Power generation coordinated regulation and control method and platform for new energy field group

Abstract

The invention provides a power generation coordinated regulation method and platform for a new energy field group, and relates to the technical field of power generation regulation, and the method comprises the steps: obtaining K power generation unit sets of a target new energy field group; traversing the operation data of the K power generation unit sets, and performing time sequence sensing convergence on the energy storage state and the power grid load demand of the target new energy field group to obtain operation convergence data of the K power generation units, field group energy storage state convergence data and power grid load demand convergence data; performing working condition prototype identification by using a field group digital twin platform, and determining an initial power generation cooperative regulation strategy; and carrying out twinborn simulation, correcting the initial power generation coordinated regulation strategy, and determining a target power generation coordinated regulation strategy. The method solves the technical problems that the prior art mainly depends on static historical data and cannot respond to the dynamic change of new energy power generation in time, so that the coordination scheduling between new energy field groups has hysteresis, and the power grid stability and the power generation efficiency are influenced.

Description

Technical Field

[0001] The present invention relates to the field of power generation control technology, and in particular to a method and platform for coordinated power generation control of a new energy field group. Background Art

[0002] A renewable energy cluster is a power generation system consisting of multiple stations, each of which is composed of multiple power generation units, including wind farms and photovoltaic power stations. The advantages of renewable energy are abundant resources, cleanness, and environmental protection, but they also suffer from volatility and intermittency. This makes the power generation output of renewable energy clusters difficult to predict, posing challenges to grid load scheduling and the stable operation of the power system. Many traditional methods for coordinated power generation control rely on historical data and static models, but these methods fail to fully utilize real-time data to adapt to operating conditions and are unable to respond promptly to dynamic changes in grid load and renewable energy generation. As a result, there is a lag in the coordinated scheduling between power generation units, energy storage systems, and grid loads, resulting in an inability to balance supply and demand in a timely manner, which in turn affects grid stability and power generation efficiency. Summary of the Invention

[0003] The present application provides a method and platform for coordinated power generation control of new energy clusters, aiming to solve the technical problem that the existing technology mainly relies on static historical data and cannot respond to the dynamic changes of new energy power generation in a timely manner, resulting in lags in the coordinated scheduling between new energy clusters, which in turn affects the stability of the power grid and the power generation efficiency.

[0004] The first aspect disclosed in the present application provides a method for coordinated power generation control of a new energy field group, the method comprising: obtaining a set of K power generation units of K stations of a target new energy field group; within a preset perception window, traversing the operating data of the set of K power generation units, as well as the energy storage status and grid load demand of the target new energy field group for time-series perception aggregation, to obtain K power generation unit operation aggregation data, field group energy storage status aggregation data and grid load demand aggregation data; using the field group digital twin platform to perform working condition prototype identification on the K power generation unit operation aggregation data, field group energy storage status aggregation data and grid load demand aggregation data, and determining an initial power generation coordinated control strategy based on the identification results; transmitting the initial power generation coordinated control strategy and the K power generation unit operation aggregation data, field group energy storage status aggregation data and grid load demand aggregation data to the field group digital twin platform for twin simulation, and correcting the initial power generation coordinated control strategy based on the twin simulation results to determine the target power generation coordinated control strategy.

[0005] The second aspect disclosed in the present application provides a power generation coordinated control platform for a new energy field group, which is used for the above-mentioned power generation coordinated control method for a new energy field group. The platform includes: a power generation unit acquisition module, which is used to obtain a set of K power generation units of K stations of a target new energy field group; a time sequence perception aggregation module, which is used to traverse the operation data of the set of K power generation units, as well as the energy storage status and grid load demand of the target new energy field group within a preset perception window, and perform time sequence perception aggregation to obtain the K power generation unit operation aggregation data, the field group energy storage status aggregation data and the grid load demand aggregation data. The invention relates to a power generation coordinated regulation strategy, a control strategy acquisition module, and a power generation coordinated regulation strategy. The control strategy acquisition module is used to transmit the initial power generation coordinated regulation strategy and the K power generation unit operation convergence data, the power generation group energy storage status convergence data, and the power grid load demand convergence data to the power generation group digital twin platform for twin simulation, and to correct the initial power generation coordinated regulation strategy according to the twin simulation results to determine the target power generation coordinated regulation strategy.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects: By acquiring a set of K power generation units from K stations of a target new energy cluster, the actual operation status of the target new energy cluster can be fully understood, comprehensive monitoring of various power generation units can be ensured, and accurate input data can be provided for subsequent control strategies; through time-series perception aggregation, the operation data of the set of K power generation units within the preset perception window, as well as the energy storage status and grid load demand of the target new energy cluster can be effectively integrated to generate accurate aggregation data, which not only provides a comprehensive view of each power generation unit and system status, but also captures the dynamic changes of power generation, energy storage and grid load by setting a preset perception window for time-series data analysis, improves the timeliness and responsiveness of the system, and provides a data basis for subsequent working condition analysis and coordinated control strategy optimization; with the help of digital twin technology, the historical working conditions of the target new energy cluster can be virtually reconstructed for efficient working condition prototype identification. Aggregate data and grid load demand aggregate data for working condition prototype identification, which can identify the similarity between current working conditions and historical working conditions, so that when facing complex operating conditions, the closest historical working condition mode can be quickly found, and the initial power generation coordinated control strategy can be formulated based on this mode. This not only improves the rationality of the strategy, but also accelerates the strategy generation process; in the field group digital twin platform, the initial power generation coordinated control strategy is twin simulated based on the current system state. This simulation can verify the impact of the initial power generation coordinated control strategy on the grid load, power generation unit operation and energy storage status. According to the twin simulation results, the initial power generation coordinated control strategy is iteratively corrected, and finally the target power generation coordinated control strategy is determined. This process ensures that the target power generation coordinated control strategy can adapt to the current grid demand and be flexibly adjusted according to real-time data, which improves the automation and intelligence level of the control strategy and reduces the risk and unnecessary costs during operation.

[0007] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a method for coordinated power generation control of a new energy field group provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of the structure of a power generation collaborative control platform for a new energy field cluster provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: power generation unit acquisition module 10 , timing perception aggregation module 20 , operating condition prototype identification module 30 , control strategy acquisition module 40 . DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a method and platform for coordinated power generation control of new energy clusters, which solves the technical problem that the existing technology mainly relies on static historical data and cannot respond to the dynamic changes of new energy power generation in a timely manner, resulting in lags in the coordinated scheduling between new energy clusters, thereby affecting the stability of the power grid and the power generation efficiency.

[0012] After introducing the basic principles of this application, various non-limiting embodiments of this application will be specifically described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0013] Example 1, as Figure 1 As shown, an embodiment of the present application provides a method for coordinated control of power generation of a new energy field group, the method comprising: Obtain a set of K power generation units of K stations in the target new energy field group.

[0014] The target new energy cluster refers to a regional power generation system that includes multiple stations. The task of the target new energy cluster is to generate and manage all wind power and photovoltaic power generation resources in the region. The cluster consists of multiple stations, and each station contains multiple power generation units. These power generation units can be wind power units (wind turbines) and photovoltaic units (solar panels). This means that each station will have two types of power generation units, wind power and photovoltaic power generation units. The scale and type of each station are different, but as a whole they are part of the new energy power generation system. K represents the number of stations in the cluster, and the set of K power generation units refers to the set of all power generation units in the entire cluster. These power generation units are used for coordinated regulation.

[0015] Within the preset perception window, the operating data of the set of K power generation units, as well as the energy storage status and grid load demand of the target new energy field group are traversed for time-series perception aggregation to obtain the aggregated operating data of the K power generation units, the aggregated energy storage status data of the field group and the aggregated grid load demand data.

[0016] The preset perception window is a period of time, usually hours or longer. It is the time window for the system to analyze and perceive data, indicating the perception and collection of data within this time period. The size and time span of the preset perception window will affect the data processing and optimization results.

[0017] Within the preset perception window, the operating data of a set of K power generation units are traversed. The operating data include: wind power unit data, such as wind speed, output power of wind turbines, operating status, etc.; photovoltaic unit data, such as solar radiation intensity, power output of photovoltaic panels, temperature, etc.; time series of operating data, that is, the power output, status and other data of each power generation unit at each moment during the perception window.

[0018] Within the same preset perception window, the energy storage status of the target new energy field group is collected. The energy storage system is a system used to store excess electrical energy in the target new energy field group, such as a battery energy storage system. The energy storage status includes information such as the charging and discharging status and remaining power of the energy storage system. The energy storage status determines whether additional power support can be provided when demand is high; within the same preset perception window, the grid load demand is obtained. The grid load demand refers to the power demand of the target grid within a specific time period, which is affected by various factors such as weather, electricity usage habits, and industrial production. By analyzing the grid load demand, the scheduling strategy of the power generation unit and the energy storage equipment can be adjusted to ensure the balance of power supply and demand.

[0019] Time-series perception aggregation processes the operating data of the above-mentioned K power generation units, the energy storage status of the target new energy field group and the grid load demand in chronological order. These data are continuous and need to be aggregated and processed according to the characteristics of the time series to form unified aggregated data. Among them, the K power generation unit operation aggregation data contains the power output, operating status, efficiency and other data aggregation results of all wind power and photovoltaic units within the perception window; the field group energy storage status aggregation data contains the charging and discharging status, remaining power and other aggregation information of all energy storage devices in the entire target new energy field group; the grid load demand aggregation data contains the power demand of the grid within the perception window, which helps to judge the changing trend of the grid load.

[0020] The cluster digital twin platform is used to identify the operating condition prototypes of the K power generation units' operation aggregated data, the cluster energy storage status aggregated data, and the grid load demand aggregated data, and the initial power generation coordinated control strategy is determined based on the identification results.

[0021] The digital twin platform for a cluster refers to a platform that virtually replicates the target new energy cluster on a digital platform. It uses real-time data, models, and algorithms to simulate and predict the behavior of physical systems based on digital twins. It is used to simulate and analyze the actual operation of the target new energy cluster. The platform simulates by continuously receiving actual operation data, including the aggregated operation data of K power generation units, the aggregated energy storage status data of the cluster, and the aggregated data of grid load demand, to simulate the operation process of the target new energy cluster.

[0022] The operating condition prototype refers to the typical performance of the target new energy field group under a specific operating environment or conditions. It is usually a collection of historical data, reflecting the working mode of the system under certain load, grid demand, climate and other conditions. Specifically, the field group digital twin platform is used to perform pattern recognition on the obtained K power generation unit operation aggregated data, field group energy storage status aggregated data and grid load demand aggregated data. By comparing the current data with historical operating conditions, the field group digital twin platform identifies the operating condition prototype of the current system. This process is similar to matching, that is, finding the historical operating condition that is most similar to the current system performance and using it as the reference mode of the current system.

[0023] When the operating condition prototype is identified, an initial power generation coordinated control strategy is formulated based on the known control strategies in historical operating conditions. This initial power generation coordinated control strategy is mainly based on the experience of historical patterns and may not be the most perfect, but it provides a basis for subsequent optimization and correction.

[0024] The initial power generation coordinated regulation strategy and the aggregated operation data of K power generation units, the aggregated energy storage status data of the field group and the aggregated load demand data of the power grid are transmitted to the field group digital twin platform for twin simulation. The initial power generation coordinated regulation strategy is revised according to the twin simulation results to determine the target power generation coordinated regulation strategy.

[0025] The initial power generation coordinated control strategy, as well as the aggregated operating data of K power generation units, the aggregated data of the energy storage status of the cluster and the aggregated data of the grid load demand are transmitted to the cluster digital twin platform, and the real data is combined with the digital model. The purpose of the twin simulation is to evaluate the performance of the current initial power generation coordinated control strategy in actual operation. The twin simulation results are obtained through twin simulation. The twin simulation results can reflect the operating performance of the system in actual operation. The operating performance specifically includes: whether the power generation and grid load demand can be balanced, whether the energy storage equipment is used reasonably, whether each power generation unit works as expected, and whether there is any overload or inefficient operation.

[0026] Compare the twin simulation results with the expected control targets and evaluate the differences between the two. If the twin simulation results show that the initial power generation coordinated control strategy cannot effectively meet the expected control targets, or is unstable under certain conditions, it will be corrected. The correction process includes adjusting the output power of each power generation unit, optimizing energy storage scheduling, modifying the scheduling strategy, etc. After the correction, the new strategy is transmitted back to the field group digital twin platform, and the twin simulation is performed again until the expected control targets are achieved.

[0027] Through iterative twin simulation and correction processes, the target power generation coordinated control strategy is finally output, that is, the optimized final strategy. This target power generation coordinated control strategy achieves the best balance between each power generation unit, energy storage equipment and grid load demand, and can ensure the efficiency and stability of system operation.

[0028] Furthermore, within a preset perception window, the operating data of the K power generation units, as well as the energy storage status of the target new energy farm group and the grid load demand are traversed for time-series perception aggregation to obtain the aggregated operating data of the K power generation units, the aggregated energy storage status data of the farm group, and the aggregated grid load demand data, including: Traverse and extract the operating data of the K power generation unit sets within the preset perception window to obtain the K power generation unit operating data sequence sets; perform time-series perception aggregation on the K power generation unit operating data sequence sets to determine the K power generation unit operating aggregation data; perform time-series perception aggregation on the energy storage status and grid load demand of the target new energy field group within the preset perception window respectively to obtain the field group energy storage status aggregation data and grid load demand aggregation data.

[0029] The operating data of the K power generation units in the target new energy field group within the preset perception window are extracted. Each power generation unit will generate a series of operating data within the preset perception window, including but not limited to: wind power unit data, such as wind speed, output power of wind turbines, operating status, etc.; photovoltaic unit data, such as solar radiation intensity, power output of photovoltaic panels, temperature, etc.; time series of operating data, that is, the power output, status and other data of each power generation unit at each moment during the perception window.

[0030] The operating data of the K power generation unit sets are extracted and sorted to generate a power generation unit operating data sequence for each power generation unit within a preset perception window. The power generation unit operating data sequence refers to a set of continuous data of each power generation unit in the time dimension, which can be multi-dimensional data collected by minutes, hours or other time granularities. For all K power generation unit sets, the corresponding K power generation unit operating data sequence sets are obtained.

[0031] Time-series-aware aggregation is performed on a set of K power generation unit operating data sequences. The purpose of time-series-aware aggregation is to extract meaningful patterns or features from the original time series data and synthesize them into an aggregated result. The process includes analyzing, counting, and summarizing the set of each power generation unit operating data sequence. For example, the mean of data in different time periods is calculated, such as the average power output per hour or per minute, and other statistical features such as standard deviation, maximum value, minimum value, etc. are calculated to characterize the stability and efficiency of the power generation unit, compare the output change trends of different power generation units, and identify potential anomalies or faults. The aggregated operating data of K power generation units is the summary result of the set of K power generation unit operating data sequences, which contains the key operating characteristics of each power generation unit in the time period, such as power output level, stability indicators, performance trends, etc.

[0032] In the same way, the energy storage status and grid load demand of the target new energy cluster are time-series sensed and aggregated to obtain cluster energy storage status aggregated data and grid load demand aggregated data. These aggregated data are similar to the aggregated data of power generation unit operation, and for the sake of brevity of the specification, they will not be elaborated here.

[0033] Furthermore, performing time-series-aware aggregation on the K power generation unit operation data sequence sets to determine K power generation unit operation aggregated data includes: Traverse the K power generation unit operation data sequence sets to perceive the operation data trends and determine the K power generation unit operation data trend feature sets; interactively iterate the K power generation unit operation data trend feature sets respectively to determine the K power generation unit operation data iteration trend features; calculate the operation data means of the K power generation unit operation data sequence sets respectively to obtain the K power generation unit operation data means; use the K power generation unit operation data iteration trend features to perform implicit state aggregation on the corresponding K power generation unit operation data means to determine the K power generation unit operation aggregation data.

[0034] Operating data trend perception refers to the time series analysis of each power generation unit's operating data sequence to identify trend characteristics. Specifically, the change pattern, fluctuation amplitude, periodicity and other characteristics of the power generation unit data are analyzed to determine the overall trend of each power generation unit during the perception window. For example, it is recognized that the power generation power of a certain wind power unit shows a certain growth trend with changes in wind speed, or that the output power of a certain photovoltaic unit shows a linear relationship with changes in solar radiation intensity. During the operating data trend perception process, the corresponding trend characteristics are extracted based on the operating data sequence of each power generation unit. These characteristics reflect the typical operating mode of the power generation unit over a period of time. Trend characteristics include average power output, maximum and minimum power values, volatility or standard deviation, and upward or downward trends in operation. After summarizing the trend characteristics, a set of K power generation unit operating data trend characteristics is obtained. This set provides basic data for subsequent analysis and optimization.

[0035] Interactive iteration refers to multiple iterations of the trend feature set of the operating data of K power generation units. By analyzing the mutual influence or synergy between different power generation units, the operating trends of the power generation units are further optimized. This method helps to understand the relationship between the power generation units and improve the coordination of the scheduling strategy. The interactive iteration process includes feature combination and feature adjustment. Feature combination refers to pairing or combining the trend features of different power generation units to identify their impact on the overall operation. For example, the output power of a wind power unit has a certain correlation with the output power of another photovoltaic unit. Interactive iteration can identify these related features; feature adjustment refers to adjusting the trend features of each power generation unit in an interactive manner to ensure that they can match the features of other power generation units. Through continuous iteration, the coordination relationship between the power generation units in the system is optimized.

[0036] Through interactive iteration, based on the interaction and synergistic influence between power generation units, the iterative trend characteristics of each power generation unit are obtained, and the iterative trend characteristics of the operating data of K power generation units are determined. These iterative trend characteristics reflect the performance of power generation units under more complex and coordinated operating modes. For example, some power generation units may show higher efficiency when operating in coordination than when operating alone. This synergistic effect can be captured through interactive iteration.

[0037] For each generation unit's operating data series, the mean of the data series is calculated. This means the average operating data for the generation unit within a preset perception window. This mean calculation indicates the overall performance of each generation unit within that time period. For example, if a wind turbine's operating data includes power output at multiple time points, the mean calculation yields the average power output level within that time period, reflecting the long-term operating trend of the generation unit. This mean calculation yields the mean of the operating data for K generation units, reflecting the long-term average status of each generation unit.

[0038] Implicit state aggregation refers to the aggregation of the iterative trend characteristics of the operating data of K power generation units and the mean operating data of K power generation units through a neural network model to obtain a more accurate and comprehensive operating state. The neural network model is used for pattern recognition in this process. Specifically, the iterative trend characteristics of the operating data of K power generation units and the corresponding mean operating data of K power generation units are input into the neural network model. The neural network model is trained and learned through a hierarchical structure to explore deep associations and patterns in the data. In this way, the neural network model can extract the implicit state characteristics of each power generation unit, that is, those deep information that cannot be directly identified from the original data.

[0039] In the output of the neural network model, the aggregated operating data of K power generation units are obtained. These aggregated data integrate the mean and iterative trend characteristics of the power generation units, representing the actual performance of each power generation unit in a more complex operating environment.

[0040] Furthermore, interactively iterating the trend feature sets of the K power generation unit operation data respectively to determine the iterative trend features of the K power generation unit operation data includes: Perform pairwise feature combination on the K power generation unit operation data trend feature sets respectively to obtain K power generation unit operation data trend feature combination sets; perform sub-similarity calculation on the K power generation unit operation data trend feature combination sets, normalize the calculation results, fill the processed results into an initially empty matrix, and construct K interactive iterative matrices; use the K interactive iterative matrices to convolve the K power generation unit operation data trend feature combination sets to obtain K power generation unit operation data enhanced trend feature combination sets; calculate the mean of the K power generation unit operation data enhanced trend feature combination sets to obtain K power generation unit operation data iterative trend features.

[0041] Performing pairwise feature combinations within the set of K power generation unit operating data trend features means pairing each power generation unit operating data trend feature with other power generation unit operating data trend features to form new combinations. For example, assuming there are three power generation units A, B, and C, each with a trend feature, such as the changing trend of power output, pairwise feature combinations include: Feature A combined with Feature B, Feature A combined with Feature C, and Feature B combined with Feature C. After pairwise feature combinations, all combinations constitute the set of K power generation unit operating data trend feature combinations. This set contains all possible trend feature pairs of the K power generation units. The features of these combinations are used to subsequently analyze the collaborative operating modes or mutual influence relationships between different power generation units.

[0042] A similarity calculation is performed on each pair of power generation unit operating data trend feature combinations. Similarity calculation is used to measure the correlation between the two. Similarity measurement methods include Euclidean distance, Pearson correlation coefficient, and cosine similarity. Each pair of power generation unit operating data trend feature combinations will generate a set of similarity values, indicating the degree of similarity between the two power generation units in all operating characteristics.

[0043] The calculated similarity values are normalized so that all similarity values are within a unified standard range, usually between 0 and 1. The purpose of normalization is to eliminate the scale differences that may exist in the original similarity calculation, so that the similarities of different operating characteristics between different power generation units can be fairly compared.

[0044] The normalized similarity values are then populated into an empty matrix. Each element in the matrix represents the similarity of each operating characteristic between two power generation units. The resulting similarity matrix is actually an interaction iteration matrix, describing the interactive relationship or synergy between each pair of power generation units and used for subsequent iterative calculations. Ultimately, for each set of K power generation unit operating data trend characteristic combinations, K interaction iteration matrices are obtained.

[0045] Convolution is an operation that transforms input data using a specific kernel (or filter). It can help extract more complex feature patterns, thereby enhancing data features and enabling the utilization of richer and more representative trend information when optimizing collaborative control strategies. In this step, the convolution operation applies an interactive iteration matrix to the trend feature combinations of power generation unit operation data to enhance the expression of these features. Specifically, the convolution operation weightedly multiplies each element of the interactive iteration matrix by the corresponding feature in the trend feature combinations of power generation unit operation data and aggregates the results. This convolution result enhances power generation unit combinations that better meet collaborative characteristics and reduces irrelevant or insignificant features. The convolution result produces a set of K enhanced trend feature combinations of power generation unit operation data. This enhanced set of feature combinations better captures the complex relationships and synergies between power generation units.

[0046] Calculate the mean of the enhanced trend feature combination set of the operating data of K power generation units. The mean calculation process is to average all the features in the enhanced trend feature combination set of the operating data of the power generation units to obtain an overall trend feature of each power generation unit. Through the mean calculation, the iterative trend features of the operating data of K power generation units are obtained. These features can comprehensively reflect the operating performance of the power generation unit in multiple time periods and the synergy effect with other power generation units.

[0047] Furthermore, the cluster digital twin platform is used to identify the operating condition prototypes of the K power generation units' aggregated operation data, the cluster energy storage status aggregated data, and the grid load demand aggregated data. Based on the identification results, the initial power generation coordinated control strategy is determined, including: Obtain a historical operating condition prototype library of the field group digital twin platform, wherein each historical operating condition prototype includes K prototype power generation unit operation aggregation data, prototype field group energy storage status aggregation data, and prototype grid load demand aggregation data; using the K power generation unit operation aggregation data, field group energy storage status aggregation data, and grid load demand aggregation data as indexes, perform an approximate analysis on each historical operating condition prototype in the historical operating condition prototype library, including K prototype power generation unit operation aggregation data, prototype field group energy storage status aggregation data, and prototype grid load demand aggregation data, and take the historical operating condition prototype with the highest approximation as the identification result; and use the prototype power generation coordinated control strategy corresponding to the identification result as the initial power generation coordinated control strategy.

[0048] The farm cluster digital twin platform provides a historical operating condition prototype library, which contains operating data under different historical operating conditions. Each historical operating condition represents the typical operating status of the farm cluster within a specific time period. Among them, the aggregated operating data of K prototype power generation units describes the operating status of the power generation units under the corresponding historical operating conditions, including information such as power generation power, equipment status, and operating efficiency; the aggregated energy storage status data of the prototype farm cluster refers to the operating data of the energy storage equipment in the farm cluster under the corresponding historical operating conditions, including the charging and discharging status of the energy storage equipment, the remaining power, etc.; the aggregated load demand data of the prototype power grid reflects the load demand of the power grid under the corresponding historical operating conditions, including load peaks, troughs, and load fluctuation trends. These historical operating condition prototypes provide a reference for subsequent real-time operations, which are used to quickly respond to current operating conditions and optimize control strategies.

[0049] The system's current K power generation unit operation aggregate data, field group energy storage state aggregate data, and grid load demand aggregate data are used as indexes to find the most similar historical operating conditions in the historical operating condition prototype library. Approximate analysis refers to finding the closest operating condition by calculating the similarity between the current system state and the historical operating condition prototype. The similarity calculation methods include Euclidean distance, Pearson correlation coefficient, cosine similarity, etc. Through similarity calculation, it can be determined which historical operating condition prototype best matches the current operating state. Specifically, each historical operating condition prototype is traversed, and similarity is calculated for the K power generation unit operation aggregate data and the K prototype power generation unit operation aggregate data, the field group energy storage state aggregate data and the prototype field group energy storage state aggregate data, and the grid load demand aggregate data and the prototype grid load demand aggregate data. The similarity values of each pair of combinations are summarized to obtain the total similarity between the historical operating condition prototype and the current system state. For all historical operating condition prototypes in the historical operating condition prototype library, the historical operating condition prototype with the highest total similarity is extracted as the recognition result, representing the reference template of the current system state.

[0050] Each historical operating condition prototype includes not only the operating data of the power generation units, energy storage status, and grid load data, but also the corresponding prototype power generation coordinated control strategy. This strategy reflects how the system coordinated the needs of the various power generation units, energy storage equipment, and grid load at that time to achieve the optimal balance between power supply and demand. The prototype power generation coordinated control strategy under this historical operating condition prototype is used as the initial power generation coordinated control strategy for the current system. This initial power generation coordinated control strategy may not be fully applicable to the current environment, but it provides a starting point for subsequent control.

[0051] Furthermore, a historical operating condition prototype library of the farm cluster digital twin platform is obtained, wherein each historical operating condition prototype includes K prototype power generation unit operation aggregated data, prototype farm cluster energy storage state aggregated data, and prototype grid load demand aggregated data, including: Obtain a set of historical operating condition data of the field cluster digital twin platform, wherein each historical operating condition data includes K historical power generation unit operation aggregation data, historical field cluster energy storage status aggregation data and historical power grid load demand aggregation data; based on the preset operating condition data similarity as a constraint, perform similar aggregation on the historical operating condition data sets to determine multiple aggregated historical operating condition data sets; obtain multiple aggregated historical power generation collaborative control strategies of the multiple aggregated historical operating condition data sets and perform mean calculation to obtain multiple prototype historical power generation collaborative control strategies; traverse the multiple aggregated historical operating condition data sets to perform mean calculation to determine multiple historical operating condition prototypes; associate the multiple prototype historical power generation collaborative control strategies with the multiple historical operating condition prototypes to construct a historical operating condition prototype library.

[0052] The cluster digital twin platform acquires a collection of historical operating condition data. This data describes the typical states of the cluster during its past operations. The aggregated operating data for K historical power generation units describes the operating states of the power generation units under corresponding historical operating conditions, including information such as power generation power, equipment status, and operating efficiency. The aggregated energy storage state data for the cluster refers to the operating data of the energy storage devices within the cluster under corresponding historical operating conditions, including the charging and discharging status of the energy storage devices and the remaining power. The aggregated historical grid load demand data reflects the load demand of the grid under corresponding historical operating conditions, including peaks, valleys, and trends in load fluctuations. Through continuous monitoring and recording, these historical operating condition data sets form typical operating modes of the cluster at different times and under different conditions, providing a wealth of historical experience data for the cluster digital twin platform.

[0053] The preset operating condition data similarity is a preset operating condition data similarity standard used for cluster analysis of historical operating condition data sets. The goal of the similarity measurement is to evaluate the similarity between two historical operating condition data in terms of power generation units, energy storage status, and grid load. Similar aggregation is to classify and aggregate the data in the historical operating condition data set according to similarity. In this process, historical operating condition data with similar characteristics are found and classified into the same category. For example, if a certain type of historical operating condition has a high degree of similarity in terms of the output of the power generation unit, the charging and discharging mode of the energy storage, and the fluctuation of the grid load demand, then they will be classified into the same category. This aggregation operation helps to reduce the complexity of the data and helps the system extract useful rules and patterns from large-scale data sets. After similarity calculation and similar aggregation, the historical operating condition data set is divided into multiple aggregated historical operating condition data sets. The data in each aggregated historical operating condition data set has similar characteristics and can represent a typical operating condition mode.

[0054] For each aggregated historical operating condition data set, there is a corresponding aggregated historical power generation coordinated regulation strategy, that is, the corresponding coordinated control scheme at that time. The aggregated historical power generation coordinated regulation strategy is a control scheme formulated based on factors such as the current grid load demand, power generation unit output, and energy storage status, and is used to optimize the coordination of power generation, energy storage, and load.

[0055] For each aggregated historical operating condition data set, a mean calculation is performed on the aggregated historical power generation coordinated control strategy corresponding to the aggregated historical operating condition data set. The purpose of the mean calculation is to summarize the control strategies under multiple similar historical operating conditions to derive a comprehensive control strategy, namely the typical control strategy under the aggregated historical operating condition. Each aggregated historical operating condition data set generates a prototype historical power generation coordinated control strategy, which represents the most typical control strategy under that historical operating condition. These prototype strategies serve as templates for reference and optimization of the control strategy under the current operating condition.

[0056] Traverse each aggregated historical operating condition data set and calculate the mean of all historical operating condition data in it. Through the mean calculation, we can obtain the typical representative of each operating condition set, that is, the historical operating condition prototype. These operating condition prototypes reflect the overall trend of the aggregated data set. The mean of each aggregated historical operating condition data set represents a historical operating condition prototype. These historical operating condition prototypes can show different types of historical operating modes.

[0057] Each prototype historical power generation coordinated control strategy is associated with the corresponding historical operating condition prototype. This association is achieved by matching the operating mode of each historical operating condition prototype with its corresponding power generation coordinated control strategy, ensuring that each historical operating condition prototype has a corresponding control strategy to support it. Through matching, a historical operating condition prototype library is constructed. This historical operating condition prototype library contains multiple historical operating condition prototypes, each of which is associated with a corresponding prototype historical power generation coordinated control strategy. The historical operating condition prototype library provides the system with a decision support tool based on historical experience, which can help quickly find the most suitable control strategy under the current operating conditions.

[0058] Furthermore, the initial power generation coordinated control strategy and the aggregated operation data of K power generation units, the aggregated energy storage status data of the cluster, and the aggregated load demand data of the power grid are transmitted to the cluster digital twin platform for twin simulation. The initial power generation coordinated control strategy is modified according to the twin simulation results to determine the target power generation coordinated control strategy, including: Determine the degree of difference between the twin simulation result and the expected control target; determine a correction scale based on the size of the difference, and perform multiple corrections on the initial power generation coordinated control strategy according to a preset correction method according to the preset correction scale to obtain a corrected power generation coordinated control strategy set; transmit the corrected power generation coordinated control strategy set and the K power generation unit operation aggregation data, the field group energy storage status aggregation data and the grid load demand aggregation data to the field group digital twin platform for twin simulation, and determine a corrected difference degree set in combination with the expected control target; determine whether there is a corrected difference degree less than or equal to the difference degree in the corrected difference degree set, and if so, use the corresponding corrected power generation coordinated control strategy as the target power generation coordinated control strategy.

[0059] The expected control target is a pre-set system performance target. It can be a static target, such as a fixed value for power output, or a dynamic target, such as the changing trend of load demand. After the twin simulation is completed, the difference between the twin simulation results and the expected control target is compared. For example, the mean square error is used to calculate the mean of the sum of squared errors between the twin simulation results and the expected control target. The smaller the error, the better the system performance. The absolute error is used to directly calculate the difference between the twin simulation results and the expected control target, reflecting the direct deviation between the two. The weighted difference is used to assign weights to multiple targets and calculate the weighted difference. If the difference is large, it means that the strategy has not yet effectively met the expected control target and needs further adjustment.

[0060] The correction scale refers to the amplitude of the adjustment strategy determined according to the size of the difference. Specifically, the larger the difference, the larger the correction scale; the smaller the difference, the smaller the correction scale. The preset correction method is to randomly increase or decrease the output of different power generation units in the initial power generation coordinated control strategy according to the correction scale. The correction process is not completed in one go, but is optimized through multiple iterations. After each correction, the twin simulation is performed again to check whether the corrected strategy is closer to the expected control target. Each correction is based on the simulation results and correction scale of the previous step, and gradually approaches the expected control target. Through this iterative process, the effect of the control strategy can be continuously improved until the target requirements are met, and a set of corrected power generation coordinated control strategies is obtained. These strategies represent strategies at different correction stages. As the correction proceeds, the effect of the strategy will gradually optimize.

[0061] Each control strategy in the set of revised power generation coordinated control strategies, as well as the aggregated operation data of K power generation units, the aggregated energy storage status data of the cluster and the aggregated load demand data of the power grid are transmitted to the cluster digital twin platform respectively to simulate the operation of the power generation system in the current state. The goal of the simulation is to verify whether each revised power generation coordinated control strategy can meet the expected control target. After each simulation, the difference between the twin simulation results of each revised power generation coordinated control strategy and the expected control target is calculated. These differences constitute a new set as the revised difference set.

[0062] Check whether any of the corrected differences in the corrected difference set is less than or equal to the initial difference. If such a corrected difference exists, it means that the corrected power generation coordinated control strategy is closer to the expected control target and can be used as the final control strategy. Taking it as the target power generation coordinated control strategy means that this strategy is the strategy that is ultimately applied to actual operation and can meet the grid load demand, optimize the use of energy storage, and coordinate the output of each power generation unit.

[0063] Furthermore, the preset correction method is to randomly increase or decrease the output levels of different power generation units in the initial power generation coordinated control strategy according to the correction scale.

[0064] The output level of different power generation units refers to the power output level of each power generation unit under the initial power generation coordinated control strategy. The output level of each power generation unit will be affected by many factors, such as environmental conditions, equipment status, grid load demand, etc. The correction scale is obtained by calculating the difference, reflecting the size of the difference and the required adjustment range. The larger the correction scale, the greater the need for adjustment to the initial power generation coordinated control strategy to better match the expected control target.

[0065] To optimize the initial coordinated power generation control strategy, the output of different generating units is randomly increased or decreased according to a correction scale. The purpose of this random adjustment is to explore different adjustment ranges to find the output configuration that best meets the target. Random increases refer to randomly increasing the output of certain generating units to meet grid load demands or improve power generation efficiency; random decreases refer to randomly reducing the output of certain generating units to avoid overgeneration or reduce equipment burden. These adjustments are performed within a preset correction scale to ensure system stability and avoid excessive or insufficient adjustments.

[0066] Furthermore, if there is no modified difference less than or equal to the difference in the modified difference set, the initial power generation coordinated regulation strategy is used as the target power generation coordinated regulation strategy.

[0067] Check whether there is any corrected difference in the corrected difference set that is less than or equal to the initial difference. If there is no such corrected difference, it means that the current correction strategy has not significantly improved the degree of matching with the target. In this case, fall back to the initial power generation coordinated regulation strategy, which is considered to be the most appropriate target strategy. Although it may not fully meet the expected target, it is still used as the best strategy at present and is used as the target power generation coordinated regulation strategy.

[0068] In summary, the embodiment of the present application provides a method for coordinated control of power generation in a new energy cluster, which has the following technical effects: By acquiring a set of K power generation units from K stations of a target new energy cluster, the actual operation status of the target new energy cluster can be fully understood, comprehensive monitoring of various power generation units can be ensured, and accurate input data can be provided for subsequent control strategies; through time-series perception aggregation, the operation data of the set of K power generation units within the preset perception window, as well as the energy storage status and grid load demand of the target new energy cluster can be effectively integrated to generate accurate aggregation data, which not only provides a comprehensive view of each power generation unit and system status, but also captures the dynamic changes of power generation, energy storage and grid load by setting a preset perception window for time-series data analysis, improves the timeliness and responsiveness of the system, and provides a data basis for subsequent working condition analysis and coordinated control strategy optimization; with the help of digital twin technology, the historical working conditions of the target new energy cluster can be virtually reconstructed for efficient working condition prototype identification. Aggregate data and grid load demand aggregate data for working condition prototype identification, which can identify the similarity between current working conditions and historical working conditions, so that when facing complex operating conditions, the closest historical working condition mode can be quickly found, and the initial power generation coordinated control strategy can be formulated based on this mode. This not only improves the rationality of the strategy, but also accelerates the strategy generation process; in the field group digital twin platform, the initial power generation coordinated control strategy is twin simulated based on the current system state. This simulation can verify the impact of the initial power generation coordinated control strategy on the grid load, power generation unit operation and energy storage status. According to the twin simulation results, the initial power generation coordinated control strategy is iteratively corrected, and finally the target power generation coordinated control strategy is determined. This process ensures that the target power generation coordinated control strategy can adapt to the current grid demand and be flexibly adjusted according to real-time data, which improves the automation and intelligence level of the control strategy and reduces the risk and unnecessary costs during operation.

[0069] Example 2, based on the same inventive concept as the method for coordinated control of power generation of a new energy field group in the above embodiment, as Figure 2 As shown, the embodiment of the present application provides a power generation coordinated control platform for a new energy field group, the platform comprising: The power generation unit acquisition module 10 is used to acquire a set of K power generation units of K stations in the target new energy field group.

[0070] The timing perception aggregation module 20 is used to traverse the operating data of the set of K power generation units, as well as the energy storage status and grid load demand of the target new energy field group within a preset perception window to perform timing perception aggregation, and obtain the K power generation unit operation aggregation data, field group energy storage status aggregation data and grid load demand aggregation data.

[0071] The operating condition prototype identification module 30 is used to use the field cluster digital twin platform to perform operating condition prototype identification on the aggregated operation data of the K power generation units, the aggregated energy storage status data of the field cluster, and the aggregated power grid load demand data, and determine the initial power generation coordinated control strategy based on the identification results.

[0072] The control strategy acquisition module 40 is used to transmit the initial power generation coordinated control strategy and the aggregated operation data of K power generation units, the aggregated energy storage status data of the field group, and the aggregated power grid load demand data to the field group digital twin platform for twin simulation, and to modify the initial power generation coordinated control strategy according to the twin simulation results to determine the target power generation coordinated control strategy.

[0073] Furthermore, the timing-aware aggregation module 20 is configured to perform the following steps: Traverse and extract the operating data of the K power generation unit sets within the preset perception window to obtain the K power generation unit operating data sequence sets; perform time-series perception aggregation on the K power generation unit operating data sequence sets to determine the K power generation unit operating aggregation data; perform time-series perception aggregation on the energy storage status and grid load demand of the target new energy field group within the preset perception window respectively to obtain the field group energy storage status aggregation data and grid load demand aggregation data.

[0074] Furthermore, the timing-aware aggregation module 20 is configured to perform the following steps: Traverse the K power generation unit operation data sequence sets to perceive the operation data trends and determine the K power generation unit operation data trend feature sets; interactively iterate the K power generation unit operation data trend feature sets respectively to determine the K power generation unit operation data iteration trend features; calculate the operation data means of the K power generation unit operation data sequence sets respectively to obtain the K power generation unit operation data means; use the K power generation unit operation data iteration trend features to perform implicit state aggregation on the corresponding K power generation unit operation data means to determine the K power generation unit operation aggregation data.

[0075] Furthermore, the timing-aware aggregation module 20 is configured to perform the following steps: Perform pairwise feature combination on the K power generation unit operation data trend feature sets respectively to obtain K power generation unit operation data trend feature combination sets; perform sub-similarity calculation on the K power generation unit operation data trend feature combination sets, normalize the calculation results, fill the processed results into an initially empty matrix, and construct K interactive iterative matrices; use the K interactive iterative matrices to convolve the K power generation unit operation data trend feature combination sets to obtain K power generation unit operation data enhanced trend feature combination sets; calculate the mean of the K power generation unit operation data enhanced trend feature combination sets to obtain K power generation unit operation data iterative trend features.

[0076] Furthermore, the working condition prototype identification module 30 is used to perform the following operation steps: Obtain a historical operating condition prototype library of the field group digital twin platform, wherein each historical operating condition prototype includes K prototype power generation unit operation aggregation data, prototype field group energy storage status aggregation data, and prototype grid load demand aggregation data; using the K power generation unit operation aggregation data, field group energy storage status aggregation data, and grid load demand aggregation data as indexes, perform an approximate analysis on each historical operating condition prototype in the historical operating condition prototype library, including K prototype power generation unit operation aggregation data, prototype field group energy storage status aggregation data, and prototype grid load demand aggregation data, and take the historical operating condition prototype with the highest approximation as the identification result; and use the prototype power generation coordinated control strategy corresponding to the identification result as the initial power generation coordinated control strategy.

[0077] Furthermore, the working condition prototype identification module 30 is used to perform the following operation steps: Obtain a set of historical operating condition data of the field cluster digital twin platform, wherein each historical operating condition data includes K historical power generation unit operation aggregation data, historical field cluster energy storage status aggregation data and historical power grid load demand aggregation data; based on the preset operating condition data similarity as a constraint, perform similar aggregation on the historical operating condition data sets to determine multiple aggregated historical operating condition data sets; obtain multiple aggregated historical power generation collaborative control strategies of the multiple aggregated historical operating condition data sets and perform mean calculation to obtain multiple prototype historical power generation collaborative control strategies; traverse the multiple aggregated historical operating condition data sets to perform mean calculation to determine multiple historical operating condition prototypes; associate the multiple prototype historical power generation collaborative control strategies with the multiple historical operating condition prototypes to construct a historical operating condition prototype library.

[0078] Furthermore, the control strategy acquisition module 40 is used to perform the following operation steps: Determine the degree of difference between the twin simulation result and the expected control target; determine a correction scale based on the size of the difference, and perform multiple corrections on the initial power generation coordinated control strategy according to a preset correction method according to the preset correction scale to obtain a corrected power generation coordinated control strategy set; transmit the corrected power generation coordinated control strategy set and the K power generation unit operation aggregation data, the field group energy storage status aggregation data and the grid load demand aggregation data to the field group digital twin platform for twin simulation, and determine a corrected difference degree set in combination with the expected control target; determine whether there is a corrected difference degree less than or equal to the difference degree in the corrected difference degree set, and if so, use the corresponding corrected power generation coordinated control strategy as the target power generation coordinated control strategy.

[0079] Furthermore, the preset correction method is to randomly increase or decrease the output levels of different power generation units in the initial power generation coordinated control strategy according to the correction scale.

[0080] Furthermore, if there is no modified difference less than or equal to the difference in the modified difference set, the initial power generation coordinated regulation strategy is used as the target power generation coordinated regulation strategy.

[0081] Through the above detailed description of a method for coordinated control of power generation for a new energy field group in this specification, those skilled in the art can clearly understand a platform for coordinated control of power generation for a new energy field group in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0082] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for coordinated control of power generation in a new energy field group, characterized in that: The method comprises: Obtain a set of K power generation units of K stations in the target new energy field group; Within a preset perception window, the operating data of the K power generation units, as well as the energy storage status and grid load demand of the target new energy farm group are traversed for time-series perception aggregation to obtain the aggregated operating data of the K power generation units, the aggregated energy storage status data of the farm group, and the aggregated grid load demand data; Using the cluster digital twin platform, the K power generation units' operating data, cluster energy storage status data, and grid load demand data are aggregated to identify operating condition prototypes, and an initial power generation coordinated control strategy is determined based on the identification results. The initial power generation coordinated regulation strategy and the aggregated operation data of K power generation units, the aggregated energy storage status data of the field group and the aggregated load demand data of the power grid are transmitted to the field group digital twin platform for twin simulation. The initial power generation coordinated regulation strategy is revised according to the twin simulation results to determine the target power generation coordinated regulation strategy.

2. A method for coordinated control of power generation of a new energy field group according to claim 1, characterized in that: Within a preset perception window, the operating data of the K power generation units, as well as the energy storage status and grid load demand of the target new energy farm group are traversed for time-series perception aggregation to obtain the aggregated operating data of the K power generation units, the aggregated energy storage status data of the farm group, and the aggregated grid load demand data, including: Traversing and extracting the operating data of the K power generation unit sets within a preset sensing window to obtain a set of operating data sequences of the K power generation units; Performing time series sensing aggregation on the K power generation unit operation data sequence sets to determine K power generation unit operation aggregated data; The energy storage status and grid load demand of the target new energy field group are respectively perceived and aggregated in a time series manner within a preset perception window to obtain the field group energy storage status aggregation data and grid load demand aggregation data.

3. A method for coordinated control of power generation of a new energy field group according to claim 2, characterized in that: Performing time-series-aware aggregation on the K power generation unit operation data sequence sets to determine K power generation unit operation aggregated data, including: Traversing the K power generation unit operation data sequence sets to sense the operation data trends and determine the K power generation unit operation data trend feature sets; Interactively iterating the trend feature sets of the K power generation unit operation data respectively to determine the iterative trend features of the K power generation unit operation data; Calculating the operating data means of the K power generation unit operating data sequence sets respectively to obtain the operating data means of the K power generation units; The iterative trend characteristics of the K power generation unit operation data are used to perform implicit state aggregation on the corresponding mean values of the K power generation unit operation data to determine the K power generation unit operation aggregation data.

4. A method for coordinated control of power generation of a new energy field group according to claim 3, characterized in that: Interactively iterating the trend feature sets of the K power generation unit operation data respectively to determine the iterative trend features of the K power generation unit operation data, including: Performing pairwise feature combinations on the K power generation unit operation data trend feature sets to obtain K power generation unit operation data trend feature combination sets; Performing sub-similarity calculation on the K power generation unit operation data trend feature combination sets, normalizing the calculation results, and filling the processed results into an initially empty matrix to construct K interactive iterative matrices; Convolving the K power generation unit operation data trend feature combination sets using the K interactive iterative matrices to obtain K power generation unit operation data enhanced trend feature combination sets; The mean of the K power generation unit operation data enhanced trend feature combination set is calculated to obtain the K power generation unit operation data iterative trend features.

5. The method for coordinated control of power generation of a new energy field group according to claim 1, characterized in that: The cluster digital twin platform is used to identify the operating condition prototypes of the K power generation units' aggregated operation data, cluster energy storage status aggregated data, and grid load demand aggregated data. The initial power generation coordinated control strategy is determined based on the identification results, including: Obtain a historical operating condition prototype library of the farm cluster digital twin platform, wherein each historical operating condition prototype includes K prototype power generation unit operation aggregated data, prototype farm cluster energy storage state aggregated data, and prototype power grid load demand aggregated data; Using the K power generation unit operation aggregated data, the field group energy storage state aggregated data, and the grid load demand aggregated data as indexes, an approximate analysis is performed on each historical operating condition prototype in the historical operating condition prototype library, including the K prototype power generation unit operation aggregated data, the prototype field group energy storage state aggregated data, and the prototype grid load demand aggregated data, and the historical operating condition prototype with the highest approximation is taken as the recognition result; The prototype power generation coordinated regulation strategy corresponding to the identification result is used as the initial power generation coordinated regulation strategy.

6. A method for coordinated control of power generation of a new energy field group according to claim 5, characterized in that: Obtain a historical operating condition prototype library of the farm cluster digital twin platform, wherein each historical operating condition prototype includes K prototype power generation unit operation aggregated data, prototype farm cluster energy storage state aggregated data, and prototype grid load demand aggregated data, including: Obtain a set of historical operating condition data of the farm cluster digital twin platform, wherein each set of historical operating condition data includes K historical power generation unit operation aggregated data, historical farm cluster energy storage status aggregated data, and historical power grid load demand aggregated data; Based on the preset similarity of the operating condition data, the historical operating condition data sets are aggregated into similar types to determine a plurality of aggregated historical operating condition data sets; Obtaining multiple aggregated historical power generation coordinated control strategies of the multiple aggregated historical operating condition data sets, performing mean calculation, and obtaining multiple prototype historical power generation coordinated control strategies; Traversing the plurality of aggregated historical operating condition data sets to perform mean calculations and determine a plurality of historical operating condition prototypes; The multiple prototype historical power generation coordinated control strategies are associated with the multiple historical operating condition prototypes to construct a historical operating condition prototype library.

7. The method for coordinated control of power generation of a new energy field group according to claim 1, characterized in that: The initial power generation coordinated control strategy and the aggregated operation data of K power generation units, the aggregated energy storage status data of the cluster, and the aggregated load demand data of the power grid are transmitted to the cluster digital twin platform for twin simulation. The initial power generation coordinated control strategy is modified according to the twin simulation results to determine the target power generation coordinated control strategy, including: Determining the degree of difference between the twin simulation results and the expected control target; determining a correction scale based on the magnitude of the difference, and performing multiple corrections on the initial power generation coordinated control strategy according to a preset correction scale and a preset correction method to obtain a set of corrected power generation coordinated control strategies; The modified power generation coordinated control strategy set, the aggregated operation data of K power generation units, the aggregated energy storage status data of the cluster, and the aggregated load demand data of the grid are transmitted to the cluster digital twin platform for twin simulation, and the modified difference degree set is determined in combination with the expected control target; It is determined whether there is a modified difference degree less than or equal to the difference degree in the modified difference degree set. If so, the corresponding modified power generation coordinated regulation strategy is used as the target power generation coordinated regulation strategy.

8. A method for coordinated control of power generation of a new energy field group according to claim 7, characterized in that: The preset correction method is to randomly increase or decrease the output levels of different power generation units in the initial power generation coordinated control strategy according to the correction scale.

9. The method for coordinated control of power generation of a new energy field group according to claim 7, characterized in that: If there is no modified difference less than or equal to the difference in the modified difference set, the initial power generation coordinated regulation strategy is used as the target power generation coordinated regulation strategy.

10. A power generation coordination and control platform for a new energy field group, characterized in that: For implementing a method for coordinated control of power generation for a new energy field group according to any one of claims 1 to 9, the platform comprises: A power generation unit acquisition module is used to obtain a set of K power generation units of K stations in a target new energy field group; A time-series perception and aggregation module is used to perform time-series perception and aggregation on the operating data of the K power generation units, the energy storage status of the target new energy farm group, and the grid load demand within a preset perception window, and obtain the aggregated operating data of the K power generation units, the aggregated energy storage status data of the farm group, and the aggregated grid load demand data; The operating condition prototype identification module is used to use the cluster digital twin platform to identify the operating condition prototype of the K power generation units, the cluster energy storage status data, and the grid load demand data, and determine the initial power generation coordinated control strategy based on the identification results; The control strategy acquisition module is used to transmit the initial power generation coordinated control strategy and the aggregated operation data of K power generation units, the aggregated energy storage status data of the field group, and the aggregated power grid load demand data to the field group digital twin platform for twin simulation, and to modify the initial power generation coordinated control strategy according to the twin simulation results to determine the target power generation coordinated control strategy.

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