Scene reduction method and system for multi-dimensional operation scene of active power distribution network, terminal and storage medium

Abstract

The invention belongs to the technical field of power distribution network operation management, and particularly provides a scene reduction method and system for a multi-dimensional operation scene of an active power distribution network, a terminal and a storage medium. Respectively generating a corresponding load power scene, a photovoltaic output power scene and a wind power output power scene; distributing a corresponding occurrence probability for each generated scene; combining the scenes generated by different factors based on the occurrence probability of each scene to obtain an initial scene set; after a large number of initial scenes are generated, clustering analysis is performed on the scenes by using a preset clustering algorithm, so that typical output working conditions capable of representing original data features are screened out to serve as a reduced scene set. According to the process, the number of scenes is effectively reduced, the complexity of subsequent optimization calculation is reduced, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network operation management, and specifically relates to a scenario reduction method, system, terminal and storage medium for multi-dimensional operation scenarios of an active distribution network. Background Art

[0002] With the rapid development of distributed generation technologies (such as wind power and photovoltaic power generation), active distribution networks have become an important part of future smart grids. Active distribution networks include distributed generation units, loads, energy storage devices, etc. The operating status of these components has significant uncertainty. For example, the output power of wind power and photovoltaic power generation fluctuates greatly due to weather, and load demand varies with factors such as seasons, working days and holidays. When optimizing the operation of active distribution networks, these uncertain factors need to be considered to formulate reasonable operation plans.

[0003] Traditionally, the optimization of distribution network scheduling mainly relies on deterministic models, which assumes that all input parameters are certain. However, in actual projects, due to the existence of various uncertain factors, this method is difficult to accurately reflect the actual operating status of the distribution network. In addition, the large number of scenarios generated contain many low-probability scenarios that have little impact on the final operating plan. The existence of these scenarios not only increases the computational burden, but may also interfere with the optimization results. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a scenario reduction method, system, terminal and storage medium for multi-dimensional operation scenarios of an active distribution network to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a scenario reduction method for multi-dimensional operation scenarios of an active distribution network, comprising: According to the uncertain factors of load power value, photovoltaic power value and wind power value, the corresponding load power scenario, photovoltaic output power scenario and wind power output power scenario are generated respectively; Assign a corresponding probability of occurrence to each generated scenario; Based on the probability of occurrence of each scenario, the scenarios generated by different factors are combined to obtain an initial scenario set; The initial scene set is clustered and analyzed using a preset clustering algorithm to obtain a typical output condition that can reflect the characteristics of the original data as a reduced scene set.

[0006] Further improvements of the technical solution include generating corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios respectively based on a preset random sampling algorithm according to the uncertain factors of load power values, photovoltaic power values ​​and wind power values, and the specific methods include: Based on the Monte Carlo random sampling algorithm, the corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios are generated according to the uncertainties of load power values, photovoltaic power values ​​and wind power values. Among them, the uncertainty factors of load power cover working days and holidays in spring, summer, autumn and winter, the uncertainty factors of photovoltaic output power include sunny days, cloudy days and rainy days, and the uncertainty factors of wind power output power include spring, summer, autumn and winter.

[0007] A further improvement of the technical solution is to assign a corresponding probability of occurrence to each generated scenario, and the method specifically includes: According to the uncertain factors of load power value, photovoltaic power value and wind power value, their respective probability distribution functions are determined respectively; The probability distribution function of each power value is layered, and the cumulative probability curve is divided into several equal intervals, and the width of each interval is equal; In each stratum of the probability distribution function of each power value, a sample point is randomly selected; Combine the sample points extracted from load, photovoltaic and wind power to generate an operation scenario; According to the hierarchical position of each power value in the respective probability distribution function and the width of the hierarchical layer, the occurrence probability of each power value in the scenario is calculated.

[0008] A further improvement of the technical solution is to use the K-means clustering algorithm to perform cluster analysis on the initial scene set, and the specific method includes: Randomly select the initial centers of k classes; In the i-th iteration, the distances between all samples and each cluster center are calculated, and each sample is divided into the category where the center with the shortest distance is located; Use the mean of each class to update the central value of each category; Determine whether the convergence condition is met; If not, continue iterating and return to calculate the distance between all samples and each cluster center.

[0009] A further improvement of the technical solution is that the convergence condition is: in each iteration, the change of the cluster center is calculated, and if the change of the cluster center is less than a preset threshold, the iteration is stopped.

[0010] Further improvements of this technical solution also include: The scene set obtained after cluster analysis is reduced is screened according to the preset screening conditions, and scenes that do not meet the preset screening conditions are eliminated; The filtered scene set is optimized by merging scenes with high similarity or adjusting the probability of occurrence of scenes.

[0011] A further improvement of the technical solution is that the screening conditions include representativeness of the scene and a threshold of occurrence probability.

[0012] In a second aspect, the present invention provides a scenario reduction system for multi-dimensional operation scenarios of an active distribution network, comprising: A scenario generation module is used to generate corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios respectively based on a preset random sampling algorithm and according to the power value of the load, the power value of the photovoltaic power and the power value of the wind power; The scenario probability calculation module is used to determine the respective probability distribution functions according to the uncertain factors of the load power value, the photovoltaic power value and the wind power value, and calculate the corresponding occurrence probability of each scenario according to the respective probability distribution functions; A scenario combination module is used to combine scenarios generated by different factors based on the probability of occurrence of each scenario to obtain an initial scenario set; The scenario reduction module is used to perform cluster analysis on the initial scenario set using a preset clustering algorithm to obtain a typical output condition that can reflect the characteristics of the original data as a reduced scenario set.

[0013] In a third aspect, a terminal is provided, including: processor, memory, wherein: The memory is used to store computer programs. The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0014] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0015] The beneficial effects of the present invention are: Improve the accuracy of optimized scheduling: By generating multiple scenarios of load power, photovoltaic output power and wind power output power based on a random sampling algorithm and considering their occurrence probabilities, the present invention can more comprehensively reflect the operating status of the distribution network under different conditions. This method overcomes the limitation of traditional deterministic models that cannot accurately handle uncertainty, making the optimized scheduling scheme closer to reality and improving the accuracy and reliability of scheduling.

[0016] Reduce computational complexity: After generating a large number of initial scenarios, the present invention uses a preset clustering algorithm to perform cluster analysis on the scenarios, thereby screening out typical output conditions that can represent the characteristics of the original data as a reduced set of scenarios. This process effectively reduces the number of scenarios, reduces the complexity of subsequent optimization calculations, and improves computational efficiency.

[0017] Eliminate interference from low-probability scenarios: The initial scenario set often contains a large number of low-probability scenarios that have little impact on the final operation plan. These scenarios not only increase the computational burden, but may also cause unnecessary interference to the optimization results. Through cluster analysis, the present invention can eliminate or merge these low-probability scenarios to ensure that the optimization results are more stable and reliable.

[0018] Enhanced system adaptability: By taking into account the changes in load demand due to seasons, working days and holidays, as well as the volatility of wind power and photovoltaic power output affected by weather, the method of the present invention enables the optimization scheduling scheme to better adapt to the actual operation of the distribution network. This adaptability helps to improve the overall performance and stability of the system and reduce operational risks.

[0019] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0022] Figure 2 Graph showing the actual wind power output data of the wind turbine in the embodiment.

[0023] Figure 3 This is the wind power output data diagram of the wind turbine after the scene reduction of the wind turbine is performed using the K-means clustering algorithm.

[0024] Figure 4 This is the wind power output data diagram of the wind turbine after the scene reduction of the wind turbine is performed using the fuzzy C-means (FCM) clustering algorithm.

[0025] Figure 5 is a schematic block diagram of a system according to an embodiment of the present invention.

[0026] Figure 6 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention.

[0027] 210 is a scene generation module, 220 is a scene probability calculation module, 230 is a scene combination module, and 240 is a scene reduction module. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions in 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 described embodiments 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 should fall within the scope of protection of the present invention.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0030] The scenario reduction method for multi-dimensional operation scenarios of an active distribution network provided in an embodiment of the present invention is executed by a computer device, and accordingly, the scenario reduction system for multi-dimensional operation scenarios of an active distribution network runs in the computer device.

[0031] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a scenario reduction system for a multi-dimensional operation scenario of an active distribution network. According to different requirements, the order of the steps in the flowchart may be changed, and some may be omitted.

[0032] like Figure 1 As shown, the method includes: Step 110, generating corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios respectively according to the uncertainties of the load power value, the photovoltaic power value and the wind power value; Step 120, assigning a corresponding probability of occurrence to each generated scenario; Step 130, combining the scenarios generated by different factors based on the occurrence probability of each scenario to obtain an initial scenario set; Step 140 , performing cluster analysis on the initial scene set using a preset clustering algorithm to obtain a typical output operating condition that can reflect the characteristics of the original data as a reduced scene set.

[0033] To facilitate understanding of the present invention, the following is a further description of the scene reduction method for multi-dimensional operation scenarios of an active distribution network provided by the present invention based on the principle of the scene reduction method for multi-dimensional operation scenarios of an active distribution network of the present invention and the process of scene reduction of multi-dimensional operation scenarios of an active distribution network in an embodiment.

[0034] Among them, based on a preset random sampling algorithm, according to the uncertainties of the load power value, photovoltaic power value and wind power value, corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios are generated respectively. The specific method includes: based on a Monte Carlo random sampling algorithm, according to the uncertainties of the load power value, photovoltaic power value and wind power value, corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios are generated respectively. Among them, the uncertainty factors of load power cover working days and holidays in spring, summer, autumn and winter, the uncertainty factors of photovoltaic output power include sunny days, cloudy days and rainy days, and the uncertainty factors of wind power output power include spring, summer, autumn and winter.

[0035] In addition, a corresponding probability of occurrence is assigned to each generated scenario, and the method specifically includes: S121. Determine the probability distribution functions of the load power value, the photovoltaic power value, and the wind power value according to the uncertainties; S122, performing layered processing on the probability distribution function of each power value, dividing the cumulative probability curve into a number of equal intervals, each interval having an equal width; S123, randomly selecting a sample point in each layer of the probability distribution function of each power value; S124, combining sample points extracted from load, photovoltaic power, and wind power to generate an operation scenario; S125. Calculate the occurrence probability of each power value in the scenario according to the layer position of each power value in the respective probability distribution function and the width of the layer.

[0036] In addition, the K-means clustering algorithm and the fuzzy C-means (FCM) clustering algorithm may be used to perform cluster analysis on the initial scene set using a preset clustering algorithm; wherein, the steps of performing cluster analysis on the initial scene set using the K-means clustering algorithm specifically include: S151, randomly select the initial centers of k classes; S152, in the i-th iteration, calculating the distance between all samples and each cluster center, and classifying each sample into the category where the center with the shortest distance is located; S153, using the mean of each class to update the central value of each category; S154, judging whether the convergence condition is met; if not, returning to S152 to continue iteration, and calculating the distances between all samples and each cluster center.

[0037] The convergence condition is as follows: in each iteration, the change of the cluster center is calculated, and if the change of the cluster center is less than a preset threshold, the iteration is stopped.

[0038] Specifically, k scenes are randomly selected from the initial scene set as cluster centers, and each cluster center represents a possible typical output condition. In the i-th iteration, the distance between all scenes (samples) and each cluster center is calculated. The calculation formula is: ; in, For the jth scene, is the kth cluster center, and are the values ​​of the jth scenario and the kth cluster center in the mth dimension (load power, photovoltaic power, wind power), is the weight of the mth dimension (determined based on the probability of occurrence).

[0039] The center value of each category is updated using the mean of each category, that is, the new cluster center is the mean of all scenes of this category in each dimension, and the probability of occurrence is considered for weighting: ; in, Represents the cluster center value of the mth dimension of the kth category after update; represents the jth scene belonging to the kth category; is the probability of occurrence of the jth scenario (or the weight calculated based on the probability of occurrence).

[0040] Furthermore, the scene reduction method also includes: The scene set obtained after cluster analysis is reduced is screened according to the preset screening conditions, and scenes that do not meet the preset screening conditions are eliminated; The filtered scene set is optimized by merging scenes with high similarity or adjusting the probability of occurrence of scenes.

[0041] Among them, the screening conditions include the representativeness of the scenario and the occurrence probability threshold.

[0042] Specifically, different scenarios have different probabilities of occurrence, and their impact on the final operation plan is also different. The uncertain factors that need to be considered in the scenario mainly include the power values ​​of load, photovoltaic, and wind power, the number of scenarios of load power, and the number of scenarios of wind power. , covering working days and holidays in spring, summer, autumn and winter, with the probability of occurrence respectively , , , , , , , ; Number of scenarios for photovoltaic output power , mainly including sunny, cloudy and rainy days, with the probability of occurrence respectively , , ; Number of wind power output scenarios , including spring, summer, autumn and winter, with the probability of occurrence respectively , , , Combining the above scenarios, we can get Considering that the number of load power scenarios and wind power scenarios both take into account seasonal factors, the scenarios can be reduced to kind.

[0043] Due to the volatility of distributed power sources such as wind power and photovoltaic power, it is necessary to reduce the scenario. For example, the implementation object is a wind turbine, and its output level is taken from the measured wind power output data of a certain place, such as Figure 2 In this example, based on a large amount of historical data of wind power operation, the K-means clustering algorithm and the fuzzy C-means (FCM) clustering algorithm are used to reduce the scene and obtain the typical output curve of the wind turbine, as shown in the figure. Figure 3 and Figure 4 As shown in Figure 2, it can be seen that similar results can be obtained regardless of K-means hard partitioning or FCM soft partitioning.

[0044] In some embodiments, the scenario reduction system 200 for multi-dimensional operation scenarios of the active power distribution network may include a plurality of functional modules composed of computer program segments. The computer programs of the various program segments in the scenario reduction system 200 for multi-dimensional operation scenarios of the active power distribution network may be stored in the memory of a computer device and executed by at least one processor to execute (see Figure 1 Description) Scenario reduction function for multi-dimensional operation scenarios of active distribution networks.

[0045] In this embodiment, the scenario reduction system 200 for multi-dimensional operation scenarios of the active power distribution network can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules may include: a scene generation module 210, a scene probability calculation module 220, a scene combination module 230 and a scene reduction module 240. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0046] Specifically, the scenario generation module is used to generate corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios respectively according to the power values ​​of the load, photovoltaic power values ​​and wind power values ​​based on a preset random sampling algorithm; the scenario probability calculation module is used to determine their respective probability distribution functions according to the uncertain factors of the load power value, photovoltaic power value and wind power value, and calculate the corresponding occurrence probability of each scenario according to their respective probability distribution functions; the scenario combination module is used to combine the scenarios generated by different factors based on the occurrence probability of each scenario to obtain an initial scenario set; the scenario reduction module is used to perform cluster analysis on the initial scenario set using a preset clustering algorithm to obtain a typical output condition that can reflect the characteristics of the original data as a reduced scenario set.

[0047] Figure 3 A structural schematic diagram of a terminal 300 provided in an embodiment of the present invention, wherein the terminal 300 can be used to execute a scenario reduction method for multi-dimensional operation scenarios of an active distribution network provided in an embodiment of the present invention.

[0048] The terminal 300 may include: a processor 310, a memory 320 and a communication module 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0049] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0050] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0051] The communication module 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0052] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0053] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0054] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0055] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 systems or modules, which can be electrical, mechanical or other forms.

[0056] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0057] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0058] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A scenario reduction method for multi-dimensional operation scenarios of an active distribution network, characterized in that: include: According to the uncertain factors of load power value, photovoltaic power value and wind power value, the corresponding load power scenario, photovoltaic output power scenario and wind power output power scenario are generated respectively; Assign a corresponding probability of occurrence to each generated scenario; Combine the scenarios generated by different factors to obtain an initial scenario set; The preset clustering algorithm is used to perform cluster analysis on the initial scenario set based on the occurrence probability of each scenario, so as to obtain a typical output condition that can reflect the characteristics of the original data as a reduced scenario set.

2. The scenario reduction method for multi-dimensional operation scenarios of an active power distribution network according to claim 1, characterized in that: According to the uncertain factors of the load power value, the photovoltaic power value and the wind power value, the corresponding load power scenario, the photovoltaic output power scenario and the wind power output power scenario are generated respectively, and the specific method includes: Based on the Monte Carlo random sampling algorithm, the corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios are generated according to the uncertainties of load power values, photovoltaic power values ​​and wind power values. Among them, the uncertainty factors of load power cover working days and holidays in spring, summer, autumn and winter, the uncertainty factors of photovoltaic output power include sunny days, cloudy days and rainy days, and the uncertainty factors of wind power output power include spring, summer, autumn and winter.

3. The scenario reduction method for multi-dimensional operation scenarios of an active power distribution network according to claim 2, characterized in that: Assigning a corresponding probability of occurrence to each generated scenario, the method specifically includes: According to the uncertain factors of load power value, photovoltaic power value and wind power value, their respective probability distribution functions are determined respectively; The probability distribution function of each power value is layered, and the cumulative probability curve is divided into several equal intervals, and the width of each interval is equal; In each stratum of the probability distribution function of each power value, a sample point is randomly selected; Combine the sample points extracted from load, photovoltaic and wind power to generate an operation scenario; According to the hierarchical position of each power value in the respective probability distribution function and the width of the hierarchical layer, the occurrence probability of each power value in the scenario is calculated.

4. The scenario reduction method for multi-dimensional operation scenarios of an active power distribution network according to claim 1, characterized in that: The K-means clustering algorithm is used to perform cluster analysis on the initial scene set. The specific method includes: Randomly select the initial centers of k classes; In the i-th iteration, the distances between all samples and each cluster center are calculated, and each sample is divided into the category where the center with the shortest distance is located; Use the mean of each class to update the central value of each category; Determine whether the convergence condition is met; If not, continue iterating and return to calculate the distance between all samples and each cluster center.

5. The scenario reduction method for multi-dimensional operation scenarios of an active power distribution network according to claim 4, characterized in that: The convergence condition is: in each iteration, the change of the cluster center is calculated. If the change of the cluster center is less than the preset threshold, the iteration is stopped.

6. The scenario reduction method for multi-dimensional operation scenarios of an active power distribution network according to claim 1, characterized in that: Also includes: The scene set obtained after cluster analysis is reduced is screened according to the preset screening conditions, and scenes that do not meet the preset screening conditions are eliminated; The filtered scene set is optimized by merging scenes with high similarity or adjusting the probability of occurrence of scenes.

7. The scenario reduction method for multi-dimensional operation scenarios of an active power distribution network according to claim 6, characterized in that: The screening criteria include the representativeness of the scenario and the threshold of the probability of occurrence.

8. A scenario reduction system for multi-dimensional operation scenarios of an active distribution network, characterized in that: include: A scenario generation module is used to generate corresponding load power scenarios, photovoltaic output power scenarios and wind power output power scenarios respectively based on a preset random sampling algorithm and according to the power value of the load, the power value of the photovoltaic power and the power value of the wind power; The scenario probability calculation module is used to determine the respective probability distribution functions according to the uncertain factors of the load power value, the photovoltaic power value and the wind power value, and calculate the corresponding occurrence probability of each scenario according to the respective probability distribution functions; A scenario combination module is used to combine scenarios generated by different factors based on the probability of occurrence of each scenario to obtain an initial scenario set; The scenario reduction module is used to perform cluster analysis on the initial scenario set using a preset clustering algorithm to obtain a typical output condition that can reflect the characteristics of the original data as a reduced scenario set.

9. A terminal, characterized in that: include: processor; A memory for storing execution instructions of the processor; The processor is configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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