A regional power dispatching method and system based on new energy station power generation output characteristic analysis and optimization

Abstract

The application discloses a regional power dispatching method and system based on new energy station power generation output characteristic analysis and optimization, relates to the technical field of new energy stations, and comprises the following steps: obtaining historical power generation output data and geographical positions of the stations, extracting power change rate characteristic sequences by adopting time series analysis, calculating spatial distances between the stations and output correlation coefficients to determine cluster grouping structures, quantifying output coordination effects in the clusters by a clustering algorithm, and fusing to generate power change trends at a regional scale. A hierarchical model is constructed, multi-level output information is integrated to form a unified index system, and index weight optimization is simulated through real-time data, so that power dispatching parameters are finally output and combined with historical record evaluation of regional power balance. The application realizes organic fusion of accurate characterization of station output characteristics and regional coordinated dispatching, improves the stability and efficiency of power grid dispatching operation, and provides technical support for high proportion of new energy access to power grids.

Description

Technical Field

[0001] This invention relates to the field of new energy power station technology, and in particular to a regional power dispatching method and system based on the analysis and optimization of the power generation output characteristics of new energy power stations. Background Technology

[0002] As a key area for promoting energy transition and achieving carbon neutrality, the importance of new energy power generation is self-evident. Especially in the rapidly growing wind and solar power generation, the rational management and optimization of output characteristics directly affect the stable operation of the power grid and the efficient utilization of energy. However, the volatility and uncertainty of new energy power generation pose significant challenges to power system dispatch and balancing, particularly the precise quantification and management of power ramping at different spatial scales, which has become a pressing technical problem. Currently, research on new energy power ramping largely focuses on the analysis of power generation characteristics of individual power plants, such as predicting output changes of individual wind or solar power plants using historical data. However, this method often ignores the spatial correlation and synergistic effects between power plants, making it difficult to accurately characterize the overall characteristics of power ramping at a larger cluster or regional scale. Especially in regions like Liaoning, where new energy power plants are widely distributed and geographical conditions are complex, the output differences and mutual influences between power plants are overlooked, resulting in a lack of targeted optimization basis for regional power dispatch, thus affecting the stability of the power grid.

[0003] The technical challenges mainly lie in two interrelated aspects. First, the output characteristics of renewable energy power plants are influenced by various factors, such as diurnal variations, seasonal fluctuations, and geographical location differences. These factors manifest differently at different spatial scales. A core challenge is how to uniformly quantify these characteristics across the three scales of power plants, clusters, and regions, and establish a consistent indicator system. For example, a single power plant may experience a rapid power ramp-up due to sudden weather changes, but how to coordinate this local fluctuation with the output of neighboring power plants to achieve stable output at the cluster level lacks an effective analytical framework. Second, due to differences in spatial distribution and output characteristics among power plants, the synergistic effect between clusters is difficult to accurately assess. For example, some power plants may have complementary outputs due to differences in terrain or wind direction, while other power plants may experience amplified power fluctuations due to similar meteorological conditions. Accurately measuring this synergy and integrating it into the assessment of regional power balance is another key challenge. Therefore, how to construct a unified power ramp-up indicator system at different spatial scales to comprehensively consider the synergistic effect between power plants and the overall regional power balance has become a crucial issue in renewable energy output management. For example, in a wind power cluster, some stations may experience a rapid increase in power output due to localized strong winds, while neighboring stations may experience a decrease in output due to wind direction shifts. This inconsistent fluctuation makes the overall power output of the cluster unpredictable, thus affecting the regional power grid's dispatch decisions. At the regional level, the problem of renewable energy consumption further exacerbates this challenge, especially during peak electricity consumption periods. Excessive power ramp-up may prevent the grid from adjusting in time, resulting in power waste or system instability. Therefore, how to accurately capture the power ramp-up characteristics at the station, cluster, and regional scales through a reasonable indicator system, and effectively integrate this information to optimize power dispatch, has become a key issue in renewable energy output management. Summary of the Invention

[0004] The purpose of this invention is to provide a regional power dispatching method based on the analysis and optimization of the power generation output characteristics of new energy power plants. It addresses the challenges of high power output volatility and difficulty in regional collaborative dispatching in business scenarios. This method achieves the organic integration of accurate characterization of power plant output characteristics and regional collaborative dispatching, thereby improving the stability and efficiency of power grid dispatching and providing technical support for the high-proportion integration of new energy into the power grid.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a regional power dispatching method based on the analysis and optimization of the power generation characteristics of new energy power plants, which includes acquiring historical power generation data and geographical location coordinates of multiple new energy power plants within a specified area;

[0007] The historical power generation output data is processed by time series analysis to extract the power change rate characteristic sequence of each power station;

[0008] Based on the power change rate characteristic sequence, the spatial distance between power stations and the correlation coefficient of power generation output are calculated. Power stations with correlation coefficients higher than a preset threshold are grouped into the same cluster to determine the cluster grouping structure of new energy power stations.

[0009] The power change rate feature sequence of each power station in the cluster grouping structure is obtained, and processed by a clustering algorithm to obtain the quantitative value of the power output synergy effect at the cluster level.

[0010] The aggregation features are extracted from the quantitative value of the power output synergy effect, and the aggregation features are fused through time series analysis to obtain the power change trend at the regional scale.

[0011] Based on the power change trend at the regional scale, a hierarchical model from the power station to the region is constructed, and the change indicators of each level in the hierarchical model are obtained. A unified indicator system is obtained by calculating the correlation coefficient.

[0012] Input real-time power generation output data into the unified index system for simulation calculation. Based on the comparison between the cluster output synergy effect value and the preset synergy threshold in the simulation, adjust or maintain the index weights to determine the optimized index system.

[0013] Based on the optimized indicator system, and combined with historical dispatch records, power dispatch parameters are output through data fusion processing.

[0014] As a preferred embodiment of the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants according to the present invention, wherein determining the cluster grouping structure of new energy power plants includes:

[0015] Calculate the spatial distance between stations based on their geographical coordinates.

[0016] Calculate the correlation coefficient of power generation output between power stations based on the power change rate characteristic sequence;

[0017] The power generation output correlation coefficient is compared with a preset threshold, and the power stations with outputs exceeding the preset threshold are grouped into the same cluster group to generate a cluster group structure.

[0018] As a preferred embodiment of the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in this invention, the formula for calculating the power generation output correlation coefficient is as follows:

[0019] ;

[0020] in: Indicates station and station The correlation coefficient between the power generation outputs of the two groups Indicates station At any moment The characteristic value of the rate of change of power, Indicates station At any moment The characteristic value of the rate of change of power, Indicates station The mean of the characteristic sequence of power change rate, Indicates station The power change rate characteristic sequence mean, where T represents the total length of the time series.

[0021] As a preferred embodiment of the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in this invention, wherein: the quantitative value of the power output synergy effect at the cluster level includes:

[0022] Based on the power change rate characteristic sequence, the stations within each cluster are grouped using a clustering algorithm;

[0023] Calculate the variance within and between clusters to generate a quantitative value of the synergistic effect of cluster output.

[0024] As a preferred embodiment of the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in this invention, the calculation formula for the quantification value of the cluster power output synergy effect is as follows:

[0025] ;

[0026] in: Indicates the first Quantitative value of the output synergy effect of each cluster. Indicates the first Number of stations within a cluster This represents the variance of the power change rate of station m within the cluster. Represents a cluster Variance of power change rate compared to other clusters This represents the weight coefficient of station m in the cluster.

[0027] As a preferred embodiment of the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in this invention, the construction of a hierarchical model from power plants to the region includes:

[0028] The model uses stations, clusters, and regions as different levels.

[0029] Standardize the change indicators at each level;

[0030] Based on weight allocation, indicators at different levels are weighted and integrated to obtain a unified indicator system with multiple levels, and the comprehensive evaluation value of the unified indicator system is calculated.

[0031] As a preferred embodiment of the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in this invention, the formula for calculating the comprehensive evaluation value of the unified index system is as follows:

[0032] ;

[0033] in: L represents the comprehensive evaluation value of the unified indicator system, and L represents the total number of levels in the hierarchical model. Indicates the first The weighting coefficients of the indicators at different levels. Indicates the first The original values ​​of the hierarchical change indicators Indicates the first The maximum value of the hierarchical indicator, Indicates the first The minimum value of the hierarchical indicator.

[0034] As a preferred embodiment of the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants according to the present invention, the step of inputting real-time power generation output data into the unified index system for simulation calculation includes:

[0035] Real-time power generation output data is input into a unified index system for simulation calculation to calculate the real-time cluster output synergy effect value.

[0036] The real-time cluster output synergy effect value is compared with the preset synergy threshold. If it is lower than the preset synergy threshold, the weight coefficients of each level of the unified indicator system are adjusted according to the difference between the real-time cluster output synergy effect value and the preset synergy threshold.

[0037] If the value exceeds the preset collaborative threshold, the existing weights will be maintained, and the unified indicator system will be optimized based on the adjusted indicator weight coefficients.

[0038] As a preferred embodiment of the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in this invention, the output power dispatching parameters include:

[0039] Based on the optimized unified indicator system, extract scheduling parameters;

[0040] By combining historical dispatch records, power dispatch parameters are generated through data fusion processing.

[0041] Secondly, the present invention provides a regional power dispatching system based on the analysis and optimization of the power generation characteristics of new energy power plants, which includes: a data acquisition module for collecting historical power generation data and geographic coordinate information of the power plants;

[0042] The feature extraction module is used to extract the power change rate feature sequence through time series analysis;

[0043] The cluster partitioning module is used to calculate the spatial distance and output correlation coefficient between power stations, and to determine cluster grouping based on thresholds;

[0044] The synergy effect analysis module is used to cluster the characteristic sequences of stations within the cluster and output the synergy effect quantification value.

[0045] The hierarchical modeling module is used to construct multi-level power output models from the station to the region and form a unified index system.

[0046] The real-time simulation and optimization module is used to input real-time data for simulation calculations and dynamically adjust the indicator weights.

[0047] The dispatch output module is used to combine historical data to perform data fusion and generate power dispatch parameters.

[0048] The beneficial effects of this invention are as follows: Addressing the challenges of highly volatile power output at renewable energy power plants and the difficulty of regional coordinated dispatch, this invention acquires historical power output data and geographical locations of the power plants. Time series analysis is used to extract power change rate characteristic sequences, and the correlation coefficient between spatial distance and power output between power plants is calculated to determine the cluster grouping structure. Furthermore, clustering algorithms are used to quantify the power output synergy within the cluster, generating regional-scale power change trends. Based on these trends, a hierarchical model is constructed, integrating multi-level power output information to form a unified index system. Real-time data simulation is used to optimize index weights, ultimately outputting power dispatch parameters and combining historical records to assess regional power balance. This invention achieves the organic integration of accurate characterization of power plant output characteristics and regional coordinated dispatch, improving the stability and efficiency of power grid dispatch operation and providing technical support for high-proportion renewable energy grid integration. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart of a regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants; Figure 2 A flowchart for constructing a unified indicator system. Detailed Implementation

[0051] To make the above-mentioned objects, features, and advantages of the present invention more readily understood, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0052] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0054] Reference Figure 1 This is the first embodiment of the present invention, which provides a regional power dispatching method based on the analysis and optimization of the power generation characteristics of new energy power plants, including:

[0055] S101. Obtain historical power generation output data and geographical coordinate information of multiple new energy power stations within a specified area, process the historical power generation output data using time series analysis methods, and extract the power change rate characteristic sequence of each power station to characterize the power output fluctuation characteristics.

[0056] S102. Based on the power change rate characteristic sequence, calculate the correlation coefficient between the spatial distance and power generation output between power stations. If the correlation coefficient is higher than the preset correlation threshold, the power stations are grouped into the same cluster group; otherwise, they are grouped into different cluster groups, thus determining the cluster grouping structure of new energy power stations.

[0057] S103. For the cluster grouping structure, obtain the power change rate feature sequence of each station in the cluster, and perform grouping processing on the feature sequence through clustering algorithm to obtain the output synergy effect quantification value at the cluster level, which is used to characterize the output complementarity characteristics between stations.

[0058] S104. From the quantified value of the power output synergy effect, extract the aggregation characteristics of all clusters in the region, and use time series analysis to fuse the aggregation characteristics to obtain the power change trend at the regional scale, which is used to characterize the overall power output characteristics of the region.

[0059] S105. Based on the power change trend at the regional scale, construct a hierarchical model from the power station to the region, obtain the change indicators of each level in the model, and obtain a unified indicator system through correlation coefficient calculation to integrate multi-level power output information.

[0060] S106. For the unified index system, input real-time power generation output data for simulation calculation. If the cluster output synergy effect value is lower than the preset synergy threshold in the simulation, adjust the index weight; otherwise, maintain the current weight and determine the optimized index system.

[0061] S107. From the optimized index system, output power dispatch parameters, and through data fusion processing combined with historical dispatch records, obtain regional power balance assessment results to guide power grid dispatch operation.

[0062] Determining the cluster grouping structure of new energy power stations includes: calculating the spatial distance between power stations based on their geographical location coordinates;

[0063] Calculate the correlation coefficient of power generation output between power stations based on the power change rate characteristic sequence;

[0064] The power generation output correlation coefficient is compared with a preset threshold, and the power stations with outputs exceeding the preset threshold are grouped into the same cluster group to generate a cluster group structure.

[0065] The formula for calculating the correlation coefficient of power generation output is:

[0066] ;

[0067] in: Indicates station and station The correlation coefficient between the power generation outputs of the two groups Indicates station At any moment The characteristic value of the rate of change of power, Indicates station At any moment The characteristic value of the rate of change of power, Indicates station The mean of the characteristic sequence of power change rate, Indicates station The mean of the power change rate characteristic sequence, where T represents the total length of the time series. This is used to calculate the output correlation between power stations; when the correlation coefficient exceeds a preset threshold, the power stations are grouped into the same cluster.

[0068] Obtaining the quantitative value of the power output synergy effect at the cluster level includes: grouping the stations within each cluster according to the power change rate characteristic sequence using a clustering algorithm;

[0069] Calculate the variance within and between clusters to generate a quantitative value of the synergistic effect of cluster output.

[0070] The formula for calculating the quantified value of the cluster output synergy effect is as follows:

[0071] ;

[0072] in: Indicates the first Quantitative value of the output synergy effect of each cluster. Indicates the first Number of stations within a cluster This represents the variance of the power change rate of station m within the cluster. Represents a cluster Variance of power change rate compared to other clusters This represents the weighting coefficient of power station m within the cluster. The output complementarity characteristics between power stations are quantified by comparing the differences in power changes within and outside the cluster.

[0073] Constructing a hierarchical model from stations to regions includes treating stations, clusters, and regions as different levels of the model;

[0074] The change indicators at each level are standardized; based on the weight allocation, the indicators at different levels are weighted and integrated to obtain a unified indicator system at multiple levels, and the comprehensive evaluation value of the unified indicator system is calculated.

[0075] The formula for calculating the comprehensive evaluation value of the unified indicator system is as follows:

[0076] ;

[0077] in: L represents the comprehensive evaluation value of the unified indicator system, and L represents the total number of levels in the hierarchical model. Indicates the first The weighting coefficients of the indicators at different levels. Indicates the first The original values ​​of the hierarchical change indicators Indicates the first The maximum value of the hierarchical indicator, Indicates the first The minimum value of the hierarchical index. This formula standardizes and weights the output information from each level from the station to the region, forming a unified multi-level output evaluation index.

[0078] Inputting real-time power generation output data into the unified index system for simulation calculation includes: inputting real-time power generation output data into the unified index system for simulation calculation, and calculating the real-time cluster output synergy effect value;

[0079] The real-time cluster output synergy effect value is compared with the preset synergy threshold. If it is lower than the preset synergy threshold, the weight coefficients of each level of the unified indicator system are adjusted according to the difference between the real-time cluster output synergy effect value and the preset synergy threshold.

[0080] If the value exceeds the preset collaborative threshold, the existing weights will be maintained, and the unified indicator system will be optimized based on the adjusted indicator weight coefficients.

[0081] Output power dispatch parameters include: extracting dispatch parameters based on the optimized unified index system;

[0082] By combining historical dispatch records, power dispatch parameters are generated through data fusion processing.

[0083] Furthermore, this embodiment also provides a regional power dispatching system based on the analysis and optimization of the power generation output characteristics of new energy power plants, including: a data acquisition module, used to collect historical power generation output data and geographical coordinate information of the power plants;

[0084] The feature extraction module is used to extract the power change rate feature sequence through time series analysis;

[0085] The cluster partitioning module is used to calculate the spatial distance and output correlation coefficient between power stations, and to determine cluster grouping based on thresholds;

[0086] The synergy effect analysis module is used to cluster the characteristic sequences of stations within the cluster and output the synergy effect quantification value.

[0087] The hierarchical modeling module is used to construct multi-level power output models from the station to the region and form a unified index system.

[0088] The real-time simulation and optimization module is used to input real-time data for simulation calculations and dynamically adjust the indicator weights.

[0089] The dispatch output module is used to combine historical data to perform data fusion and generate power dispatch parameters.

[0090] In summary, this invention addresses the challenges of highly volatile power output from renewable energy power plants and the difficulty of regional coordinated dispatching. By acquiring historical power output data and geographical locations of these plants, time series analysis is used to extract power change rate characteristic sequences. The correlation coefficient between spatial distance and power output between power plants is calculated to determine the cluster grouping structure. Furthermore, clustering algorithms are used to quantify the synergistic effect of power output within clusters, generating regional-scale power change trends. Based on these trends, a hierarchical model is constructed, integrating multi-level power output information to form a unified index system. Real-time data simulation is used to optimize index weights, ultimately outputting power dispatching parameters and combining historical records to assess regional power balance. This invention achieves an organic integration of accurate characterization of power plant output characteristics and regional coordinated dispatching, improving the stability and efficiency of power grid dispatching and providing technical support for high-proportion renewable energy grid integration.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants, characterized in that: include, Obtain historical power generation output data and geographic location coordinates of multiple renewable energy power stations within a specified area; The historical power generation output data is processed by time series analysis to extract the power change rate characteristic sequence of each power station; Based on the power change rate characteristic sequence, the spatial distance between power stations and the correlation coefficient of power generation output are calculated. Power stations with correlation coefficients higher than a preset threshold are grouped into the same cluster to determine the cluster grouping structure of new energy power stations. The power change rate feature sequence of each power station in the cluster grouping structure is obtained, and processed by a clustering algorithm to obtain the quantitative value of the power output synergy effect at the cluster level. The aggregation features are extracted from the quantitative value of the power output synergy effect, and the aggregation features are fused through time series analysis to obtain the power change trend at the regional scale. Based on the power change trend at the regional scale, a hierarchical model from the power station to the region is constructed, and the change indicators of each level in the hierarchical model are obtained. A unified indicator system is obtained by calculating the correlation coefficient. Input real-time power generation output data into the unified index system for simulation calculation. Based on the comparison between the cluster output synergy effect value and the preset synergy threshold in the simulation, adjust or maintain the index weights to determine the optimized index system. Based on the optimized indicator system, and combined with historical dispatch records, power dispatch parameters are output through data fusion processing.

2. The regional power dispatching method based on the power generation characteristics analysis and optimization of new energy power plants as described in claim 1, characterized in that: The determination of the cluster grouping structure for new energy power stations includes: Calculate the spatial distance between stations based on their geographical coordinates. Calculate the correlation coefficient of power generation output between power stations based on the power change rate characteristic sequence; The power generation output correlation coefficient is compared with a preset threshold, and the power stations with outputs exceeding the preset threshold are grouped into the same cluster group to generate a cluster group structure.

3. The regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in claim 2, characterized in that: The formula for calculating the correlation coefficient of power generation output is as follows: ； in: Indicates station and station The correlation coefficient between the power generation outputs of the two groups Indicates station At any moment The characteristic value of the rate of change of power, Indicates station At any moment The characteristic value of the rate of change of power, Indicates station The mean of the characteristic sequence of power change rate, Indicates station The power change rate characteristic sequence mean, where T represents the total length of the time series.

4. The regional power dispatching method based on the power generation characteristics analysis and optimization of new energy power plants as described in claim 1, characterized in that: The obtained quantification value of the output synergy effect at the cluster level includes: Based on the power change rate characteristic sequence, the stations within each cluster are grouped using a clustering algorithm; Calculate the variance within and between clusters to generate a quantitative value of the synergistic effect of cluster output.

5. A regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in claim 4, characterized in that: The formula for calculating the quantitative value of the cluster output synergy effect is as follows: ； in: Indicates the first Quantitative value of the output synergy effect of each cluster. Indicates the first Number of stations within a cluster This represents the variance of the power change rate of station m within the cluster. Represents a cluster Variance of power change rate compared to other clusters This represents the weight coefficient of station m in the cluster.

6. The regional power dispatching method based on the power generation characteristics analysis and optimization of new energy power plants as described in claim 1, characterized in that: The construction of the hierarchical model from the station to the region includes: The model uses stations, clusters, and regions as different levels. Standardize the change indicators at each level; Based on weight allocation, indicators at different levels are weighted and integrated to obtain a unified indicator system with multiple levels, and the comprehensive evaluation value of the unified indicator system is calculated.

7. A regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in claim 6, characterized in that: The formula for calculating the comprehensive evaluation value of the unified indicator system is as follows: ； in: L represents the comprehensive evaluation value of the unified indicator system, and L represents the total number of levels in the hierarchical model. Indicates the first The weighting coefficients of the indicators at different levels. Indicates the first The original values ​​of the hierarchical change indicators Indicates the first The maximum value of the hierarchical indicator, Indicates the first The minimum value of the hierarchical indicator.

8. A regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in claim 1, characterized in that: The process of inputting real-time power generation output data into the unified index system for simulation calculation includes: Real-time power generation output data is input into a unified index system for simulation calculation to calculate the real-time cluster output synergy effect value. The real-time cluster output synergy effect value is compared with the preset synergy threshold. If it is lower than the preset synergy threshold, the weight coefficients of each level of the unified indicator system are adjusted according to the difference between the real-time cluster output synergy effect value and the preset synergy threshold. If the value exceeds the preset collaborative threshold, the existing weights will be maintained, and the unified indicator system will be optimized based on the adjusted indicator weight coefficients.

9. A regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in claim 1, characterized in that: The output power dispatch parameters include: Based on the optimized unified indicator system, extract scheduling parameters; By combining historical dispatch records, power dispatch parameters are generated through data fusion processing.

10. A regional power dispatching system based on the analysis and optimization of power generation characteristics of new energy power plants, based on the regional power dispatching method based on the analysis and optimization of power generation characteristics of new energy power plants as described in any one of claims 1 to 9, characterized in that: include, The data acquisition module is used to collect historical power generation output data and geographic coordinate information of the power station; The feature extraction module is used to extract the power change rate feature sequence through time series analysis; The cluster partitioning module is used to calculate the spatial distance and output correlation coefficient between power stations, and to determine cluster grouping based on thresholds; The synergy effect analysis module is used to cluster the characteristic sequences of stations within the cluster and output the synergy effect quantification value. The hierarchical modeling module is used to construct multi-level power output models from the station to the region and form a unified index system. The real-time simulation and optimization module is used to input real-time data for simulation calculations and dynamically adjust the indicator weights. The dispatch output module is used to combine historical data to perform data fusion and generate power dispatch parameters.

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