Method and system for dividing operation modes of high-proportion distributed new energy power grids

Abstract

The present invention discloses a method and system for dividing operation modes of a high-proportion distributed new energy power grid. The method obtains and pre-processes the operation data of the power grid, then coordinates the factors affecting the operation mode of the power system, and obtains the characteristics of each factor through logical analysis; defines the characteristic quantity expression corresponding to the characteristics of each factor, calculates the characteristic quantity of each factor and normalizes it; clusters each factor according to the normalized characteristic quantity of each factor, obtains several typical scenes of the factor, and extracts the typical curve of each typical scene of each factor; combines the permutations and combinations of the typical curves of the typical scenes of various factors in actual daily operation, and obtains the normalized operation mode set of the factors actually existing in the power grid; clusters the normalized operation modes of each factor according to the weighted characteristic vector, and obtains a rough set of power grid operation modes, and obtains the final power grid operation mode set through aggregation, merging, calibration and supplementation.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for dividing operating modes for a high-proportion distributed new energy power grid. Background Art

[0002] The power system operating mode describes the typical operating state of the power grid. It not only provides a reference boundary for the safe and stable operation of the power system, but also serves as an important theoretical basis for dispatchers to evaluate the current operating status of the power grid. In traditional power systems primarily powered by thermal and hydropower, the operating mode is primarily influenced by load and seasonal hydropower output. This often relies on simple, fixed, and manually selected classifications such as "high in winter, low in summer, high in summer, and high in flood and low in drought."

[0003] The construction of a new power system, primarily based on renewable energy, is being steadily advanced. Compared to traditional power systems, these systems integrate large-scale renewable energy sources, such as wind power and photovoltaics, resulting in unprecedented increases in grid size and complexity, and a greater diversity of operational modes. Furthermore, the volatility, randomness, and intermittency of renewable energy output integrated into these systems complicates their operation. Consequently, traditional, empirically based methods for classifying power system operating modes struggle to effectively categorize these new power system operating modes. To accurately and efficiently classify these new power systems, it is necessary to comprehensively consider a wider range of influencing factors and fully exploit actual operational data. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for dividing the operating modes of a high-proportion distributed new energy power grid to overcome the defects of the existing technology. Based on the idea of ​​dividing the operating modes of traditional power systems, the present invention further comprehensively considers new energy elements and combines the k-means clustering algorithm to propose a method for dividing the operating modes that is both practical and explainable, and further realizes the problem orientation of the operating mode based on the division logic.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The method for classifying the operation modes of a high-proportion distributed new energy power grid includes the following steps:

[0007] Acquire power grid operation data and pre-process the power grid operation data;

[0008] Based on the pre-processed grid operation data, the factors affecting the operation mode of the power system are comprehensively analyzed, and the characteristics of each factor are obtained through logical analysis;

[0009] Define the feature quantity expression corresponding to each factor feature, calculate the feature quantity of each factor, and normalize the feature quantity of each factor;

[0010] According to the normalized feature quantity of each element, each element is clustered to obtain several typical scenes of the elements, and the typical curve of the typical scene of each element is extracted;

[0011] Combining the permutations and combinations of typical curves of typical scenarios of various elements in actual daily operation, we obtain the combination mode of typical curves of typical scenarios of actual elements, which is used as the normalized operation mode set of elements actually existing in the power grid.

[0012] Establish a weighted feature vector of the operation mode, cluster the normalized operation modes of each element according to the weighted feature vector, and obtain a rough set of power grid operation modes;

[0013] The operation modes in the rough set of power grid operation modes are merged, the special operation modes of the power grid are calibrated, and the potential operation modes of the power grid are supplemented to obtain the final set of power grid operation modes.

[0014] Furthermore, the grid operation data includes grid load, photovoltaic, wind power, thermal power, hydropower, nuclear energy and external power operation data; and the preprocessing includes cleaning and denoising.

[0015] Furthermore, the denoising is specifically as follows: using wavelet transform to decompose the noise-contaminated power grid operation data, and obtaining the noise-removed power grid operation data by reconstructing the processed wavelet coefficients.

[0016] Furthermore, the factors include photovoltaic power, wind power, thermal power, hydropower, nuclear power, external electricity and load. The characteristics of each factor include the power generation, volatility, and correlation between output and load changes of photovoltaic and wind power, the daily power generation of thermal power, hydropower, nuclear power and external electricity, as well as the daily power consumption of load and the daily maximum load utilization time.

[0017] Furthermore, the feature quantity expression corresponding to each element feature is specifically defined as follows:

[0018] 1) Calculate the daily power generation of all power sources or the daily power consumption of loads:

[0019]

[0020] in, is the number of power supply daily data collection, is the sampling period, Indicates the daily power generation of the power source or the daily power consumption of the load. Indicates the daily sampling point number, Indicates the electricity consumption or generation data at the sampling time;

[0021] 2) Define wind power fluctuation quantity:

[0022]

[0023] in, For the Maximum daily wind power output, For the Minimum daily wind power output;

[0024] 3) Define wind power peak offset:

[0025]

[0026] in, For the Serial number of sampling point for daily corresponding wind power output;

[0027] 4) Perform Fourier decomposition on the normalized photovoltaic output to obtain the various frequency intensities after decomposition. Sum the various frequency intensities on the day to obtain the photovoltaic daily fluctuation, which is specifically expressed as follows:

[0028]

[0029]

[0030] in, Indicates the frequency domain sampling point number of Fourier transform, represents the frequency domain intensity, Indicates the photovoltaic power generation per day The amount of effort at any moment, represents the daily fluctuation of photovoltaic power;

[0031] 5) Define PV output and load related quantities:

[0032]

[0033] in, Indicates photovoltaic Daily power generation, Represents the quantity related to photovoltaic and load, Indicates load Daily electricity consumption at each hour;

[0034] 6) Define the maximum load utilization time on the load day:

[0035]

[0036] in, Indicates the maximum load utilization time, Indicates the daily power consumption of the load.

[0037] Furthermore, the calculation obtains the characteristic value of each element and normalizes the characteristic value of each element, specifically:

[0038] Substitute the pre-processed grid operation data into the characteristic quantity expression for calculation, and then normalize the result to obtain the normalized element characteristic quantity: wind power daily power generation series , Wind power daily fluctuation momentum series , Wind power daily peak offset sequence , Photovoltaic daily power generation series , Photovoltaic daily fluctuation momentum series , photovoltaic daily load related quantity series , Thermal power daily power generation series , Hydropower daily power generation series , nuclear power daily power generation series , Daily power quantity series of external power , daily load power consumption sequence , Load day maximum load utilization time series .

[0039] Furthermore, the normalized feature values ​​of each element are used to cluster each element to obtain several typical scenes of the element, and a typical curve of the typical scene of each element is extracted, specifically:

[0040] Based on the normalized feature quantities of each element, a daily feature vector is formed. , photovoltaic day characteristic vector , wind power daily characteristic vector , load day characteristic vector ;

[0041] Based on the k-means clustering algorithm, the Elbow Method and Silhouette Coefficient are used for comprehensive judgment. The combined results are then clustered to obtain typical scenarios for each element, including typical photovoltaic scenario categories. , Typical wind power scenario categories , Typical scenario categories of traditional power supply combination And typical load scenario categories ;

[0042] The mean curve of typical scenarios of the same type of elements is taken as the typical curve of the typical scenario to obtain the mean of the daily data series:

[0043]

[0044] in, For a certain element Typical daily scenes of the day data points, For the The number of days the class runs.

[0045] Furthermore, the normalized operating mode set of elements is expressed as:

[0046]

[0047] in, Represents a set of normalized operation modes of elements, Indicates the number of operating modes.

[0048] Furthermore, the weighted characteristic vector of the operation mode is established, and the normalized operation mode of each element is clustered according to the weighted characteristic vector to obtain a rough set of power grid operation modes, specifically:

[0049] For the normalized operating mode set of actual elements in the power grid , based on the element characteristic quantity, solve the characteristic vector under different operation modes:

[0050]

[0051] in, is the operating mode characteristic vector, grid The superscripts are abbreviated and represent solar Photovoltaic, wind Wind power, thermal Thermal power, water hydropower, nuclear nuclear power, external Incoming calls and load load, j Represents the sample number in each type of operation mode, For the The number of sample curves of the class operation mode;

[0052] Based on the operating mode feature vector, the further weighted definition of the operating mode weighted feature vector is as follows:

[0053]

[0054] in, is the experience weight sequence, Weighted feature vector for the operation mode;

[0055] Weighted feature vector sequence for operation mode Perform k-means cluster analysis again to obtain a rough set of power grid operation modes ,in, Represents the samples in the rough set of running modes, Indicates the number of samples in the rough set of running modes.

[0056] The operation mode classification system for a high-proportion distributed renewable energy grid includes:

[0057] Data acquisition and processing module: used to acquire power grid operation data and pre-process the power grid operation data;

[0058] Data analysis module: used to coordinate the factors affecting the operation mode of the power system based on the pre-processed power grid operation data, and obtain the characteristics of each factor through logical analysis;

[0059] Normalization module: used to define the feature quantity expression corresponding to each factor feature, calculate the feature quantity of each factor, and normalize the feature quantity of each factor;

[0060] The first clustering module is used to cluster each element according to its normalized feature quantity, obtain several typical scenes of the elements, and extract the typical curve of the typical scene of each element;

[0061] Combination module: used to combine the permutations and combinations of typical curves of typical scenarios of various elements in actual daily operation to obtain the combination mode of typical curves of typical scenarios of actual elements as the normalized operation mode set of elements actually existing in the power grid;

[0062] The second clustering module is used to establish a weighted feature vector of the operation mode, cluster the normalized operation mode of each element according to the weighted feature vector, and obtain a rough set of power grid operation modes;

[0063] Merging processing module: used to merge the operating modes in the rough set of power grid operating modes, calibrate the special operating modes of the power grid, and supplement the potential operating modes of the power grid to obtain the final power grid operating mode set.

[0064] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for dividing the operating mode for a high-proportion distributed new energy power grid are implemented.

[0065] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for dividing the operating modes of a high-proportion distributed new energy power grid.

[0066] Compared with the prior art, the present invention has the following beneficial technical effects:

[0067] The present invention comprehensively considers the factors that affect the grid mode and analyzes the relevant characteristics, achieving a more comprehensive and accurate division of the operation mode. The present invention first performs a logical analysis of the factors that affect the grid operation mode, and obtains the factors that affect the operation mode; then, by constructing the characteristic quantity of the factors and performing a clustering on them, the typical scenarios of the factors are obtained, and then the typical curves of the typical scenarios are obtained; then, according to the actual operation data, the typical curves of the typical scenarios of different factors are combined to obtain the normalized operation mode set of the factors that actually exist in the grid, and then the weighted characteristic vector of the operation mode is constructed, and a secondary clustering is performed to obtain the operation mode set of the grid. This method achieves a significant reduction in the complexity of clustering data through the constructed characteristic quantity of the factors, improves the computational efficiency and clustering effect, and can realize visualization of cluster points due to the reduction of the data dimension.

[0068] In addition, the present invention performs secondary clustering by setting experience weights, thereby achieving efficient merging of operation modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0070] Figure 1 This is a flow chart of a method for dividing the operation modes of a high-proportion distributed new energy power grid according to the present invention;

[0071] Figure 2 This is a schematic diagram of the system structure for the operation mode division of the high-proportion distributed new energy power grid of the present invention;

[0072] Figure 3 It is the cluster scatter plot of load characteristic quantity;

[0073] Figure 4 These are typical curves of various elements in a certain operating scenario. DETAILED DESCRIPTION

[0074] In order to enable those skilled in the art to better understand the solutions of the present invention, 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 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 making creative efforts should fall within the scope of protection of the present invention.

[0075] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0076] Example 1

[0077] See also Figure 1 The present invention provides a method for dividing the operation modes of a high-proportion distributed new energy power grid, comprising the following steps:

[0078] Acquire power grid operation data and pre-process the power grid operation data;

[0079] Based on the pre-processed grid operation data, the factors affecting the operation mode of the power system are comprehensively analyzed, and the characteristics of each factor are obtained through logical analysis;

[0080] Define the feature quantity expression corresponding to each factor feature, calculate the feature quantity of each factor, and normalize the feature quantity of each factor;

[0081] According to the normalized feature quantity of each element, each element is clustered to obtain several typical scenes of the elements, and the typical curve of the typical scene of each element is extracted;

[0082] Combining the permutations and combinations of typical curves of typical scenarios of various elements in actual daily operation, we obtain the combination mode of typical curves of typical scenarios of actual elements, which is used as the normalized operation mode set of elements actually existing in the power grid.

[0083] Establish a weighted feature vector of the operation mode, cluster the normalized operation modes of each element according to the weighted feature vector, and obtain a rough set of power grid operation modes;

[0084] The operation modes in the rough set of power grid operation modes are merged, the special operation modes of the power grid are calibrated, and the potential operation modes of the power grid are supplemented to obtain the final set of power grid operation modes.

[0085] This invention comprehensively considers the factors that influence power grid operation and analyzes related characteristics, achieving a more comprehensive and accurate classification of operating modes. The factor feature quantities constructed by this invention significantly reduce the complexity of clustering data, improving computational efficiency and clustering effectiveness. Due to the reduced data dimensionality, cluster point visualization is possible.

[0086] Example 2

[0087] Based on the traditional idea of ​​power system operation mode division, the present invention further comprehensively considers new energy elements and proposes a practical and interpretable operation mode division method in combination with the k-means clustering algorithm, further realizing the problem orientation of the operation mode based on the division logic.

[0088] The specific steps include:

[0089] Step 1: Obtain grid operation data and perform pre-processing of the data by cleaning and denoising;

[0090] First, obtain grid operation data including grid load, photovoltaic, wind power, thermal power, hydropower, nuclear energy and external electricity.

[0091] Since the sources of power grid operation data are diverse and sensors are prone to data loss and delayed transmission during the collection process, it is necessary to eliminate dates with a large number of missing data points. For dates with fewer missing data points, the missing data points are filled by linear interpolation and abnormal data are cleaned.

[0092] Due to the noise pollution generated by the data measurement and transmission process, is the real data of the element running on a certain day, and the noisy data contaminated by noise is expressed as:

[0093] (1)

[0094] in, is the length of the element data for the day, For noise.

[0095] In order to convert the original signal that is not contaminated by noise From the signal contaminated by noise After being recovered, the wavelet transform can be used to decompose the signal contaminated by noise:

[0096] (2)

[0097] in, represents the part containing noise, and the detailed part is represented by , the similar part is expressed as , , the signal after noise removal It can be obtained by reconstructing the wavelet coefficients.

[0098] After data preprocessing, we get photovoltaic intraday series , Wind power daily sequence 、Daily sequence of thermal power , hydropower daily series , nuclear power daily sequence 、Incoming call intraday sequence , load intraday series .

[0099] Step 2: Based on the pre-processed grid operation data, coordinate the factors that affect the operation mode of the power system, and then logically analyze the characteristics of each factor;

[0100] The division of power system operation modes needs to take into account relevant factors, namely the operating scenarios of various types of power sources and loads. For general regional power grids, power sources can be divided into new energy and traditional power sources. Adding the factor of load, the operation of the power grid should be considered from three aspects, namely new energy, traditional power sources, and loads.

[0101] Renewable energy sources primarily include photovoltaic and wind power. Compared to traditional power sources, their daily power generation varies significantly depending on natural conditions and is subject to fluctuations. With the recent increase in the number of renewable energy sources connected to the grid, their absorption has become an increasingly serious issue. Therefore, analysis of renewable energy issues also requires consideration of the correlation between load and renewable energy output. Therefore, analysis of renewable energy sources requires consideration of power generation—both large and small—and its volatility, as well as the correlation between renewable energy output and load fluctuations.

[0102] Traditional power sources include thermal power, hydropower, nuclear energy, and external power. Thermal power, with its mature technology and flexible regulation, is the primary component of traditional power sources. Hydropower has distinct seasonality, and should be prioritized during the high-water season. Nuclear power operations prioritize safety and generally serve as baseload, not participating in daily regulation. External power can supplement local electricity, effectively ensuring regional energy security and grid stability. Therefore, the primary consideration for traditional power sources is daily power generation.

[0103] Load fluctuations are clearly correlated with workday and holiday schedules, as well as the average person's daily routine. While intraday trends generally show similarities, on unusual days, load trends can shift significantly, typically manifesting as reduced power consumption and a lower peak-to-valley ratio. Therefore, load analysis requires consideration of both daily power consumption and the time of peak load utilization.

[0104] The above logical analysis is summarized in the following table:

[0105]

[0106] If each characteristic of each element is regarded as a dimension, there are 12 dimensions in total, and these 12 dimensions form the steady-state operation space of the power grid.

[0107] Step 3: Define the corresponding feature quantity expression of each factor feature, calculate the feature quantity of each factor, and normalize the result;

[0108] Calculate the daily power generation of all power sources or the daily power consumption of loads. This characteristic quantity is as follows:

[0109] (3)

[0110] in, is the length of the element data for the day, is the sampling period, Indicates the daily power generation of the power source or the daily power consumption of the load. Indicates the daily sampling point number, Indicates the electricity consumption or generation data at the sampling time.

[0111] Define wind power fluctuation momentum:

[0112] (4)

[0113] in, For the Maximum daily wind power output, For the Minimum daily wind power output.

[0114] Define wind power peak offset:

[0115] (5)

[0116] in, For the The serial number of the sampling point corresponding to the daily wind power output.

[0117] Although photovoltaic output is affected by natural conditions and has randomness and fluctuations, the light intensity within a day is still regular. Therefore, the photovoltaic output after normalization of the daily output is subjected to Fourier decomposition:

[0118] (6)

[0119] in, Indicates the frequency domain sampling point number of Fourier transform, represents the frequency domain intensity, Indicates the photovoltaic power generation per day The amount of effort at all times.

[0120] The intensity of each frequency component after decomposition is calculated, and the various frequency intensities of the day are summed up to obtain the photovoltaic daily fluctuation:

[0121] (7)

[0122] Define photovoltaic output and load related quantities:

[0123] (8)

[0124] Define the maximum load utilization time on the load day:

[0125] (9)

[0126] in, Indicates the maximum load utilization time, Indicates the daily power consumption of the load.

[0127] Furthermore, the pre-processed grid operation data is substituted into the characteristic quantity expression for calculation, and then the result is normalized to obtain the normalized element characteristic quantity: wind power daily power generation series , Wind power daily fluctuation momentum series , Wind power daily peak offset sequence , Photovoltaic daily power generation series , Photovoltaic daily fluctuation momentum series , photovoltaic daily load related quantity series , Thermal power daily power generation series , Hydropower daily power generation series , nuclear power daily power generation series , Daily power quantity series of external power , daily load power consumption sequence , Load day maximum load utilization time series .

[0128] Step 4: Cluster each element based on its normalized feature quantity to obtain the typical scene of the element or element combination, and extract the typical curve of the typical scene of each element;

[0129] Based on the above defined element characteristics, daily characteristic vectors are formed for the traditional power supply combination. , photovoltaic day characteristic vector , wind power daily characteristic vector , load day characteristic vector .

[0130] Since the time series data has been preprocessed and the characteristic quantities of each element have been defined, a simple and efficient k-means algorithm is adopted.

[0131] The k-means clustering algorithm is a partition-based clustering algorithm that uses distance as a measure of similarity between data. Represents the number of clusters, and the within-cluster sum of squared errors (SSE) between the mass points of each cluster and the sample points within the cluster is called the distortion degree. The expression is as follows:

[0132] (10)

[0133] in, It is kind, yes Sample points in the class, yes The centroid of the class ( The mean of all samples in ).

[0134] K-means takes minimizing the square error between samples and points as the objective function, while the number of clusters in cluster analysis is Determining the value of is crucial to clustering quality. For a cluster, lower distortion indicates a tighter structure within the cluster, while higher distortion indicates a looser structure. Distortion decreases as the number of classes increases. However, for data with a certain degree of differentiation, distortion improves significantly at a certain critical point and then slowly decreases. This critical point can be considered the point of good clustering performance, i.e., the Elbow Method. The Silhouette Coefficient is an evaluation metric for the density and dispersion of classes, as shown in the following formula:

[0135] (11)

[0136] in, is the mean distance between samples in the same cluster, is the mean distance between the sample and the nearest sample in the cluster other than its own cluster, and the Silhouette Coefficient is , the value is [-1,1], the closer to 1, the The more reasonable the value.

[0137] Furthermore, the Elbow Method and Silhouette Coefficient are used to make a comprehensive judgment. The typical scenes of each element are obtained by clustering.

[0138] After clustering, typical photovoltaic scene categories are obtained , Typical wind power scenario categories , Typical scenario categories of traditional power supply combination And load typical scenario categories .

[0139] Furthermore, the mean curve of typical scenarios of the same type of elements is obtained as the characteristic curve of the typical scenario to obtain the mean of the daily data series:

[0140] (12)

[0141] in, For a certain element Typical daily scenes of the day data points, For the The number of days the class runs.

[0142] Step 5: Combine the typical curves of typical scenarios of various elements in actual daily operation to obtain the combination of typical curves of typical scenarios of actual elements, that is, the normalized operation mode set of elements actually existing in the power grid;

[0143] Specifically, combined with the arrangement and combination of typical scenarios of various elements in actual daily operation, that is, the set of normalized operation modes of elements ,in, Represents a set of normalized operation modes of elements, Indicates the number of operating modes.

[0144] Step 6: Establish a weighted feature vector of the operation mode, and cluster the normalized operation modes based on it to obtain a rough set of power grid operation modes;

[0145] For the actual set of operating modes of the power grid , based on the element characteristic quantity, solve the characteristic vector under different operation modes:

[0146] (13)

[0147] in, is the operating mode characteristic vector, grid The superscripts are abbreviated and represent solar Photovoltaic, wind Wind power, thermal Thermal power, water hydropower, nuclear nuclear power, external Incoming calls and load load, j Represents the sample number in each type of operation mode, For the The number of sample curves for this type of operation.

[0148] The weighted feature vector of the operating mode is defined as follows:

[0149] (14)

[0150] in, is the empirical weight sequence, determined by experience, The weighted operating mode feature vector.

[0151] For the operating mode feature vector sequence Perform k-means cluster analysis again to obtain a rough set of power grid operation modes ,in, Represents the samples in the rough set of running modes, Indicates the number of samples in the rough set of running modes.

[0152] Step 7: Manually merge the operation modes in the rough set of power grid operation modes, calibrate the special operation modes of the power grid, and supplement the potential operation modes of the power grid to finally obtain the power grid operation mode set.

[0153] According to the staff's experience, the operation modes in the rough set of power grid operation modes are manually merged, the special operation modes of the power grid are calibrated, and the potential operation modes of the power grid are supplemented to obtain the supplemented operation modes. .

[0154] Example 3

[0155] The actual operation data of the power grid in a certain region is selected for case analysis.

[0156] Actual operation case of a regional power grid: Two years of actual operation data of the regional power grid were obtained, including grid load, photovoltaic power, wind power, thermal power, hydropower, nuclear power, and external power elements. After preliminary comparison, the maximum output of nuclear power and hydropower is far less than the maximum load, so it has no significant impact on the classification of grid operation mode. The load characteristics are clustered, and the load characteristics cluster is obtained as follows: Figure 2 , Figure 2 The characteristic quantity clustering result of the load elements is used to divide the load output curve into 7 categories. Taking all the empirical weights as 1, the typical curves of each element under a certain operation mode are obtained as follows Figure 3 As shown, Figure 3 It represents the output curve of load, photovoltaic power, wind power, thermal power and external power under a certain operating mode.

[0157] Example 4

[0158] See also Figure 2 The present invention provides an operation mode classification system for a high-proportion distributed new energy power grid, comprising:

[0159] Data acquisition and processing module: used to acquire power grid operation data and pre-process the power grid operation data;

[0160] Data analysis module: used to coordinate the factors affecting the operation mode of the power system based on the pre-processed power grid operation data, and obtain the characteristics of each factor through logical analysis;

[0161] Normalization module: used to define the feature quantity expression corresponding to each factor feature, calculate the feature quantity of each factor, and normalize the feature quantity of each factor;

[0162] The first clustering module is used to cluster each element according to its normalized feature quantity, obtain several typical scenes of the elements, and extract the typical curve of the typical scene of each element;

[0163] Combination module: used to combine the permutations and combinations of typical curves of typical scenarios of various elements in actual daily operation to obtain the combination mode of typical curves of typical scenarios of actual elements as the normalized operation mode set of elements actually existing in the power grid;

[0164] The second clustering module is used to establish a weighted feature vector of the operation mode, cluster the normalized operation mode of each element according to the weighted feature vector, and obtain a rough set of power grid operation modes;

[0165] Merging processing module: used to merge the operating modes in the rough set of power grid operating modes, calibrate the special operating modes of the power grid, and supplement the potential operating modes of the power grid to obtain the final power grid operating mode set.

[0166] Example 5

[0167] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for dividing the operating modes for a high-proportion distributed new energy power grid are implemented.

[0168] Example 6

[0169] The present invention provides a computer-readable storage medium, which stores a computer program, and is characterized in that when the computer program is executed by a processor, it implements the steps of the method for dividing the operating mode of a high-proportion distributed new energy power grid.

[0170] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0172] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0174] Finally, 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 its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. The method for dividing the operation modes of a high-proportion distributed new energy power grid is characterized by: The steps include: Acquire power grid operation data and pre-process the power grid operation data; Based on pre-processed grid operation data, factors influencing the operation mode of the power system are comprehensively analyzed, and characteristics of each factor are obtained through logical analysis; the factors include photovoltaic power, wind power, thermal power, hydropower, nuclear power, external power, and load. The characteristics of each factor include the power generation, volatility, and correlation between output and load changes of photovoltaic and wind power, the daily power generation of thermal power, hydropower, nuclear power, and external power, as well as the daily power consumption and daily maximum load utilization time of the load; Define the feature quantity expression corresponding to each factor feature, calculate the feature quantity of each factor, and normalize the feature quantity of each factor; According to the normalized feature quantity of each element, each element is clustered to obtain several typical scenes of the elements, and the typical curve of the typical scene of each element is extracted; Combining the permutations and combinations of typical curves of typical scenarios of various elements in actual daily operation, we obtain the combination mode of typical curves of typical scenarios of actual elements, which is used as the normalized operation mode set of elements actually existing in the power grid. Establish a weighted feature vector of the operation mode, cluster the normalized operation modes of each element according to the weighted feature vector, and obtain a rough set of power grid operation modes; The operation modes in the rough set of power grid operation modes are merged, the special operation modes of the power grid are calibrated, and the potential operation modes of the power grid are supplemented to obtain the final set of power grid operation modes.

2. The method for dividing the operation modes of a high-proportion distributed new energy power grid according to claim 1 is characterized in that: The grid operation data includes grid load, photovoltaic, wind power, thermal power, hydropower, nuclear energy and external power operation data; the preprocessing includes cleaning and denoising.

3. The method for dividing the operation modes of a high-proportion distributed new energy power grid according to claim 2 is characterized in that: The denoising is specifically as follows: using wavelet transform to decompose the noise-contaminated power grid operation data, and obtaining the noise-removed power grid operation data by reconstructing the processed wavelet coefficients.

4. The method for dividing the operation modes of a high-proportion distributed new energy power grid according to claim 1 is characterized in that: The feature quantity expression corresponding to each element feature is specifically defined as follows: 1) Calculate the daily power generation of all power sources or the daily power consumption of loads: Where N is the number of power supply data collected per day, ΔT is the sampling period, and α d represents the daily power generation of the power source or the daily power consumption of the load, k represents the daily sampling point number, x d (k) electricity consumption or generation data at the sampling time; 2) Define wind power fluctuation quantity: Among them, max{x d (k)} is the maximum wind power output on day d, min{x d (k)} is the minimum wind power output on day d; 3) Define wind power peak offset: in, is the serial number of the wind power output sampling point corresponding to day d; 4) Perform Fourier decomposition on the normalized photovoltaic output to obtain the various frequency intensities after decomposition. Sum the various frequency intensities on the day to obtain the photovoltaic daily fluctuation, which is specifically expressed as follows: Among them, k' represents the frequency domain sampling point number of Fourier transform, y d,k' represents the frequency domain intensity, It represents the output of photovoltaic power generation at time k. Represents the daily fluctuation of photovoltaic power; 5) Define photovoltaic output and load related quantities: in, represents the photovoltaic power generation at the time of day d, Represents the quantity related to photovoltaic and load, Indicates the power consumption of the load at the time of day d; 6) Define the maximum load utilization time on the load day: in, Indicates the maximum load utilization time, Indicates the daily power consumption of the load.

5. The method for dividing the operation modes of a high-proportion distributed new energy power grid according to claim 4 is characterized in that: The calculation obtains the characteristic value of each element and normalizes the characteristic value of each element, specifically: Substitute the pre-processed grid operation data into the characteristic quantity expression for calculation, and then normalize the result to obtain the normalized element characteristic quantity: wind power daily power generation series Wind power daily fluctuation momentum series Wind power daily peak offset series Photovoltaic daily power generation series Photovoltaic daily fluctuation momentum series Photovoltaic daily load related quantity series Thermal power daily power generation series Hydropower daily power generation series Nuclear power daily power generation series Daily power series of external power Load daily power consumption sequence Time series of maximum load utilization on load day 6. The method for dividing the operation modes of a high-proportion distributed new energy power grid according to claim 5 is characterized in that: According to the normalized feature quantity of each element, each element is clustered to obtain several typical scenes of the elements, and a typical curve of the typical scene of each element is extracted, specifically: Based on the normalized feature quantities of each element, a daily feature vector is formed. Photovoltaic day feature vector Wind power daily feature vector Load day characteristic vector Based on the k-means clustering algorithm, the Elbow Method and Silhouette Coefficient are used to comprehensively determine the k value, and then the combined results are clustered to obtain the typical scenes of each element, including the typical photovoltaic scene categories. Typical wind power scenario categories Typical scenario categories of traditional power supply combinations And typical load scenario categories The mean curve of typical scenarios of the same type of elements is taken as the typical curve of the typical scenario to obtain the mean of the daily data series: in, is the kth data point of the typical daily scene of the jth category of a certain element, k j is the number of days of the jth type of operation.

7. The method for dividing the operation modes of a high-proportion distributed new energy power grid according to claim 1 is characterized in that: The normalized operating mode set of the elements is expressed as: in, Represents the set of normalized operation modes of elements, k grido Indicates the number of operating modes.

8. The method for dividing the operation modes of a high-proportion distributed new energy power grid according to claim 1 is characterized in that: The weighted characteristic vector of the operation mode is established, and the normalized operation mode of each element is clustered according to the weighted characteristic vector to obtain a rough set of power grid operation modes, specifically: For the normalized operating mode set of actual elements in the power grid Solve the characteristic vectors under different operation modes based on the characteristic quantities of the elements: in, is the characteristic vector of the operation mode. The superscripts in the grid are omitted, representing solar photovoltaic, wind power, thermal power, water power, nuclear power, external power and load respectively. j represents the sample number in each operation mode. D i is the number of sample curves of the i-th type of operation mode; Based on the operating mode feature vector, the further weighted definition of the operating mode weighted feature vector is as follows: Among them, {λ j } is the experience weight sequence, Weighted feature vector for the operation mode; Weighted feature vector sequence for operation mode Perform k-means cluster analysis again to obtain a rough set of power grid operation modes in, Represents the samples in the rough set of running modes, k grid Indicates the number of samples in the rough set of running modes.

9. The operation mode classification system for high-proportion distributed new energy power grid is characterized by: include: Data acquisition and processing module: used to acquire power grid operation data and pre-process the power grid operation data; Data analysis module: used to coordinate the factors affecting the operation mode of the power system based on pre-processed power grid operation data, and obtain the characteristics of each factor through logical analysis; the factors include photovoltaic power, wind power, thermal power, hydropower, nuclear power, external power and load. The characteristics of each factor include the power generation, volatility, and correlation between output and load changes of photovoltaic and wind power; the daily power generation of thermal power, hydropower, nuclear power and external power; as well as the daily power consumption and daily maximum load utilization time of load; Normalization module: used to define the feature quantity expression corresponding to each factor feature, calculate the feature quantity of each factor, and normalize the feature quantity of each factor; The first clustering module is used to cluster each element according to its normalized feature quantity, obtain several typical scenes of the elements, and extract the typical curve of the typical scene of each element; Combination module: used to combine the permutations and combinations of typical curves of typical scenarios of various elements in actual daily operation to obtain the combination mode of typical curves of typical scenarios of actual elements as the normalized operation mode set of elements actually existing in the power grid; The second clustering module is used to establish a weighted feature vector of the operation mode, cluster the normalized operation mode of each element according to the weighted feature vector, and obtain a rough set of power grid operation modes; Merging processing module: used to merge the operating modes in the rough set of power grid operating modes, calibrate the special operating modes of the power grid, and supplement the potential operating modes of the power grid to obtain the final power grid operating mode set.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for dividing the operating mode for a high-proportion distributed new energy power grid are implemented as described in any one of claims 1 to 8.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for dividing the operation mode for a high-proportion distributed new energy power grid are implemented as described in any one of claims 1 to 8.

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