the steps: collecting and preprocessing meteorological data, and constructing a runoff prediction model and a wind-light power generation capability prediction model according to the collected data; based on the power supply characteristics and the topological structure, constructing a water, wind and photoelectric aggregation model, and analyzing aggregation characteristics; and based on a prediction model output result, combining aggregation characteristic analysis, formulating a long-medium-short-term coupling scheduling strategy, and performing active adjustment through cascade hydropower. According to the invention, based on aggregation characteristic analysis and prediction model output, optimal distribution of hydropower, wind power and photovoltaic resources is realized. The cascade flow matching mode ensures effective utilization of water resources, and the water energy utilization efficiency is improved. And the long, medium and short-term coupling scheduling strategy provided by the invention provides multi-level and multi-time scale scheduling flexibility. The active hydroelectric regulation and control method can be adjusted in real time according to the output change of the wind and light new energy, and the self-adaptive capacity of scheduling is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy dispatching, and in particular to a method and system for wind-solar-water complementary dispatching based on active regulation of cascade hydropower. Background Art

[0002] "Integration of wind, solar, water, fire and storage" is a necessary part of achieving high-quality development of the power system, an important means to improve the quality and efficiency of energy and power development, and in line with the construction direction of the new power system. It has important practical significance and far-reaching strategic significance for promoting structural reforms on the energy supply side, improving the complementary and coordinated capabilities of various energy sources, and promoting my country's energy transformation and economic and social development. Guizhou Province is an important energy base in the southern region. In recent years, the installed capacity of new energy has grown rapidly (the installed capacity of new energy such as wind power and photovoltaic power in the five provinces and regions of the Southern Power Grid has the highest proportion and the fastest growth rate), accounting for more than 22%. It is expected that new energy will grow further, and the problem of integrated coordinated operation of wind, solar, water, fire and storage will become more prominent.

[0003] The advantages of multi-time scale active regulation of cascade hydropower stations are not fully utilized. There are many cascade hydropower stations in the Wujiang River basin in Guizhou. Hydropower stations with annual regulation or above have the ability to complement and coordinate with wind and solar energy in long, medium and short time scales. However, the synergistic and complementary role in this regard has not been fully utilized, especially the research on cascade hydropower and photovoltaic complementarity is still blank. It is urgent to carry out research on multi-scale coupling complementary scheduling technology of cascade hydropower and photovoltaic under the background of hydropower and photovoltaic crowding out channels. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that the advantages of multi-time scale active regulation of cascade hydropower stations are currently insufficiently utilized, and the synergistic and complementary effects are not fully exerted, especially the research on cascade water-photovoltaic complementarity is still blank.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a wind-solar-water complementary scheduling method based on active regulation of cascade hydropower, which includes collecting and preprocessing meteorological data, and constructing a runoff prediction model and a wind-solar power generation capacity prediction model based on the collected data; based on the power supply characteristics and topological structure, constructing a water, wind, and photovoltaic aggregation model, and analyzing the aggregation characteristics; based on the output results of the prediction model and combined with the analysis of the aggregation characteristics, formulating long-, medium- and short-term coupling scheduling strategies, and actively adjusting through cascade hydropower.

[0007] As a preferred scheme of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower described in the present invention, the meteorological data includes historical data and real-time data of temperature, humidity, wind speed, light, runoff, wind power, and photovoltaic power generation; the preprocessing includes data cleaning and normalization.

[0008] As a preferred solution of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower described in the present invention, wherein: the construction of the runoff prediction model and the wind-solar power generation capacity prediction model includes: the method of constructing the runoff prediction model is to collect historical meteorological data including rainfall, temperature, humidity and runoff data, and output the runoff observation value of the corresponding time period; screen the meteorological factors and influencing factors related to the runoff, and construct a feature set including historical runoff data, selected meteorological factors and influencing factors; divide the data set into a training set and a validation set, perform feature engineering on the training set data, use the training set data to train the deep learning model, use the validation set data to evaluate the model performance, and use evaluation indicators for evaluation, The model structure and parameters are adjusted according to the evaluation results to optimize the model; the method for constructing a wind and solar power generation capacity prediction model is to collect historical meteorological data including rainfall, temperature and humidity, as well as wind power and photovoltaic power generation data, and output wind power and photovoltaic power generation in the corresponding time period; the relationship between meteorological factors and wind and solar power generation is analyzed, key influencing factors are screened, and a feature set is constructed including historical power generation data and selected meteorological factors; the data set is divided into a training set and a validation set, feature engineering is performed on the training set data, the neural network model is trained using the training set data, the model performance is evaluated using the validation set data, and evaluation indicators are used for evaluation. According to the evaluation results, the model structure and parameters are adjusted to optimize the model.

[0009] As a preferred solution of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower described in the present invention, the screening process of the meteorological factors and influencing factors includes calculating the Pearson correlation coefficient between each meteorological factor, influencing factor and historical runoff data, evaluating their linear correlation, selecting factors whose absolute value of the correlation coefficient reaches a threshold as candidate features, and using the Spearman rank correlation coefficient to evaluate nonlinear correlation for data with non-normal distribution or outliers; performing principal component analysis on the meteorological factors and influencing factors to extract the main principal components, which are linear combinations of the original factors, retaining the original information, and based on the results of the present invention. According to the variance contribution rate of the principal component, the principal component with a cumulative variance contribution rate reaching a certain proportion is selected as the feature; the mutual information is calculated, the mutual information between each factor and runoff is evaluated, the nonlinear relationship and complex dependency relationship are captured, and the factor with the highest mutual information value is selected as the feature; the random forest is used for recursive feature elimination, and the feature with the smallest contribution to the model performance is deleted in each iteration until the predetermined number of features is reached, and the feature importance score in the tree model is used to screen out important features; historical runoff observation data or historical wind power and photovoltaic power generation data are collected and organized as important input features of the model, and meteorological factors related to runoff or wind power and photovoltaic are screened out.

[0010] As a preferred scheme of the wind-solar-hydro complementary scheduling method based on active regulation of cascade hydropower described in the present invention, the analysis of aggregation characteristics includes analyzing the grid-connected mode of water, wind and photovoltaic power in the selected area, identifying the cascade topology of the hydropower stations in the selected area, describing the power supply characteristics of water, wind and photovoltaic power in the selected area, extracting characteristic parameters, constructing an aggregation model of water, wind and photovoltaic power in the selected area, and analyzing aggregation characteristics.

[0011] As a preferred scheme of the wind, solar and water complementary scheduling method based on active regulation of cascade hydropower described in the present invention, the formulation of long-, medium- and short-term coupled scheduling strategies includes: analyzing the output results of the prediction model, including short-term, medium-term and long-term power generation forecasts, evaluating the uncertainty of the prediction results, considering the distribution and range of the prediction errors, combining the aggregation characteristic analysis results, obtaining the output characteristics, volatility and dispatchability of different regions and power types, and adjusting the prediction results according to the aggregation characteristics; formulating a long-, medium- and short-term coupled scheduling strategy framework to coordinate the scheduling strategies at all levels; the long-term strategy includes annual plans and seasonal adjustments, the medium-term strategy includes monthly and weekly plans, and the short-term strategy includes intraday real-time scheduling.

[0012] As an optimal solution of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower described in the present invention, the active regulation through cascade hydropower includes analyzing the regulation needs of wind and solar energy according to the scheduling strategy, including frequency regulation, voltage support and power smoothing, and evaluating the regulation needs under different time scales, including short-term rapid regulation and long-term capacity scheduling; evaluating the regulation capacity of the cascade hydropower station group, constructing a flow matching model of the cascade hydropower station group, and dynamically adjusting the flow matching model according to the output forecast and regulation needs of wind and solar energy to regulate different electric energies.

[0013] Another object of the present invention is to provide a wind, solar and water complementary scheduling system based on active regulation of cascade hydropower, which can actively regulate electric energy through cascade hydropower.

[0014] To solve the above technical problems, the present invention provides the following technical solutions: a system for a wind-solar-water complementary scheduling method based on active regulation of cascade hydropower, comprising: a prediction model construction module, an aggregation characteristic analysis module and an adjustment module; the prediction model construction module collects meteorological data and performs preprocessing, and constructs a runoff prediction model and a wind-solar power generation capacity prediction model according to the collected data; the aggregation characteristic analysis module constructs an aggregation model of water, wind, and photovoltaic power based on power supply characteristics and topological structure, and analyzes the aggregation characteristics; the adjustment module formulates long-, medium- and short-term coupling scheduling strategies based on the output results of the prediction model and combined with the aggregation characteristic analysis, and performs active regulation through cascade hydropower.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower are implemented as described above.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower as described above.

[0017] The beneficial effects of the present invention are as follows: the present invention realizes the optimal allocation of hydropower, wind power and photovoltaic resources based on the aggregate characteristic analysis and prediction model output. The cascade flow matching mode ensures the effective use of water resources and improves the efficiency of water energy utilization.

[0018] In addition, the long-, medium- and short-term coupled scheduling strategy of the present invention provides multi-level and multi-time scale scheduling flexibility. The hydropower active regulation method can be adjusted in real time according to the output changes of wind and solar energy, enhancing the adaptive ability of scheduling. Through the long-, medium- and short-term coupled scheduling strategy, the utilization of wind and solar energy and other new energy sources can be maximized, the phenomenon of wind and solar energy abandonment can be reduced, the intermittent and volatility of wind and solar energy can be effectively supplemented, and the overall utilization rate of clean energy can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0020] Figure 1 This is a flow chart of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower in Example 1.

[0021] Figure 2 This is a module structure diagram of the wind, solar and water complementary scheduling system based on active regulation of cascade hydropower in Example 2. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a wind-solar-water complementary scheduling method based on active regulation of cascade hydropower, including: Figure 1 As shown:

[0025] S1. Collect meteorological data and pre-process them, and build a runoff prediction model and a wind and solar power generation capacity prediction model based on the collected data.

[0026] Meteorological data include temperature, humidity, wind speed, light, runoff, wind power, photovoltaic power generation historical data and real-time data, and the data is preprocessed including data cleaning and normalization.

[0027] The method for constructing a runoff prediction model is to collect historical meteorological data including rainfall, temperature, humidity and runoff data, and output the runoff observation values ​​for the corresponding time period.

[0028] Meteorological factors and influencing factors related to runoff were screened, and a feature set was constructed including historical runoff data, selected meteorological factors and influencing factors.

[0029] The data set is divided into a training set and a validation set, feature engineering is performed on the training set data, the deep learning model is trained using the training set data, the model performance is evaluated using the validation set data, evaluation indicators are used for evaluation, and the model structure and parameters are adjusted according to the evaluation results to optimize the model.

[0030] The method of constructing a wind and solar power generation capacity prediction model is to collect historical meteorological data including rainfall, temperature and humidity, as well as wind power and photovoltaic power generation data, and output the wind power and photovoltaic power generation in the corresponding time period;

[0031] The relationship between meteorological factors and wind and solar power generation is analyzed, key influencing factors are screened, and a feature set is constructed including historical power generation data and selected meteorological factors.

[0032] The data set is divided into a training set and a validation set, feature engineering is performed on the training set data, the neural network model is trained using the training set data, the model performance is evaluated using the validation set data, evaluation indicators are used for evaluation, and the model structure and parameters are adjusted according to the evaluation results to optimize the model.

[0033] The screening process of meteorological factors and influencing factors specifically includes calculating the Pearson correlation coefficient between each meteorological factor, influencing factor and historical runoff data, evaluating their linear correlation, selecting factors whose absolute value of the correlation coefficient reaches the threshold as candidate features, and using the Spearman rank correlation coefficient to evaluate nonlinear correlation for data with non-normal distribution or outliers.

[0034] Principal component analysis is performed on meteorological factors and influencing factors to extract the main principal components. The principal components are linear combinations of the original factors and retain the original information. According to the variance contribution rate of the principal components, the principal components whose cumulative variance contribution rate reaches a certain proportion are selected as features.

[0035] Mutual information was calculated to evaluate the mutual information between each factor and runoff, to capture nonlinear relationships and complex dependencies, and the factor with the highest mutual information value was selected as the feature.

[0036] Random forests are used for recursive feature elimination, and the features that contribute the least to the model performance are removed in each iteration until the predetermined number of features is reached. The feature importance scores in the tree model are used to screen out important features.

[0037] Collect and organize historical runoff observation data or historical wind power and photovoltaic power generation data as important input features of the model, and screen out meteorological factors related to runoff or wind power and photovoltaic. According to the above feature selection method, screen out meteorological factors closely related to power generation, such as wind speed, wind direction, temperature, humidity, air pressure, solar radiation, etc.

[0038] S2. Based on the power supply characteristics and topological structure, build an aggregation model of water, wind and photovoltaic power, and analyze the aggregation characteristics.

[0039] Analyze the grid connection mode of water, wind and photovoltaic power in the selected area, identify the cascade topology of hydropower stations in the selected area, describe the power supply characteristics of water, wind and photovoltaic power in the selected area, extract characteristic parameters, build an aggregation model of water, wind and photovoltaic power in the selected area, and analyze the aggregation characteristics.

[0040] The grid connection mode analysis includes the following categories:

[0041] Centralized grid connection: Large hydropower stations, wind farms, and photovoltaic power stations are directly connected to the main grid through high-voltage transmission lines.

[0042] Distributed grid connection: Small hydropower stations, distributed wind power and photovoltaic systems are connected to the grid through the distribution network.

[0043] Analysis of grid-connected characteristics:

[0044] Centralized grid connection: large capacity, significant impact on the power grid, and the need to consider the transmission capacity and stability of the transmission lines.

[0045] Distributed grid connection: small capacity, wide distribution, greater impact on local power grid, need to consider the distribution network's acceptance capacity and voltage stability.

[0046] Grid connection impact assessment: Evaluate the impact of different grid connection methods on grid power flow, voltage stability, frequency control, etc.

[0047] The ladder topology analysis includes topology identification:

[0048] Hydropower Station: Analyze the upstream and downstream relationship of cascade hydropower stations and identify the cascade topology.

[0049] Wind power station: Consider the geographical location of the wind farm and the distribution characteristics of wind power resources.

[0050] Photovoltaic power stations: Analyze the geographical distribution of photovoltaic power stations and the spatiotemporal characteristics of photovoltaic resources.

[0051] Analysis of topological structure characteristics:

[0052] Hydropower station: Cascade hydropower stations are characterized by water resource sharing and mutual regulation.

[0053] Wind power stations: Wind farms may be distributed in clusters and have the characteristics of strong wind speed correlation.

[0054] Photovoltaic power station: The distribution of photovoltaic power stations is affected by terrain and lighting conditions, and is characterized by uneven temporal and spatial distribution.

[0055] Topology impact assessment: Evaluate the impact of cascade topology on grid dispatch, resource allocation and operational efficiency.

[0056] The power supply characteristics analysis includes the power supply characteristics description:

[0057] Hydropower station: has the characteristics of good regulation performance and stable output.

[0058] Wind power station: The output is highly random and volatile.

[0059] Photovoltaic power station: The output is affected by light conditions and has the characteristics of intraday fluctuations and seasonal changes.

[0060] Power supply characterization parameterization:

[0061] Extract characteristic parameters of each power source type, such as the storage capacity and regulation capacity of hydropower stations, wind speed frequency distribution of wind farms, and light intensity distribution of photovoltaic power stations.

[0062] Power supply characteristics impact analysis: Analyze the impact of different power supply characteristics on grid operation, dispatch and control.

[0063] S3. Based on the output results of the prediction model and combined with the analysis of aggregation characteristics, formulate long-, medium- and short-term coupling scheduling strategies and carry out active regulation through cascade hydropower.

[0064] Analyze the output results of the prediction model, including short-term (hourly to daily), medium-term (weekly to monthly) and long-term (quarterly to annual) power generation forecasts, evaluate the uncertainty of the prediction results, consider the distribution and range of the prediction error, combine the results of the aggregation characteristics analysis, obtain the output characteristics, volatility and dispatchability of different regions and power types, and adjust the prediction results according to the aggregation characteristics.

[0065] Formulate a scheduling strategy framework that couples long-, medium- and short-term scheduling to ensure coordination and consistency among scheduling strategies at all levels.

[0066] Long-term strategies include annual plans and seasonal adjustments, medium-term strategies include monthly and weekly plans, and short-term strategies include intraday real-time scheduling.

[0067] Analyze the regulation needs of wind and solar energy according to the dispatch strategy, including frequency regulation, voltage support and power smoothing, and evaluate the regulation needs at different time scales, including short-term rapid regulation and long-term capacity dispatch.

[0068] Assess the regulation capacity of cascade hydropower stations, construct a flow matching model for cascade hydropower stations, dynamically adjust the flow matching model and regulate different types of electric energy based on the output forecast and regulation needs of wind and solar energy.

[0069] Example 2, reference Figure 2 , which is the second embodiment of the present invention, and is different from the first embodiment in that: a system for a wind-solar-water complementary scheduling method based on active regulation of cascade hydropower, comprising a prediction model construction module 100, an aggregation characteristic analysis module 200 and an adjustment module 300; the prediction model construction module 100 collects and preprocesses meteorological data, and constructs a runoff prediction model and a wind-solar power generation capacity prediction model based on the collected data; the aggregation characteristic analysis module 200 constructs an aggregation model of water, wind, and photovoltaic power based on power supply characteristics and topological structure, and analyzes aggregation characteristics; the adjustment module 300 formulates long-, medium- and short-term coupling scheduling strategies based on the output results of the prediction model and combined with the aggregation characteristic analysis, and performs active regulation through cascade hydropower.

[0070] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0071] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0072] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0073] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wind-solar-water complementary dispatching method based on active regulation of cascade hydropower, characterized by: include, Collect and pre-process meteorological data, and build runoff prediction models and wind and solar power generation capacity prediction models based on the collected data; Based on the power characteristics and topological structure, build the aggregation model of water, wind and photovoltaic, and analyze the aggregation characteristics; Based on the output results of the prediction model and combined with the analysis of aggregation characteristics, long-, medium- and short-term coupled scheduling strategies are formulated to carry out active regulation through cascade hydropower.

2. The wind-solar-water complementary dispatching method based on active regulation of cascade hydropower as claimed in claim 1, characterized in that: The meteorological data include historical and real-time data of temperature, humidity, wind speed, light, runoff, wind power and photovoltaic power generation; The preprocessing includes data cleaning and normalization.

3. The wind-solar-water complementary dispatching method based on active regulation of cascade hydropower as claimed in claim 2 is characterized by: The method of constructing the runoff prediction model and the wind and solar power generation capacity prediction model includes: collecting historical meteorological data including rainfall, temperature, humidity and runoff data, and outputting the runoff observation value of the corresponding time period; Screening meteorological factors and influencing factors related to runoff, constructing a feature set including historical runoff data, selected meteorological factors and influencing factors; Divide the data set into a training set and a validation set, perform feature engineering on the training set data, use the training set data to train the deep learning model, use the validation set data to evaluate the model performance, use evaluation indicators to evaluate, adjust the model structure and parameters according to the evaluation results, and optimize the model; The method of constructing a wind and solar power generation capacity prediction model is to collect historical meteorological data including rainfall, temperature and humidity, as well as wind power and photovoltaic power generation data, and output the wind power and photovoltaic power generation in the corresponding time period; Analyze the relationship between meteorological factors and wind and solar power generation, screen key influencing factors, and construct a feature set including historical power generation data and selected meteorological factors; The data set is divided into a training set and a validation set, feature engineering is performed on the training set data, the neural network model is trained using the training set data, the model performance is evaluated using the validation set data, evaluation indicators are used for evaluation, and the model structure and parameters are adjusted according to the evaluation results to optimize the model.

4. The wind-solar-water complementary dispatching method based on active regulation of cascade hydropower as claimed in claim 3 is characterized by: The screening process of the meteorological factors and influencing factors includes calculating the Pearson correlation coefficient between each meteorological factor, influencing factor and historical runoff data, evaluating their linear correlation, selecting factors whose absolute value of the correlation coefficient reaches a threshold as candidate features, and using the Spearman rank correlation coefficient to evaluate nonlinear correlation for data with non-normal distribution or outliers; Perform principal component analysis on meteorological factors and influencing factors to extract the main principal components, which are linear combinations of original factors and retain original information. According to the variance contribution rate of the principal components, the principal components whose cumulative variance contribution rate reaches a certain proportion are selected as features; Calculate mutual information, evaluate the mutual information between each factor and runoff, capture nonlinear relationships and complex dependencies, and select the factor with the highest mutual information value as the feature; Use random forests for recursive feature elimination, removing the features that contribute the least to model performance in each iteration until the predetermined number of features is reached, and use the feature importance score in the tree model to screen out important features; Collect and organize historical runoff observation data or historical wind power and photovoltaic power generation data as important input features of the model, and screen out meteorological factors related to runoff or wind power and photovoltaics.

5. The wind-solar-water complementary dispatching method based on active regulation of cascade hydropower as claimed in claim 4, characterized in that: The analysis of aggregation characteristics includes analyzing the grid connection mode of water, wind and photovoltaic power in the selected area, identifying the cascade topology of the hydropower stations in the selected area, describing the power supply characteristics of water, wind and photovoltaic power in the selected area, extracting characteristic parameters, constructing an aggregation model of water, wind and photovoltaic power in the selected area, and analyzing aggregation characteristics.

6. The wind-solar-water complementary dispatching method based on active regulation of cascade hydropower as claimed in claim 5, characterized in that: The formulation of the long-, medium- and short-term coupled dispatching strategy includes analyzing the output results of the forecasting model, including short-, medium- and long-term power generation forecasts, evaluating the uncertainty of the forecast results, considering the distribution and range of the forecast errors, combining the results of the aggregation characteristics analysis, obtaining the output characteristics, volatility and dispatchability of different regions and power types, and adjusting the forecast results according to the aggregation characteristics; Formulate a scheduling strategy framework that couples long-, medium- and short-term scheduling to ensure coordination and consistency among scheduling strategies at all levels; Long-term strategies include annual plans and seasonal adjustments, medium-term strategies include monthly and weekly plans, and short-term strategies include intraday real-time scheduling.

7. The wind-solar-water complementary dispatching method based on active regulation of cascade hydropower as claimed in claim 6, characterized in that: The active regulation through cascade hydropower includes analyzing the regulation needs of wind and solar energy according to the dispatching strategy, including frequency regulation, voltage support and power smoothing, and evaluating the regulation needs at different time scales, including short-term rapid regulation and long-term capacity dispatch; Assess the regulation capacity of cascade hydropower stations, construct a flow matching model for cascade hydropower stations, dynamically adjust the flow matching model and regulate different types of electric energy based on the output forecast and regulation needs of wind and solar energy.

8. A system using the wind-solar-water complementary dispatching method based on active regulation of cascade hydropower as claimed in any one of claims 1 to 7, characterized in that: It includes a prediction model building module (100), an aggregation characteristic analysis module (200) and an adjustment module (300); The prediction model building module (100) collects meteorological data and performs preprocessing, and builds a runoff prediction model and a wind and solar power generation capacity prediction model based on the collected data; The aggregation characteristic analysis module (200) constructs an aggregation model of water, wind and photovoltaic power based on power supply characteristics and topological structure, and analyzes aggregation characteristics; The regulation module (300) formulates a long-, medium- and short-term coupling scheduling strategy based on the output results of the prediction model and in combination with the analysis of aggregation characteristics, and performs active regulation through cascade hydropower.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the wind-solar-water complementary scheduling method based on active regulation of cascade hydropower according to any one of claims 1 to 7 are implemented.

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