Large-scale distributed photovoltaic power generation scene simplified clustering method and device suitable for annual mode calculation of power system

Abstract

The invention discloses a large-scale distributed photovoltaic power generation scene simplified clustering method and device suitable for annual mode calculation of a power system. The method and device are suitable for annual mode calculation of the power system. The method comprises the steps of establishing a sensitivity model between photovoltaic output and line active power flow change, quantifying influence degrees of different photovoltaic scenes on power transmission line power flow, and constructing a disturbance quantity characteristic matrix of a distributed photovoltaic output scene on the power transmission line active power flow; and clustering the large-scale distributed photovoltaic output scenes by taking the disturbance quantity feature vectors as samples. According to the method, typical photovoltaic output scenes with representativeness are constructed, so that the number of redundant scenes is reduced, and typical output characteristics which have the greatest influence on safe operation of the system are reserved, and therefore, the simulation efficiency and the result reliability are improved; depth coupling of photovoltaic scene clustering and power system physical characteristics is realized, and more accurate data support is provided for system power flow analysis, load transfer research, scheduling decision and the like.

Description

Technical Field

[0001] The present invention relates to a simplified clustering method and device for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems, belonging to the field of distributed photovoltaic technology. Background Art

[0002] With the development of new energy technologies, the penetration rate of distributed photovoltaic power generation in power systems continues to increase, becoming an important component in promoting the clean, low-carbon transformation of power systems. Compared to centralized power sources, distributed photovoltaic power generation is characterized by large-scale access, wide geographical distribution, and highly uncertain operating conditions. Its output volatility and randomness pose new challenges to grid operational safety, dispatch stability, and calculation accuracy. In power system planning and operation analysis, in order to fully evaluate the impact of distributed photovoltaic output on various calculation methods such as system power flow, short circuit, and stability, it is usually necessary to construct a large number of typical output scenarios for simulation analysis. If the total installed capacity of distributed photovoltaic power generation within a provincial power grid exceeds 10 million kilowatts, it is considered large-scale distributed photovoltaic power generation. For large-scale photovoltaic sites, directly generating scenarios using full historical data or Monte Carlo simulation methods not only results in high data dimensionality and a large number of samples, but also leads to significant consumption of computing resources and low simulation efficiency.

[0003] To this end, academia and industry generally use clustering analysis methods to reduce the dimensionality of photovoltaic output scenarios, extracting representative typical scenarios to reduce computational complexity. However, existing photovoltaic scenario clustering methods mostly focus on the statistical characteristics of the data itself, such as using K-means, hierarchical clustering, spectral clustering and other methods to extract typical output curves from the original power time series. Such methods have the following shortcomings in practical applications: (1) They do not consider system response characteristics: Existing methods only cluster based on photovoltaic output itself, ignoring the differences in the impact of different output scenarios on key state variables such as the active power flow distribution and voltage level of the power system lines; (2) Clustering is not representative enough: The typical scenarios obtained by clustering may be reasonable in statistical terms, but may not effectively represent the key operating boundaries in system calculations; (3) They cannot reflect system sensitivity: Under some system operating conditions, some small changes in photovoltaic output may cause significant fluctuations in system status, and traditional clustering methods find it difficult to identify these highly sensitive areas. At the same time, since annual calculations usually require consideration of the impact of seasonal and climate change on system load and photovoltaic power generation output, especially when a high proportion of distributed photovoltaics are connected to the power system, traditional scenario generation methods are difficult to efficiently process large-scale data.

[0004] Therefore, there is an urgent need for a cluster analysis method that can comprehensively consider the impact of photovoltaic output on the operating status of the power system (such as node voltage, current, power angle, etc.) from the perspective of the power system. Summary of the Invention

[0005] The purpose of the present invention is to propose a simplified clustering method and device for large-scale distributed photovoltaic power generation scenarios suitable for annual power system mode calculation, fully considering the actual operating status of the power system, guiding scenario clustering to focus on the key response characteristics of the power system, improving the representativeness of clustered scenarios in mode calculation, reducing the number of redundant scenarios while retaining the typical output characteristics that have the greatest impact on the safe operation of the power system, thereby improving simulation efficiency and result reliability.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention provides a simplified clustering method for large-scale distributed photovoltaic power generation scenarios applicable to annual calculation of power systems, comprising:

[0008] Construct large-scale distributed photovoltaic output scenarios;

[0009] Establish a sensitivity model between distributed photovoltaic output and transmission line active power flow changes;

[0010] Based on the sensitivity model, the impact of different photovoltaic output scenarios on the active power flow of the transmission line is quantified, and a characteristic matrix of the disturbance quantity of the distributed photovoltaic output scenario on the active power flow of the transmission line is constructed;

[0011] The large-scale distributed photovoltaic output scenarios are clustered based on the disturbance characteristic matrix.

[0012] Preferably, the construction of a large-scale distributed photovoltaic output scenario includes:

[0013] Collect the historical output data of each distributed photovoltaic power station in the target area to form a photovoltaic output time series matrix, which is expressed as: ,in For the Distributed photovoltaic power stations Always make an effort, ; Number the distributed photovoltaic power station. , is the number of time periods, is the number of distributed photovoltaic power stations in the target area; the time period includes different seasons.

[0014] Preferably, the collecting of historical output data of each distributed photovoltaic power station in the target area includes:

[0015] Obtain the measured historical output data of each distributed photovoltaic power station in the target area, export it at a 15-minute time resolution, and form a multi-node photovoltaic output time series matrix;

[0016] or,

[0017] Through probability modeling and weather simulation, photovoltaic output scenarios under different seasons and climatic conditions are constructed to form a photovoltaic output time series matrix.

[0018] Preferably, the step of establishing a sensitivity model between distributed photovoltaic output and changes in active power flow of transmission lines includes:

[0019] ,

[0020] in, Indicates the The output change of distributed photovoltaic power station affects the Active power flow of transmission lines The sensitivity of the impact, 、 Number the nodes at the beginning and end of the transmission line.

[0021] Preferably, the step of establishing a sensitivity model between distributed photovoltaic output and changes in active power flow of transmission lines further includes:

[0022] Under the basic operating state of the power system, based on the sensitivity model, the output of the distributed photovoltaic power station is increased. , using the DC power flow model or the linearized AC power flow method, calculate the active power flow changes of all transmission lines, and obtain the sensitivity coefficient of the output change of distributed photovoltaic power stations to the active power flow of each transmission line;

[0023] Traverse all distributed photovoltaic power stations and all transmission lines to obtain the sensitivity matrix;

[0024] The calculation process needs to cover the impact of different seasons, load fluctuations and climate changes on the active power flow changes of the transmission lines.

[0025] Preferably, the construction of a characteristic matrix of disturbances caused by distributed photovoltaic output scenarios on the active power flow of transmission lines includes:

[0026] For each distributed photovoltaic power station output, calculate the corresponding transmission line The perturbation vector On this basis, a distributed photovoltaic output scenario is constructed to The disturbance characteristic matrix of active power flow is expressed as follows:

[0027] .

[0028] Preferably, clustering the large-scale distributed photovoltaic output scenarios based on the disturbance characteristic matrix includes:

[0029] Taking the elements in the disturbance characteristic matrix as samples, and performing dimensionality reduction processing on the samples using principal component analysis;

[0030] Use K-Means clustering algorithm to divide the data after dimensionality reduction into kind;

[0031] In each class, the original PV output scenario closest to the class center is selected as the typical PV output scenario.

[0032] In a second aspect, the present invention provides a simplified clustering device for large-scale distributed photovoltaic power generation scenarios applicable to annual calculations of power systems, which is used to implement the simplified clustering method for large-scale distributed photovoltaic power generation scenarios applicable to annual calculations of power systems. The device comprises:

[0033] Data acquisition module, used to construct large-scale distributed photovoltaic output scenarios;

[0034] A correlation module is used to establish a sensitivity model between distributed photovoltaic output and the active power flow changes of transmission lines;

[0035] A sample construction module is used to quantify the impact of different photovoltaic output scenarios on the active power flow of the transmission line based on the sensitivity model, and to construct a characteristic matrix of the disturbance amount of the distributed photovoltaic output scenario on the active power flow of the transmission line;

[0036] A clustering module is used to cluster large-scale distributed photovoltaic output scenarios based on the disturbance characteristic matrix.

[0037] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to perform any one of the simplified clustering methods for large-scale distributed photovoltaic power generation scenarios applicable to the annual calculation of the power system.

[0038] In a fourth aspect, the present invention provides a computing device comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the simplified clustering methods for large-scale distributed photovoltaic power generation scenarios calculated in an annual manner applicable to the above-mentioned power system.

[0039] The beneficial effects achieved by the present invention are as follows:

[0040] The present invention provides a simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual power system mode calculation. By establishing a sensitivity model between photovoltaic output and line active power flow changes, the impact of different photovoltaic scenarios on key transmission line flows is quantified. Line flow sensitivity is used as a clustering feature or weighted indicator to guide scenario clustering to focus on key system response characteristics, thereby improving the representativeness of clustered scenarios in mode calculation. The method reduces the number of redundant scenarios while retaining the typical output characteristics that have the greatest impact on the safe operation of the system, thereby improving simulation efficiency and result reliability.

[0041] This invention achieves a deep coupling of photovoltaic scenario clustering with the physical characteristics of the power system, providing more accurate data support for system power flow analysis, load transfer research, and scheduling decisions. The proposed method can extract typical photovoltaic scenarios with engineering guidance based on a full consideration of the system's actual operating status and grid response patterns, significantly improving the scientificity, efficiency, and practicality of power system analysis and assessment in large-scale distributed photovoltaic access scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of a simplified clustering method for large-scale distributed photovoltaic power generation scenarios applicable to annual calculations of power systems provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0044] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show structures and / or processing steps closely related to the solutions according to the present invention, while other details that are not closely related to the present invention are omitted.

[0045] It should be emphasized that the term "include / comprises" when used herein refers to the existence of features, elements, steps or components, but does not exclude the existence or addition of one or more other features, elements, steps or components.

[0046] It should be emphasized here that the step marks mentioned below do not limit the order of the steps, but it should be understood that the steps can be executed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be executed simultaneously.

[0047] This invention aims to simplify cluster analysis of distributed photovoltaic power generation scenarios, suitable for annual power system calculations. Annual calculations typically require consideration of the impact of seasonal and climate change on system load and photovoltaic power generation output. Traditional scenario generation methods struggle to efficiently process large amounts of data, especially when a high proportion of distributed photovoltaics are connected to the power system. By constructing representative typical photovoltaic output scenarios, this invention reduces the number of redundant scenarios while retaining the typical output characteristics that have the greatest impact on system safety, thereby improving simulation efficiency and result reliability.

[0048] Based on the above invention concept, this embodiment 1 provides a simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems, see Figure 1 ,include:

[0049] S1. Construct a distributed photovoltaic output scenario.

[0050] In this embodiment, historical output data from distributed PV power stations within the target area is collected, or PV output scenarios under different seasons and climate conditions are constructed through probabilistic modeling and weather simulation. This data forms a PV output time series matrix, and the long-term impact on load, power flow, and grid response is considered in the annual calculation to ensure that the scenario data meets the requirements of the annual calculation method.

[0051] In this embodiment, the photovoltaic output time series is expressed as: ,in For the Distributed photovoltaic power stations Always make an effort, ; Number the distributed photovoltaic power station. , is the number of time periods, is the number of distributed photovoltaic power stations in the target area.

[0052] It should be noted that, in this embodiment, the time periods in the photovoltaic processing time series must include different seasons to satisfy the annual calculation method.

[0053] S2. Establish a sensitivity model between photovoltaic output and line active power flow changes.

[0054] In this embodiment, under the basic operating conditions of the power system, a DC power flow model or a linearized AC power flow method is used to calculate the sensitivity coefficient of the injected power of each photovoltaic node in the power system to the active power flow of the target line. Specifically, to meet the annual calculation requirements of the power system, the sensitivity coefficient is adjusted according to different seasons, load fluctuations, and climate changes, ensuring that the extracted sensitivity accurately reflects the power flow changes under different load and photovoltaic power output conditions throughout the year.

[0055] The sensitivity coefficient is expressed as:

[0056] ,

[0057] in, Indicates the Distributed photovoltaic power station injection power Changes to the Active power flow of lines The sensitivity of the impact, 、 Number the nodes at the beginning and end of the line.

[0058] S3. Based on the constructed sensitivity model, quantify the impact of different photovoltaic output scenarios on the active power flow of the transmission line, and construct a characteristic matrix of the disturbance quantity of the distributed photovoltaic output scenario on the active power flow of the transmission line;

[0059] In this example, each PV output scenario is weighted by its sensitivity coefficient to extract its impact on the overall system or on changes in power flows on key lines. Considering the annual calculation, particular attention is paid to the impact of PV generation on the power system under different seasons and climate conditions. Weighted sensitivity coefficients are used to extract the typical scenarios with the greatest impact on annual system safety, ensuring representativeness and accuracy of the calculation.

[0060] For each distributed photovoltaic power station output, calculate the corresponding target line The perturbation vector On this basis, a distributed photovoltaic output scenario is constructed for the target line The disturbance characteristic matrix of active power flow is expressed as follows:

[0061] .

[0062] S4. Based on the above disturbance characteristic matrix, a large number of distributed photovoltaic output scenarios are classified by dimensionality reduction.

[0063] In this embodiment, based on the above distributed photovoltaic output scenario, the target line Disturbance characteristic matrix of active power flow , using clustering algorithms such as K-means and DBSCAN to reduce the dimension and classify a large number of photovoltaic output scenarios. During the clustering process, considering the annual calculation requirements of the power system, the clustering results must be able to represent typical photovoltaic output scenarios under different seasons, loads and extreme climate conditions. The clustering algorithm will weight the characteristics of the annual scenario so that the selected scenario can fully reflect the key operating boundaries of the power grid throughout the year. The system response error or cluster internal indicators (such as silhouette coefficient, CH index) can be used to evaluate the clustering effect and determine the optimal number of clusters. .

[0064] S5. Typical scenario selection and verification

[0065] Representative scenarios (such as centroid samples or maximum density points) are extracted from each cluster category for subsequent applications such as power flow analysis, stability assessment, and scheduling simulation. When selecting scenarios, special attention is paid to typical PV output scenarios that significantly impact the power system during annual periods (such as the peak load period in winter and the peak PV generation period in summer), ensuring that the selected scenarios provide an accurate analytical basis for annual power system calculations. The fidelity of the clustered scenarios in the mode calculations can be assessed by comparing them with the full scenario calculation results.

[0066] Example 2

[0067] This embodiment 2 is an application of the simplified clustering method of the above embodiment 1, and is specifically as follows:

[0068] Step 1: Build a distribution network simulation model

[0069] A typical 10kV distribution network model is established in DIgSILENT, which includes multiple distributed photovoltaic access points (DGs). Each access point has a configuration capacity ranging from 50kW to 500kW, and includes conventional equipment such as load nodes, main transformers, lines, capacitors, etc.

[0070] Step 2: Import PV historical / forecast data

[0071] Import the measured photovoltaic output data of a certain area into the simulation platform at a 15-minute time resolution to form a multi-node photovoltaic output scene time series .

[0072] Step 3: Sensitivity matrix calculation

[0073] Use the power flow analysis function in Power Factory, combined with the linearized model, to construct the sensitivity matrix through the perturbation method or automatic differentiation tools. , that is, the influence coefficient of the change of the injected power of each photovoltaic node on the power flow of each line.

[0074] During the calculation process, the output of each distributed photovoltaic node is increased , record the active power flow changes of all transmission lines and fill in the sensitivity matrix, e.g. It can be 1kW.

[0075] Step 4: Construct the power flow disturbance vector

[0076] For each time point , according to the photovoltaic power station output and sensitivity matrix, calculate the corresponding target line The power flow disturbance vector , and then construct the distributed photovoltaic output scenario for the target line Disturbance characteristic matrix of active power flow .

[0077] Step 5: PCA dimensionality reduction and cluster analysis

[0078] Sample of tidal disturbance Use principal component analysis (PCA) to reduce dimensionality and retain more than 95% of the information;

[0079] Use K-Means clustering algorithm to divide the data after dimensionality reduction into Class (such as );

[0080] In each class, the original PV output scenario closest to the class center is selected as the typical PV output scenario.

[0081] Step 6: Construct a typical simulation condition

[0082] The selected A typical photovoltaic output scenario is used as a representative working condition and imported into the PowerFactory scenario. Through scripts (DSL / Python), batch simulation tasks such as large-scale distributed photovoltaic access to the power system flow analysis, voltage qualification rate statistics, and line overload identification are carried out.

[0083] Example 3

[0084] Based on the above-mentioned inventive concept, this embodiment 3 provides a simplified clustering device for large-scale distributed photovoltaic power generation scenarios applicable to annual calculations of power systems, which is used to implement the simplified clustering method for large-scale distributed photovoltaic power generation scenarios applicable to annual calculations of power systems of the above-mentioned embodiment. The device includes:

[0085] Data acquisition module, used to construct large-scale distributed photovoltaic output scenarios;

[0086] A correlation module is used to establish a sensitivity model between distributed photovoltaic output and the active power flow changes of transmission lines;

[0087] A sample construction module is used to quantify the impact of different photovoltaic output scenarios on the active power flow of the transmission line based on the sensitivity model, and to construct a characteristic matrix of the disturbance amount of the distributed photovoltaic output scenario on the active power flow of the transmission line;

[0088] A clustering module is used to cluster large-scale distributed photovoltaic output scenarios based on the disturbance characteristic matrix.

[0089] It is worth noting that the device embodiment corresponds to the above-mentioned method embodiment, and the implementation methods of the above-mentioned method embodiments are applicable to the device embodiment and can achieve the same or similar technical effects, so they will not be repeated here.

[0090] Example 4

[0091] Based on the above-mentioned inventive concept, this embodiment 4 provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the simplified clustering methods for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to the above-mentioned embodiments.

[0092] Example 5

[0093] Based on the above-mentioned inventive concept, this embodiment 5 provides a computing device, including one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the simplified clustering methods for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to the above-mentioned embodiments.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application 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.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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.

[0096] 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.

[0097] 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.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems, characterized by: include: Construct large-scale distributed photovoltaic output scenarios; Establish a sensitivity model between distributed photovoltaic output and transmission line active power flow changes; Based on the sensitivity model, the impact of different photovoltaic output scenarios on the active power flow of the transmission line is quantified, and a characteristic matrix of the disturbance quantity of the distributed photovoltaic output scenario on the active power flow of the transmission line is constructed; The large-scale distributed photovoltaic output scenarios are clustered based on the disturbance characteristic matrix.

2. A simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to claim 1, characterized in that: The scenario of constructing large-scale distributed photovoltaic output includes: Collect the historical output data of each distributed photovoltaic power station in the target area to form a photovoltaic output time series matrix, which is expressed as: ,in For the Distributed photovoltaic power stations Always make an effort, ; Number the distributed photovoltaic power station. , is the number of time periods, is the number of distributed photovoltaic power stations in the target area; the time period includes different seasons.

3. A simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to claim 2, characterized in that: The historical output data of each distributed photovoltaic power station in the target area is collected, including: Obtain the measured historical output data of each distributed photovoltaic power station in the target area, export it at a 15-minute time resolution, and form a multi-node photovoltaic output time series matrix; or, Through probability modeling and weather simulation, photovoltaic output scenarios under different seasons and climatic conditions are constructed to form a photovoltaic output time series matrix.

4. A simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to claim 2, characterized in that: The establishment of a sensitivity model between distributed photovoltaic output and active power flow changes of transmission lines includes: ， in, Indicates the The output change of distributed photovoltaic power station affects the Active power flow of transmission lines The sensitivity of the impact, 、 Number the nodes at the beginning and end of the transmission line.

5. A simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to claim 4, characterized in that: The establishment of a sensitivity model between distributed photovoltaic output and changes in active power flow of transmission lines also includes: Under the basic operating state of the power system, based on the sensitivity model, the output of the distributed photovoltaic power station is increased. , using the DC power flow model or the linearized AC power flow method, calculate the active power flow changes of all transmission lines, and obtain the sensitivity coefficient of the output change of distributed photovoltaic power stations to the active power flow of each transmission line; Traverse all distributed photovoltaic power stations and all transmission lines to obtain the sensitivity matrix; The calculation process needs to cover the impact of different seasons, load fluctuations and climate changes on the active power flow changes of the transmission lines.

6. A simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to claim 5, characterized in that: The construction of the characteristic matrix of the disturbance quantity of the distributed photovoltaic output scenario on the active power flow of the transmission line includes: For each distributed photovoltaic power station output, calculate the corresponding transmission line The perturbation vector On this basis, a distributed photovoltaic output scenario is constructed to The disturbance characteristic matrix of active power flow is expressed as follows: 。 7. A simplified clustering method for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to claim 6, characterized in that: The clustering of large-scale distributed photovoltaic output scenarios based on the disturbance characteristic matrix includes: Taking the elements in the disturbance characteristic matrix as samples, and performing dimensionality reduction processing on the samples using principal component analysis; Use K-Means clustering algorithm to divide the data after dimensionality reduction into kind; In each class, the original PV output scenario closest to the class center is selected as the typical PV output scenario.

8. A simplified clustering device for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems, characterized by: A simplified clustering method for large-scale distributed photovoltaic power generation scenarios applicable to annual calculation of power systems according to any one of claims 1 to 7 is implemented, the device comprising: Data acquisition module, used to construct large-scale distributed photovoltaic output scenarios; A correlation module is used to establish a sensitivity model between distributed photovoltaic output and the active power flow changes of transmission lines; A sample construction module is used to quantify the impact of different photovoltaic output scenarios on the active power flow of the transmission line based on the sensitivity model, and to construct a characteristic matrix of the disturbance amount of the distributed photovoltaic output scenario on the active power flow of the transmission line; A clustering module is used to cluster large-scale distributed photovoltaic output scenarios based on the disturbance characteristic matrix.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to execute any one of the simplified clustering methods for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to claims 1 to 7.

10. A computing device, characterized in that include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the simplified clustering methods for large-scale distributed photovoltaic power generation scenarios suitable for annual calculation of power systems according to claims 1 to 7.

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