Light-load combined typical scene generation method and device

Abstract

The invention discloses a light-load combined typical scene generation method and device, and relates to the technical field of power distribution network planning, and the method comprises the steps: determining a target meteorological photovoltaic data set and a target meteorological load data set through correlation coefficient analysis based on a meteorological photovoltaic data set and a meteorological load data set, an improved time sequence generative adversarial network model is input for processing to obtain an enhanced meteorological photovoltaic data set and an enhanced meteorological load data set; dimension reduction and interactive clustering are carried out on enhanced meteorological data of the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set to obtain a photovoltaic side meteorological scene and a load side meteorological scene; and after clustering analysis is carried out on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, global-local time sequence matching of a meteorological day scene is carried out on the obtained photovoltaic cluster of each photovoltaic side meteorological scene and the obtained load cluster of each load side meteorological scene to generate a light-load combined typical scene set. According to the scheme, the reliability of the generated light-load combined typical scene is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and in particular to a method and device for generating a typical light-load combined scenario. Background Art

[0002] With the rapid development of distributed photovoltaic power generation in my country, the amount of photovoltaic grid-connected to distribution networks has increased rapidly in recent years. In the distribution network planning projects containing photovoltaic power generation, the existence of photovoltaic power generation has turned the traditional distribution network into an active distribution network. At the same time, the photovoltaic output is greatly affected by environmental factors and has the characteristics of randomness and volatility. The load of the distribution network is not only closely related to the production and life cycle of the people, but also affected by temperature, humidity, precipitation and holidays. It can be seen that the load and photovoltaic power generation scenarios are changeable, and the distribution network is affected by the superposition of distributed photovoltaic uncertainty and load uncertainty, and there are a lot of conflicts and overlaps between scenarios. Therefore, in order to scientifically describe the current status of the distribution network and enable accurate prediction of the photovoltaic-load joint scenario in the region, the scenario generation technology that accurately models the joint behavior of photovoltaic and power loads is crucial.

[0003] In order to accurately describe the original characteristics of photovoltaic output and power load timing scenarios, some studies have generated light-load joint scenarios through parametric and non-parametric methods. The existing scenario generation method based on parameter method generates scenarios by making assumptions about the probability distribution of data and then using sampling. However, simple statistical assumptions are difficult to describe the high-dimensional nonlinear characteristics of data. Existing non-parametric methods are usually machine learning and deep learning methods, which can learn the true distribution of uncertain variables based on data-driven models, but have difficulties in learning the dynamic characteristics, fluctuations, trends and periodic factors within the time series data, resulting in low reliability of the generated typical light-load joint scenarios. Summary of the invention

[0004] The present invention provides a method and device for generating a typical light-load joint scene, which solves the technical problem that in the prior art, when generating a light-load joint scene, a scene generation method based on a parameter method is difficult to describe the high-dimensional nonlinear characteristics of data, and a scene generation method based on a non-parametric method has difficulty in learning the dynamic characteristics, fluctuations, trends and periodic factors within time series data, resulting in low reliability of the generated light-load joint typical scene.

[0005] A first aspect of the present invention provides a method for generating a light-load combined typical scene, comprising:

[0006] Obtain target photovoltaic output data, multiple target meteorological data and target load data within a preset time range, and perform multi-source data fusion on each of the target meteorological data with each of the target photovoltaic output data and each of the target load data to obtain a meteorological photovoltaic data set and a meteorological load data set;

[0007] Based on the correlation coefficients among the target photovoltaic output data, the target meteorological data and the target load data, redundancy of meteorological data is eliminated from the meteorological photovoltaic data set and the meteorological load data set to determine a target meteorological photovoltaic data set and a target meteorological load data set;

[0008] Using an improved temporal generative adversarial network model to perform data enhancement on the target meteorological photovoltaic dataset and the target meteorological load dataset, respectively, to obtain an enhanced meteorological photovoltaic dataset and an enhanced meteorological load dataset;

[0009] Performing dimensionality reduction on the enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, respectively, and performing interactive clustering to obtain a photovoltaic side meteorological scene and a load side meteorological scene;

[0010] Performing cluster analysis on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set respectively to obtain photovoltaic clusters of each photovoltaic-side meteorological scenario and load clusters of each load-side meteorological scenario;

[0011] Based on the global-local time series matching of the meteorological day scenes of each of the photovoltaic clusters and each of the load clusters, a set of typical light-load combined scenes is generated.

[0012] Optionally, the acquiring of target photovoltaic output data, multiple target meteorological data and target load data within a preset time range, and performing multi-source data fusion on each target meteorological data with each target photovoltaic output data and each target load data to obtain a meteorological photovoltaic data set and a meteorological load data set include:

[0013] Obtain initial photovoltaic output data, various initial meteorological data and initial load data within a preset time range;

[0014] Performing data cleaning on each of the photovoltaic output data, each of the meteorological data, and each of the load data to obtain target photovoltaic output data, target meteorological data, and target load data;

[0015] Fusing the target meteorological data with the target photovoltaic output data according to time to generate a meteorological photovoltaic data set;

[0016] The target meteorological data and the target load data are fused according to time to determine the meteorological load data set.

[0017] Optionally, based on the correlation coefficients among the target photovoltaic output data, the target meteorological data and the target load data, eliminating meteorological data redundancy from the meteorological photovoltaic data set and the meteorological load data set, and correspondingly determining the target meteorological photovoltaic data set and the target meteorological load data set, comprises:

[0018] Calculating the peak-to-valley difference of each target meteorological data on a daily basis, and obtaining a plurality of corresponding peak-to-valley difference meteorological data;

[0019] The Pearson correlation coefficient method is used to calculate the correlation coefficient between each of the target photovoltaic output data, each of the target load data and each of the peak-to-valley difference meteorological data;

[0020] Selecting peak-valley difference meteorological data with a correlation coefficient greater than a first correlation threshold with both the target photovoltaic output data and the target load data as target peak-valley difference meteorological data;

[0021] If there are multiple categories of the target peak-to-valley difference meteorological data, based on the correlation coefficients between the various target peak-to-valley difference meteorological data, one category of the target peak-to-valley difference meteorological data with a correlation coefficient greater than a second correlation threshold is selected as a dimensionality reduction meteorological scene;

[0022] The target meteorological data associated with the dimension-reduced meteorological scene is respectively removed from the meteorological photovoltaic data set and the meteorological load data set to obtain a target meteorological photovoltaic data set and a target meteorological load data set.

[0023] Optionally, the dimensionality reduction of the enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set is performed respectively, and interactive clustering is performed to obtain a photovoltaic side meteorological scene and a load side meteorological scene, including:

[0024] Performing peak-to-valley difference calculation and average value calculation on the enhanced meteorological data in the enhanced meteorological photovoltaic data set on a daily basis to obtain a plurality of photovoltaic-side dimension-reduced meteorological data;

[0025] Performing peak-to-valley difference calculation and average value calculation on the enhanced meteorological data in the enhanced meteorological load data set on a daily basis to obtain a plurality of load-side dimension-reduced meteorological data;

[0026] Clustering the photovoltaic side dimension-reduced meteorological data to obtain a plurality of photovoltaic side meteorological scenes and photovoltaic clustering parameters;

[0027] The photovoltaic clustering parameters are used as load clustering parameters to perform cluster analysis on the load-side dimension-reduced meteorological data to obtain a plurality of load-side meteorological scenarios.

[0028] Optionally, performing cluster analysis on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set respectively to obtain photovoltaic clusters of each photovoltaic-side meteorological scene and load clusters of each load-side meteorological scene includes:

[0029] According to each of the photovoltaic side meteorological scenes, corresponding enhanced photovoltaic data are extracted from the enhanced meteorological photovoltaic data set for clustering to obtain photovoltaic clusters of each of the photovoltaic side meteorological scenes;

[0030] According to each of the load-side meteorological scenarios, corresponding enhanced load data are extracted from the enhanced meteorological load data set for clustering to obtain load clusters of each of the load-side meteorological scenarios.

[0031] Optionally, the global-local time series matching of meteorological day scenes based on each of the photovoltaic clusters and each of the load clusters to generate a set of typical light-load combined scenes includes:

[0032] taking the enhanced photovoltaic data closest to the corresponding cluster center in each photovoltaic cluster as the photovoltaic typical scene of each photovoltaic cluster;

[0033] taking the enhanced load data closest to the corresponding cluster center in each load cluster as the load typical scenario of each load cluster;

[0034] Taking the PV side meteorological scene and the load side meteorological scene in the same meteorological scene as a unit, the enhanced meteorological data of each PV typical scene on the day and the corresponding enhanced meteorological data of each load typical scene on the day are used to calculate the dynamic time warping distance and matrix profile distance;

[0035] Using the dynamic time warping distances and the associated matrix profile distances for comprehensive processing, a plurality of comprehensive distances of the typical photovoltaic scenes are obtained;

[0036] Traversing each of the comprehensive distances, and combining each of the photovoltaic typical scenarios with the load typical scenarios associated with the corresponding minimum comprehensive distance into a photovoltaic-load combined typical scenario pair;

[0037] The light-load combined typical scene pairs are used to construct a light-load combined typical scene set according to meteorological scenes.

[0038] A second aspect of the present invention provides a light-load combined typical scene generation device, comprising:

[0039] A data processing module is used to obtain target photovoltaic output data, multiple target meteorological data and target load data within a preset time range, and perform multi-source data fusion on each of the target meteorological data with each of the target photovoltaic output data and each of the target load data to obtain a meteorological photovoltaic data set and a meteorological load data set;

[0040] A data elimination module is used to eliminate meteorological data redundancy from the meteorological photovoltaic data set and the meteorological load data set based on the correlation coefficient between the target photovoltaic output data, the target meteorological data and the target load data, and determine the target meteorological photovoltaic data set and the target meteorological load data set;

[0041] A data enhancement module, used to perform data enhancement on the target meteorological photovoltaic data set and the target meteorological load data set respectively by using an improved temporal generative adversarial network model to obtain an enhanced meteorological photovoltaic data set and an enhanced meteorological load data set;

[0042] A dimension reduction clustering module, used to reduce the dimension of the enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, and perform interactive clustering to obtain the meteorological scene on the photovoltaic side and the meteorological scene on the load side;

[0043] A photovoltaic load clustering module, used to perform cluster analysis on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, respectively, to obtain photovoltaic clusters of each photovoltaic-side meteorological scene and load clusters of each load-side meteorological scene;

[0044] The typical scenario generation module is used to perform global-local time series matching of meteorological day scenarios based on each of the photovoltaic clusters and each of the load clusters to generate a set of typical scenarios for light-load combination.

[0045] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the light-load combined typical scene generation method as described in any one of the above items.

[0046] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the light-load combined typical scene generation method as described in any one of the above items.

[0047] A fifth aspect of the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the light-load combined typical scene generation method as described in any one of the above items.

[0048] It can be seen from the above technical solutions that the present invention has the following advantages:

[0049] The above scheme of the present invention provides a method for generating a typical light-load joint scene, including: obtaining target photovoltaic output data, multiple target meteorological data and target load data within a preset time range, performing multi-source data fusion on each target meteorological data with each target photovoltaic output data and each target load data, and obtaining a meteorological photovoltaic data set and a meteorological load data set; based on the correlation coefficients among the target photovoltaic output data, the target meteorological data and the target load data, eliminating meteorological data redundancy from the meteorological photovoltaic data set and the meteorological load data set, and determining the target meteorological photovoltaic data set and the target meteorological load data set; using an improved time series generative adversarial network model to perform data enhancement on the target meteorological photovoltaic data set and the target meteorological load data set, respectively, to obtain an enhanced meteorological photovoltaic data set and an enhanced meteorological load data set; performing dimensionality reduction on the enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, and performing interactive clustering to obtain a photovoltaic side meteorological scene and a load side meteorological scene; performing cluster analysis on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, respectively, to obtain a photovoltaic cluster of each photovoltaic side meteorological scene and a load cluster of each load side meteorological scene; performing global-local time series matching of meteorological day scenes based on each photovoltaic cluster and each load cluster, and generating a light-load joint typical scene set. Based on the above scheme, the dynamic and static characteristics of the target meteorological photovoltaic data set and the target meteorological load data set are fully mined through the improved time series generative adversarial network model, so that the enhanced meteorological data on the photovoltaic side and the load side can interact with each other in the clustering process, ensuring the consistency of the clustering results on both sides while improving the clustering efficiency. The global-local time series matching method takes into account the global characteristics and local details of the time series data at the same time, and can more accurately match the photovoltaic and load scenarios. In the entire process from correlation analysis to scene matching, the objective meteorological data is fully utilized to couple the photovoltaic and load scenarios, which reduces the influence of subjective factors to a certain extent, improves the authenticity and representativeness of the generated scenarios as a whole, and improves the reliability of the generated typical light-load joint scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 A flowchart of the steps of a light-load combined typical scene generation method provided by an embodiment of the present invention;

[0052] Figure 2 A data processing flow chart of a light-load combined typical scene generation method provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the structure of the Autoformer-TimeGAN model provided in an embodiment of the present invention;

[0054] Figure 4 A structural block diagram of a light-load combined typical scene generation device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The embodiments of the present invention provide a method and device for generating a typical light-load joint scene, which are used to solve the technical problem that in the prior art, when generating a light-load joint scene, the scene generation method based on the parameter method is difficult to describe the high-dimensional nonlinear characteristics of the data, and the scene generation method based on the non-parametric method has difficulty in learning the dynamic characteristics, fluctuations, trends and periodic factors within the time series data, resulting in low reliability of the generated light-load joint typical scene.

[0056] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1 , Figure 1 A flowchart of the steps of a light-load combined typical scene generation method provided in an embodiment of the present invention.

[0058] The present invention provides a light-load combined typical scene generation method, comprising:

[0059] Step 101: Obtain target photovoltaic output data, multiple target meteorological data, and target load data within a preset time range, and perform multi-source data fusion on each target meteorological data with each target photovoltaic output data and each target load data to obtain a meteorological photovoltaic data set and a meteorological load data set.

[0060] Step 101 includes the following sub-steps:

[0061] S11, obtaining initial photovoltaic output data, various initial meteorological data and initial load data within a preset time range;

[0062] S12, performing data cleaning on each photovoltaic output data, each meteorological data and each load data to obtain target photovoltaic output data, target meteorological data and target load data;

[0063] S13, fusing each target meteorological data with each target photovoltaic output data according to time to generate a meteorological photovoltaic data set;

[0064] S14, fusing each target meteorological data with each target load data according to time to determine a meteorological load data set.

[0065] It should be noted that if Figure 2 As shown, first, the initial photovoltaic output data, a variety of initial meteorological data and initial load data are obtained according to a preset time range. This time range can be a range of multiple days, one or more months and one or more years, and can be set according to needs. The initial meteorological data has multiple objective meteorological factors that affect photovoltaic output and load, such as temperature, humidity, precipitation and wind speed;

[0066] In order to lay the foundation for the subsequent comprehensive analysis of the three types of data, it is necessary to ensure that the three types of data are aligned in time and length; in specific implementation, the time alignment is reflected in that the time of any type of data in meteorological, photovoltaic output and load data should be consistent. For example, when the time range of meteorological data is the whole year of 2023, the time range of photovoltaic output and load data should also be the whole year of 2023; the length alignment is reflected in that the length of any type of data in meteorological, photovoltaic output and load data should be consistent. For example, when meteorological data is collected at 96 points per day and 365 days per year, the length is 365*96, then the photovoltaic output and load data should also be collected at 96 points per day and 365 days per year, respectively, and the length is 365*96;

[0067] In order to keep the three types of data consistent on a time scale, it is necessary to clean the initial photovoltaic output data, initial meteorological data, and initial load data, mainly including processing duplicate values, missing values, and outliers. In specific implementation, processing duplicate values ​​means identifying and deleting duplicate records in the data. For example, if a duplicate data point appears at 12:00 on January 1, 2023, the latest or earliest record is retained according to actual needs, and other duplicates are deleted. Processing missing values ​​means filling or deleting blanks or invalid values ​​in the data. For example, if the initial photovoltaic output If the force data is missing during the period of 14:00-16:00 on July 15, 2023, linear interpolation can be used to fill in the missing data before and after this period. If the missing time is greater than the preset missing time range, it is considered that the missing time is long, and historical data of the same time period of the adjacent data is considered to be used for filling; processing outliers is reflected in identifying and processing data that does not conform to the normal range. For example, if the initial load data of a certain day suddenly shows an abnormally high point that far exceeds the historical peak, statistical methods (such as the triple standard deviation method) can be used to identify this outlier and replace it with the historical average or median of the period;

[0068] When data cleaning is completed, the target photovoltaic output data, target meteorological data and target load data are output. According to the time of each data, the target meteorological data and the target photovoltaic output data are fused to obtain a photovoltaic output data set containing meteorological factors, namely, a meteorological photovoltaic data set. The target meteorological data and the target load data are fused to generate a load data set containing meteorological factors, namely, a meteorological load data set.

[0069] Step 102: based on the correlation coefficients among the target photovoltaic output data, the target meteorological data and the target load data, the meteorological data redundancy is eliminated from the meteorological photovoltaic data set and the meteorological load data set to determine the target meteorological photovoltaic data set and the target meteorological load data set.

[0070] Step 102 includes the following sub-steps:

[0071] S21. Calculate the peak-to-valley difference of various target meteorological data on a daily basis, and obtain a plurality of corresponding peak-to-valley difference meteorological data.

[0072] It should be noted that, in this embodiment, the characteristics of meteorology, photovoltaic output and load are taken into consideration. In order to determine which meteorological factors are highly correlated with photovoltaic output and load, the peak-valley difference (PV diff) of daily data in multi-day data contained in various target meteorological data, that is, the peak-valley difference meteorological data, is used as a feature. This can well capture the fluctuation characteristics of the data, which is crucial for understanding the dynamic relationship between meteorology, photovoltaic output and load. The calculation process of the peak-valley difference includes:

[0073] ;

[0074] In the formula, Represents the j-th day data of the ith meteorological factor.

[0075] S22. The Pearson correlation coefficient method is used to calculate the correlation coefficients among each target photovoltaic output data, each target load data and each peak-to-valley difference meteorological data.

[0076] It should be noted that the process of calculating the correlation coefficient using the Pearson correlation coefficient method includes:

[0077] ;

[0078] In the formula, is the first variable, is the second variable, is the covariance between the variables, is the standard deviation between the variables, is the correlation coefficient between the variables;

[0079] According to relevant theories, when When it is around 0.5, it is considered that there is a strong correlation between the two variables. When it is around 0.5, there is a moderate correlation between the two variables.

[0080] In the specific implementation, after using the peak-to-valley difference target meteorological data for dimensionality reduction, the Pearson correlation coefficient method is used to calculate the correlation coefficient between the peak-to-valley difference meteorological data and the target photovoltaic output data and the target load data. For example, the correlation coefficient matrix can be constructed: as follows:

[0081] ;

[0082] In the correlation coefficient matrix, from top to bottom and from left to right, they are air temperature, humidity, precipitation, photovoltaic output and load.

[0083] S23, selecting peak-valley difference meteorological data whose correlation coefficient with both the target photovoltaic output data and the target load data is greater than a first correlation threshold as the target peak-valley difference meteorological data.

[0084] It should be noted that, according to the correlation coefficient matrix, the temperature, humidity and precipitation are highly correlated with photovoltaic output and load. The degree of this high correlation can be determined by setting the first correlation threshold. Therefore, it can be considered that these three types of meteorological data have a strong correlation with photovoltaic output and load, and can be used as target peak-valley difference meteorological data.

[0085] S24. If there are multiple categories of target peak-valley difference meteorological data, based on the correlation coefficients between various target peak-valley difference meteorological data, select one category of target peak-valley difference meteorological data with a correlation coefficient greater than a second correlation threshold as a dimensionality reduction meteorological scenario.

[0086] It should be noted that if there are multiple target peak-valley difference meteorological data, then it is necessary to consider whether there is redundancy between the various target peak-valley difference meteorological data. From the correlation coefficient matrix, it can be seen that the correlation coefficient between temperature and humidity is 0.86, and the correlation is very high. The degree of this high correlation can be determined by setting a second correlation threshold. It can be determined that the two are redundant with each other, and one of the target peak-valley difference meteorological data that needs to be redundantly eliminated can be selected as a dimensionality reduction meteorological scenario.

[0087] S25. Target meteorological data associated with the dimension-reduced meteorological scene are respectively removed from the meteorological photovoltaic dataset and the meteorological load dataset to obtain a target meteorological photovoltaic dataset and a target meteorological load dataset.

[0088] It should be noted that if Figure 2As shown, according to the correlation coefficient analysis, the target meteorological data associated with the dimensionality reduction meteorological scene is eliminated from the meteorological photovoltaic data set and the meteorological load data set. For example, the target meteorological data excluding humidity can be selected, thereby selecting the two meteorological factors of temperature and precipitation and using the peak-to-valley difference as a feature, which can characterize the correlation and meteorological correlation between the meteorological factors and the photovoltaic output and load, and obtain a photovoltaic data set containing non-redundant meteorological data, namely the target meteorological photovoltaic data set, and a load data set containing non-redundant meteorological data, namely the target meteorological load data set.

[0089] Step 103: Use an improved time series generative adversarial network model to perform data enhancement on the target meteorological photovoltaic data set and the target meteorological load data set to obtain an enhanced meteorological photovoltaic data set and an enhanced meteorological load data set.

[0090] It should be noted that, in this embodiment, the improved time-series generative adversarial network model refers to the TimeGAN (Time-series generative adversarial networks, time-series generative adversarial network) model (Autoformer-TimeGAN) improved based on Autoformer; for photovoltaic output, the photovoltaic output in different regions and environments belongs to static characteristics, and dynamic factors such as season, weather and time constitute dynamic characteristics. The power load is closely related to factors such as people's living habits and economic level, so it constitutes a static feature. Changes with time, working days and holidays, and weather changes belong to dynamic characteristics. The original TimeGAN model combines the autoregressive model and GAN (Generative adversarial networks). In addition to the unsupervised adversarial loss of the real sequence and the generated sequence in the traditional GAN, the original data supervision loss is also introduced. By training the above two losses, the generated sequence can fully restore the static and dynamic characteristics of the time series data; on this basis, Autoformer-TimeGAN uses Autoformer to replace the LSTM (Long short-term memory, long short-term memory network), compared with LSTM which processes time series as a whole without explicit data decomposition, Autoformer introduces a time series decomposition module. This feature enables the model to accurately learn the short-term fluctuations, long-term trend changes and multi-scale periodicity of photovoltaic output and load time series data, which enables Autoformer-TimeGAN to fully learn the internal characteristics of the original time series data, thereby generating enhanced meteorological photovoltaic data sets and enhanced meteorological load data sets based on the target meteorological photovoltaic data sets and target meteorological load data sets through the improved time series generative adversarial network model;

[0091] The Autoformer-TimeGAN model consists of four parts: generator, discriminator, embedding function and recovery function. The specific structure is as follows: Figure 3 shown; represents all vectors in the dynamic feature space, represents all vectors in the static feature space, represents the discriminator’s discrimination result, represents Gaussian noise, represents the static feature space after dimensionality reduction by the embedding function, Represents the dynamic feature space at time t after dimensionality reduction; the structures of each part of the Autoformer-TimeGAN model are described as follows:

[0092] The embedding function processes static and dynamic features through recursion. For static features, the embedding function projects them into a low-dimensional space. For dynamic features, the relationship between time points is mined and projected into a low-dimensional space: , , where represents the processing of static features by the embedded function, represents the processing of dynamic features by the embedding function, represents the static features after dimensionality reduction by the embedding function, represents the dynamic characteristics at time t after dimensionality reduction, Represents the high-dimensional dynamic features at time t;

[0093] The reconstruction function reconstructs the low-dimensional vector after dimensionality reduction into a high-dimensional vector in the original space. The process is: , , where Represents the processing of low-dimensional static features by the reconstruction function, represents the processing of dynamic features by the reconstruction function, Represents the reconstructed static features, Represents the dynamic characteristics at time t after reconstruction;

[0094] The generator randomly extracts vectors from the vector space of static and dynamic features and combines the input conditions and inputs them into the low-dimensional latent space. The process is: , , where A generative network representing static features, A generative network representing dynamic features, represents the sampling of the vector space to which the static features belong, represents the sampling of the vector space to which the dynamic features belong, represents the generated static feature vector set, represents the dynamic feature vector set generated at time t, Represents the dynamic feature vector set generated at time t-1;

[0095] The output of the generator and the output of the embedding function are jointly encoded and input into the discriminator. At that time, the discriminator will judge the truth or falsehood by combining the real data, the generated data and the input conditions. If the discriminator outputs 1, it is true, and 0 is false. The process is: , , where Represents the discrimination result of the static features of the input data, represents the discriminant result of the dynamic characteristics of the input data, The discriminant network representing static features, The discriminant network representing dynamic features, when using a bidirectional recurrent network with a feedforward output layer, represents the forward hidden state sequence, represents the reverse hidden state sequence;

[0096] Autoformer-TimeGAN has the following three loss functions during training:

[0097] For the embedding function and the reconstruction function, their goal should be to generate the low-dimensional latent space and reconstruct the high-dimensional original feature space as accurately as possible, introducing the first loss:

[0098] ;

[0099] In the formula, represents the expected distribution;

[0100] There is a classic game between the generator and the discriminator of the model, and the second loss is introduced as:

[0101] ;

[0102] In the formula, Represents the discrimination result of the static characteristics of the original data, Represents the discriminant result of the dynamic characteristics of the original data, Represents the discriminant result of the static features of the generated data, Indicates the discriminant result of the dynamic characteristics of the generated data;

[0103] Introducing unsupervised loss The model focuses on describing the overall probability distribution of time series data, but has not yet learned the point-by-point conditional probability distribution. Therefore, a third loss is introduced to achieve this:

[0104] ;

[0105] The Autoformer-TimeGAN model training process is as follows:

[0106] 1. Initialize the parameters of the embedding function e, reconstruction function r, generator g and discriminator d;

[0107] 2. Original data Perform forward propagation:

[0108] 1) Compute latent representation through embedding network: ;

[0109] 2) Reconstruct data by restoring network calculation: ;

[0110] 3. Calculate reconstruction loss ;

[0111] 4. Update the parameters of the embedding function e and the reconstruction function r to minimize ;

[0112] 5. For random noise vector Perform forward propagation to calculate the generated potential representation through the generator and ;

[0113] 6. Input the real and generated potential representations and conditional information into the discriminator and calculate the unsupervised loss ;

[0114] 7. Update the parameters of the generator g and the discriminator d ( represents the parameter set of the discriminator d):

[0115] 1) Minimize the generator's objective: ;

[0116] 2) Maximize the discriminator’s goal: ;

[0117] 8. Use the conditional information c and the potential representation of the original sequence as input to calculate the potential representation of the next prediction through the generator ;

[0118] 9. Calculate supervision loss ;

[0119] 10. Update the parameters of the embedding function e and the generator g to minimize the supervision loss ;

[0120] 11. Repeat steps 2-10 until convergence or the predetermined number of training times is reached;

[0121] In the specific implementation, it is necessary to set the hyperparameters of the Autoformer-TimeGAN model. The hyperparameter settings in the embodiment of this article are shown in the following table:

[0122] Table 1 Hyperparameters of the Autoformer-TimeGAN model

[0123]

[0124] Step 104: Dimensionality reduction is performed on the enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, and interactive clustering is performed to obtain the meteorological scene on the photovoltaic side and the meteorological scene on the load side.

[0125] Step 104 includes the following sub-steps:

[0126] S31, performing peak-to-valley difference calculation and average value calculation on the enhanced meteorological data in the enhanced meteorological photovoltaic data set on a daily basis to obtain a plurality of photovoltaic-side dimension-reduced meteorological data;

[0127] S32, performing peak-to-valley difference calculation and average value calculation on the enhanced meteorological data in the enhanced meteorological load data set on a daily basis to obtain a plurality of load-side dimension-reduced meteorological data;

[0128] S33, clustering the dimensionality-reduced meteorological data on the photovoltaic side to obtain multiple photovoltaic side meteorological scenes and photovoltaic clustering parameters;

[0129] S34. Using the photovoltaic clustering parameters as the load clustering parameters, cluster analysis is performed on the load-side dimension-reduced meteorological data to obtain multiple load-side meteorological scenarios.

[0130] It should be noted that after the enhanced meteorological photovoltaic dataset and enhanced meteorological load dataset are generated by the improved time series generative adversarial network model, Figure 2 As shown in the figure, considering the characteristics of meteorological factors, photovoltaic output and distribution network load, the peak-to-valley difference and average value are used to reduce the dimension of the enhanced meteorological data to obtain the reduced-dimensional meteorological data. The peak-to-valley difference can characterize the fluctuation characteristics of the curve, and the average value can characterize the size level of the curve. This multi-dimensional feature extraction method helps to capture the characteristics of the data more comprehensively and improves the accuracy of subsequent analysis and generation. The process of average value calculation includes:

[0131] ;

[0132] In the formula, Represents the total number of data points for that day, It represents the value of the data of the jth day of the ith meteorological factor at time t;

[0133] After completing the dimensionality reduction of the enhanced meteorological data, the K-Means algorithm is used to perform interactive K-Means clustering on the dimensionality reduction meteorological data. First, the PV side dimensionality reduction meteorological data is clustered to obtain N types of PV side meteorological scenes and the corresponding PV clustering parameters. The clustering parameters can be understood as including the K value, i.e., the optimal number of clustering clusters, and the clustering center, i.e., the coordinates of the center point of each cluster. Then, the PV clustering parameters are used as the load clustering parameters for clustering the load side dimensionality reduction meteorological data to perform clustering analysis, and N types of load side meteorological scenes are obtained. By using the PV clustering parameters to initialize and guide the clustering of the load side dimensionality reduction meteorological data, the amount of calculation in the clustering stage can be effectively reduced, thereby accelerating the convergence of the algorithm and ensuring the PV The clustering results of the source side and the load side are "aligned" to some extent, that is, their clustering structures are similar, and the categories after classification are consistent, which is helpful for the subsequent generation of typical light-load joint scenes; it is understandable that since the meteorological data of the target meteorological photovoltaic dataset and the target meteorological load dataset come from the same source, their data distribution is basically the same. Therefore, the enhanced meteorological data distribution in the enhanced meteorological photovoltaic dataset and the enhanced meteorological load dataset learned and generated by the improved time series generative adversarial network model is also basically the same, which means that the number of meteorological scene categories of the two datasets is equal, which we represent as N categories; the specific process of K-Means clustering includes:

[0134] 1) Specify the number of clusters K for clustering: In the specific implementation, it can be determined based on the Davies-Bouldin Index (DBI). DBI is an indicator used to evaluate clustering effects. It measures the compactness and dispersion of clusters. A smaller DBI value indicates a good clustering effect, that is, the points within the cluster are compact and the points between clusters are dispersed. In the K-Means algorithm, people always expect the points within each cluster to be as close as possible, while the points in different clusters are as far away as possible. DBI is based on this idea to evaluate the clustering effect. The definition of DBI is as follows:

[0135] ;

[0136] In the formula, represents the number of clusters, represents the average distance from all sample points in cluster a to the cluster center. represents the average distance from all sample points in the jth cluster to the cluster center, represents the distance between the cluster center of the a-th cluster and the cluster center of the j-th cluster, Indicates the similarity between cluster a and cluster b; in actual clustering, by setting Perform multiple clustering (where c is the number of sample points) and select the K value corresponding to the minimum DBI as the optimal number of clusters K;

[0137] 2) Initialize the cluster center according to the selected optimal number of clusters K: In the specific implementation, K data points are randomly selected from the data set as the initial cluster center;

[0138] 3) Assign samples to clusters: For each sample, calculate its distance to each cluster center and assign it to the cluster with the closest distance;

[0139] 4) Update the cluster center: For each cluster, calculate the average value of all samples in the cluster and use the average value as the new cluster center;

[0140] 5) Repeat steps 3) and 4) until the stopping condition is reached; the stopping condition is that the cluster center no longer changes significantly;

[0141] 6) Output clustering results: Finally, K clusters are obtained, and each sample is assigned to a cluster.

[0142] Step 105 : cluster analysis is performed on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set respectively to obtain photovoltaic clusters of each photovoltaic-side meteorological scenario and load clusters of each load-side meteorological scenario.

[0143] Step 105 includes the following sub-steps:

[0144] S41, extracting corresponding enhanced photovoltaic data from the enhanced meteorological photovoltaic data set according to each photovoltaic side meteorological scene, and clustering them to obtain photovoltaic clusters of each photovoltaic side meteorological scene;

[0145] S42. According to each load-side meteorological scenario, corresponding enhanced load data is extracted from the enhanced meteorological load data set for clustering to obtain load clusters of each load-side meteorological scenario.

[0146] It should be noted that the enhanced photovoltaic data corresponding to each photovoltaic side meteorological scene in the enhanced meteorological photovoltaic data set are clustered using the K-Means algorithm, and N types of photovoltaic side meteorological scenes are divided into Q photovoltaic clusters. The enhanced load data corresponding to each load side meteorological scene are clustered using the K-Means algorithm, and N types of load side meteorological scenes are divided into U load clusters.

[0147] Step 106: Perform global-local time series matching of meteorological day scenes based on each photovoltaic cluster and each load cluster to generate a typical scene set of photovoltaic-load combination.

[0148] Step 106 includes the following sub-steps:

[0149] S51. Taking the enhanced photovoltaic data closest to the corresponding cluster center in each photovoltaic cluster as the photovoltaic typical scene of each photovoltaic cluster.

[0150] It should be noted that the data point closest to the cluster center in each photovoltaic cluster is defined as the typical scene of the photovoltaic cluster, so there are Q typical scenes.

[0151] S52. The enhanced load data closest to the corresponding cluster center in each load cluster is used as the typical load scenario of each load cluster, so there are U typical scenarios.

[0152] It should be noted that the data point closest to the cluster center in each load cluster is defined as the typical scenario of the load cluster.

[0153] S53, taking the PV-side meteorological scene and the load-side meteorological scene in the same meteorological scene as a unit, the enhanced meteorological data of the day of each typical PV scene and the enhanced meteorological data of the day of each corresponding typical load scene are used to calculate the dynamic time warping distance and the matrix profile distance.

[0154] It should be noted that in order to achieve accurate matching of typical scenarios of photovoltaic and load, the DTW (dynamic time warping) and MPdist (matrix profile distance) of the meteorological time series data corresponding to the day of the typical scenarios of photovoltaic and load of the same type in the enhanced dataset are calculated by global-local time series matching. DTW mainly captures the global characteristics and overall trends of the time series, while MPdist focuses on identifying local patterns and detailed features.

[0155] Among them, DTW achieves the bending of the time axis by extending and shortening the time series, thereby evaluating the morphological similarity of two time series; for two given time series and , construct the F×V distance matrix , the elements in the matrix are calculated as follows:

[0156] ;

[0157] In the formula, For point and The Euclidean distance between For time series Length, For time series Length;

[0158] The purpose of DTW is to find an optimal curved path , satisfying the point pair arrive There exists a minimum cumulative distance, where q is the number of point pairs in path W:

[0159] ;

[0160] In the formula, is the dynamic time warping distance, is the path element at the oth position in the distance matrix, and usually needs to meet the requirements of boundary, continuity and monotonicity;

[0161] Among them, MPdist is a distance measurement method used to measure the similarity of time series. The calculation formula is as follows:

[0162] ;

[0163] in, and are two time series to be compared, is from arrive The matrix profile distance, is from arrive The matrix profile distance is calculated as:

[0164] ;

[0165] In the formula, For and The distance matrix between express subsequence of With a subsequence of B The Euclidean distance between is the length of the subsequence; the core idea of ​​this formula is to find the most similar subsequence pair between two time series and use this as their distance measure.

[0166] S54, performing comprehensive processing on each dynamic time warping distance and the associated matrix profile distance to obtain multiple comprehensive distances of each typical photovoltaic scene.

[0167] S55. Traverse each comprehensive distance, and combine each photovoltaic typical scene with the corresponding load typical scene associated with the minimum comprehensive distance into a photovoltaic-load combined typical scene pair.

[0168] S56. Use each light-load joint typical scene pair to construct a light-load joint typical scene set according to the meteorological scene.

[0169] It should be noted that by combining the two measurement methods, the similarity of time series data can be evaluated more comprehensively. In the matching process, this embodiment takes the same type of meteorological scene as a unit, traverses each photovoltaic typical scene, and finds the one with the smallest absolute difference with the load typical scene under the comprehensive score of dynamic time warping distance and matrix profile distance as a similar weather day, thereby forming a light-load joint typical scene pair under this type of meteorological scene and constructing a light-load joint typical scene set under N types of meteorological scenes. Each type of meteorological scene contains multiple matching light-load joint typical scene pairs. This global-local combined matching strategy can simultaneously consider the overall similarity of the meteorological scene and the matching degree of local features. Since the matched photovoltaic and load typical scenes have similar global trends and local details, we can regard them as light-load joint typical scenes under the same meteorological conditions; in specific implementation, the dynamic time warping distance and the associated matrix profile distance can be weighted and comprehensively calculated to obtain the comprehensive distance, and the weight can be set to 0.5.

[0170] In this embodiment, the dynamic and static characteristics of the target meteorological photovoltaic data set and the target meteorological load data set are fully mined through the improved time series generative adversarial network model, so that the enhanced meteorological data on the photovoltaic side and the load side can interact with each other in the clustering process, ensuring the consistency of the clustering results on both sides while improving the clustering efficiency. The global-local time series matching method takes into account the global characteristics and local details of the time series data at the same time, and can more accurately match the photovoltaic and load scenarios. In the entire process from correlation analysis to scene matching, objective meteorological data is fully utilized to couple the photovoltaic and load scenarios, which reduces the influence of subjective factors to a certain extent, improves the authenticity and representativeness of the generated scenarios as a whole, and improves the reliability of the generated typical light-load combined scenarios.

[0171] See also Figure 4 , Figure 4 A structural block diagram of a light-load combined typical scene generation device provided in an embodiment of the present invention.

[0172] The present invention provides a light-load combined typical scene generation device, comprising:

[0173] The data processing module 401 is used to obtain target photovoltaic output data, multiple target meteorological data and target load data within a preset time range, and perform multi-source data fusion on each target meteorological data with each target photovoltaic output data and each target load data to obtain a meteorological photovoltaic data set and a meteorological load data set;

[0174] The data elimination module 402 is used to eliminate meteorological data redundancy from the meteorological photovoltaic data set and the meteorological load data set based on the correlation coefficient between the target photovoltaic output data, the target meteorological data and the target load data, and determine the target meteorological photovoltaic data set and the target meteorological load data set;

[0175] The data enhancement module 403 is used to perform data enhancement on the target meteorological photovoltaic data set and the target meteorological load data set respectively by using the improved time series generative adversarial network model to obtain an enhanced meteorological photovoltaic data set and an enhanced meteorological load data set;

[0176] The dimension reduction clustering module 404 is used to reduce the dimension of the enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, and perform interactive clustering to obtain the meteorological scene on the photovoltaic side and the meteorological scene on the load side;

[0177] The photovoltaic load clustering module 405 is used to perform cluster analysis on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set to obtain photovoltaic clusters of each photovoltaic side meteorological scene and load clusters of each load side meteorological scene;

[0178] The typical scenario generation module 406 is used to perform global-local time series matching of meteorological day scenarios based on each photovoltaic cluster and each load cluster to generate a set of typical scenarios for photovoltaic-load combination.

[0179] Optionally, the data processing module 401 is specifically used for:

[0180] Obtain initial photovoltaic output data, various initial meteorological data and initial load data within a preset time range;

[0181] Perform data cleaning on each photovoltaic output data, each meteorological data and each load data to obtain target photovoltaic output data, target meteorological data and target load data;

[0182] The target meteorological data and target photovoltaic output data are integrated according to time to generate a meteorological photovoltaic data set;

[0183] The target meteorological data and target load data are fused according to time to determine the meteorological load data set.

[0184] Optionally, the data elimination module 402 is specifically used for:

[0185] The peak-to-valley difference of various target meteorological data is calculated on a daily basis, and a plurality of corresponding peak-to-valley difference meteorological data are obtained;

[0186] The Pearson correlation coefficient method is used to calculate the correlation coefficients between each target photovoltaic output data, each target load data and each peak-to-valley difference meteorological data;

[0187] Select peak-valley difference meteorological data whose correlation coefficient with target photovoltaic output data and target load data is greater than a first correlation threshold as target peak-valley difference meteorological data;

[0188] If there are multiple categories of target peak-to-valley difference meteorological data, based on the correlation coefficients between various target peak-to-valley difference meteorological data, one category of target peak-to-valley difference meteorological data with a correlation coefficient greater than a second correlation threshold is selected as a dimensionality reduction meteorological scene;

[0189] The target meteorological data associated with the dimension-reduced meteorological scene are removed from the meteorological photovoltaic dataset and the meteorological load dataset to obtain the target meteorological photovoltaic dataset and the target meteorological load dataset.

[0190] Optionally, the dimension reduction clustering module 404 is specifically used for:

[0191] The enhanced meteorological data in the enhanced meteorological photovoltaic data set are subjected to peak-to-valley difference calculation and average value calculation on a daily basis to obtain multiple photovoltaic-side dimension-reduced meteorological data.

[0192] The enhanced meteorological data in the enhanced meteorological load data set are respectively calculated on a daily basis for peak-to-valley difference and average value, to obtain multiple load-side dimension-reduced meteorological data;

[0193] Cluster the dimensionality-reduced meteorological data on the photovoltaic side to obtain multiple photovoltaic side meteorological scenes and photovoltaic clustering parameters;

[0194] Taking photovoltaic clustering parameters as load clustering parameters, cluster analysis is performed on the load-side dimensionality reduction meteorological data to obtain multiple load-side meteorological scenarios.

[0195] Optionally, the photovoltaic load clustering module 405 is specifically used for:

[0196] The enhanced photovoltaic data are clustered to obtain the photovoltaic clusters of each photovoltaic side meteorological scene;

[0197] According to each load-side meteorological scenario, the corresponding enhanced load data are extracted from the enhanced meteorological load data set for clustering to obtain the load clusters of each load-side meteorological scenario.

[0198] Optionally, the typical scenario generating module 406 is specifically used for:

[0199] The enhanced photovoltaic data closest to the corresponding cluster center in each photovoltaic cluster is used as the photovoltaic typical scene of each photovoltaic cluster;

[0200] The enhanced load data closest to the corresponding cluster center in each load cluster is used as the typical load scenario of each load cluster;

[0201] Taking the PV side meteorological scene and the load side meteorological scene in the same meteorological scene as a unit, the enhanced meteorological data of each PV typical scene on the day and the corresponding enhanced meteorological data of each load typical scene on the day are used to calculate the dynamic time warping distance and matrix profile distance;

[0202] The dynamic time warping distances and the associated matrix profile distances are used for comprehensive processing to obtain multiple comprehensive distances for each typical photovoltaic scene;

[0203] Traverse each comprehensive distance, and combine each photovoltaic typical scenario with the corresponding load typical scenario associated with the minimum comprehensive distance into a photovoltaic-load joint typical scenario pair;

[0204] The typical light-load joint scenario pairs are used to construct a light-load joint typical scenario set according to the meteorological scenario.

[0205] An embodiment of the present invention further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the light-load combined typical scene generation method as described in any of the above embodiments.

[0206] An embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the light-load combined typical scene generation method as described in any of the above embodiments are implemented.

[0207] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the light-load combined typical scene generation method as described in any of the above embodiments.

[0208] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0209] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0210] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0211] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0212] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0213] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A light-charge joint typical scene generation method, characterized in that: include: Obtain target photovoltaic output data, multiple target meteorological data and target load data within a preset time range, and perform multi-source data fusion on each of the target meteorological data with each of the target photovoltaic output data and each of the target load data to obtain a meteorological photovoltaic data set and a meteorological load data set; Based on the correlation coefficients among the target photovoltaic output data, the target meteorological data and the target load data, redundancy of meteorological data is eliminated from the meteorological photovoltaic data set and the meteorological load data set to determine a target meteorological photovoltaic data set and a target meteorological load data set; Using an improved temporal generative adversarial network model to perform data enhancement on the target meteorological photovoltaic dataset and the target meteorological load dataset, respectively, to obtain an enhanced meteorological photovoltaic dataset and an enhanced meteorological load dataset; Performing dimensionality reduction on the enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, respectively, and performing interactive clustering to obtain a photovoltaic side meteorological scene and a load side meteorological scene; Performing cluster analysis on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set respectively to obtain photovoltaic clusters of each photovoltaic-side meteorological scenario and load clusters of each load-side meteorological scenario; Based on the global-local time series matching of the meteorological day scenes of each of the photovoltaic clusters and each of the load clusters, a set of typical light-load combined scenes is generated.

2. The light-load combined typical scene generation method according to claim 1 is characterized in that: The target photovoltaic output data, multiple target meteorological data and target load data within a preset time range are obtained, and each target meteorological data is respectively fused with each target photovoltaic output data and each target load data to obtain a meteorological photovoltaic data set and a meteorological load data set, including: Obtain initial photovoltaic output data, various initial meteorological data and initial load data within a preset time range; Performing data cleaning on each of the photovoltaic output data, each of the meteorological data, and each of the load data to obtain target photovoltaic output data, target meteorological data, and target load data; Fusing the target meteorological data with the target photovoltaic output data according to time to generate a meteorological photovoltaic data set; The target meteorological data and the target load data are fused according to time to determine a meteorological load data set.

3. The light-load combined typical scene generation method according to claim 1, characterized in that: The method of eliminating meteorological data redundancy from the meteorological photovoltaic data set and the meteorological load data set based on the correlation coefficient between the target photovoltaic output data, the target meteorological data and the target load data, and correspondingly determining the target meteorological photovoltaic data set and the target meteorological load data set includes: Calculating the peak-to-valley difference of each target meteorological data on a daily basis, and obtaining a plurality of corresponding peak-to-valley difference meteorological data; The Pearson correlation coefficient method is used to calculate the correlation coefficient between each of the target photovoltaic output data, each of the target load data and each of the peak-to-valley difference meteorological data; Selecting peak-valley difference meteorological data whose correlation coefficient with the target photovoltaic output data and the target load data is greater than a first correlation threshold as target peak-valley difference meteorological data; If there are multiple categories of the target peak-to-valley difference meteorological data, based on the correlation coefficients between the various target peak-to-valley difference meteorological data, one category of the target peak-to-valley difference meteorological data with a correlation coefficient greater than a second correlation threshold is selected as a dimensionality reduction meteorological scene; The target meteorological data associated with the dimension-reduced meteorological scene is respectively removed from the meteorological photovoltaic data set and the meteorological load data set to obtain a target meteorological photovoltaic data set and a target meteorological load data set.

4. The light-load combined typical scene generation method according to claim 1 is characterized in that: The enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set are respectively reduced in dimension, and interactively clustered to obtain a photovoltaic side meteorological scene and a load side meteorological scene, including: Performing peak-to-valley difference calculation and average value calculation on the enhanced meteorological data in the enhanced meteorological photovoltaic data set on a daily basis to obtain a plurality of photovoltaic-side dimension-reduced meteorological data; Performing peak-to-valley difference calculation and average value calculation on the enhanced meteorological data in the enhanced meteorological load data set on a daily basis to obtain a plurality of load-side dimension-reduced meteorological data; Clustering the photovoltaic side dimension-reduced meteorological data to obtain a plurality of photovoltaic side meteorological scenes and photovoltaic clustering parameters; The photovoltaic clustering parameters are used as load clustering parameters to perform cluster analysis on the load-side dimension-reduced meteorological data to obtain multiple load-side meteorological scenarios.

5. The light-load combined typical scene generation method according to claim 1 is characterized in that: The cluster analysis is performed on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set respectively to obtain the photovoltaic clusters of each photovoltaic side meteorological scene and the load clusters of each load side meteorological scene, including: According to each of the photovoltaic side meteorological scenes, corresponding enhanced photovoltaic data are extracted from the enhanced meteorological photovoltaic data set for clustering to obtain photovoltaic clusters of each of the photovoltaic side meteorological scenes; According to each of the load-side meteorological scenarios, corresponding enhanced load data are extracted from the enhanced meteorological load data set for clustering to obtain load clusters of each of the load-side meteorological scenarios.

6. The light-charge combined typical scene generation method according to claim 1, characterized in that: The global-local time series matching of meteorological day scenes based on each photovoltaic cluster and each load cluster is performed to generate a typical scene set of light-load combination, including: taking the enhanced photovoltaic data closest to the corresponding cluster center in each photovoltaic cluster as the photovoltaic typical scene of each photovoltaic cluster; taking the enhanced load data closest to the corresponding cluster center in each load cluster as the load typical scenario of each load cluster; Taking the PV side meteorological scene and the load side meteorological scene in the same meteorological scene as a unit, the enhanced meteorological data of each PV typical scene on the day and the corresponding enhanced meteorological data of each load typical scene on the day are used to calculate the dynamic time warping distance and matrix profile distance; Using the dynamic time warping distances and the associated matrix profile distances for comprehensive processing, a plurality of comprehensive distances of the typical photovoltaic scenes are obtained; Traversing each of the comprehensive distances, and combining each of the photovoltaic typical scenarios with the corresponding load typical scenarios associated with the minimum comprehensive distance into a photovoltaic-load combined typical scenario pair; The light-load combined typical scene pairs are used to construct a light-load combined typical scene set according to meteorological scenes.

7. A light-charge combined typical scene generation device, characterized in that: include: A data processing module is used to obtain target photovoltaic output data, multiple target meteorological data and target load data within a preset time range, and perform multi-source data fusion on each of the target meteorological data with each of the target photovoltaic output data and each of the target load data to obtain a meteorological photovoltaic data set and a meteorological load data set; A data elimination module is used to eliminate meteorological data redundancy from the meteorological photovoltaic data set and the meteorological load data set based on the correlation coefficient between the target photovoltaic output data, the target meteorological data and the target load data, and determine the target meteorological photovoltaic data set and the target meteorological load data set; A data enhancement module, used to perform data enhancement on the target meteorological photovoltaic data set and the target meteorological load data set respectively by using an improved temporal generative adversarial network model to obtain an enhanced meteorological photovoltaic data set and an enhanced meteorological load data set; A dimension reduction clustering module, used to reduce the dimension of the enhanced meteorological data in the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, and perform interactive clustering to obtain the meteorological scene on the photovoltaic side and the meteorological scene on the load side; A photovoltaic load clustering module, used to perform cluster analysis on the enhanced meteorological photovoltaic data set and the enhanced meteorological load data set, respectively, to obtain photovoltaic clusters of each photovoltaic-side meteorological scene and load clusters of each load-side meteorological scene; The typical scenario generation module is used to perform global-local time series matching of meteorological day scenarios based on each of the photovoltaic clusters and each of the load clusters to generate a set of typical scenarios for light-load combination.

8. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the light-load combined typical scene generation method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the light-load combined typical scene generation method as described in any one of claims 1-6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the light-load combined typical scene generation method as described in any one of claims 1-6 are implemented.

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