Power system key factor set influence quantification method and system considering uncertainty coupling and depth

Abstract

The invention discloses a power system key factor set influence quantification method and system considering uncertainty coupling and depth. The method comprises the following steps: establishing an uncertainty factor library; screening a potential uncertainty factor combination from an uncertainty factor library according to historical uncertainty factor data and online prediction information of the target area; generating a source-load power sequence corresponding to each potential uncertainty factor combination by using a quantitative mapping relation from the uncertainty factors to the source-load power sequence; calculating a corresponding loss index according to the source-load power sequence, grading the loss index to obtain a loss grading result, and selecting an expected missing power supply quantity as the loss index; grading the probability of occurrence of each potential uncertainty factor combination to obtain a probability grading result; obtaining a risk grading result and a sequence according to the loss grading result and the probability grading result; and acquiring key uncertain factor sets in different seasons and different time periods according to a risk grading result and sorting.

Description

Technical Field

[0001] This application belongs to the field of power system technology and relates to a method and system for quantifying the combined influence of key factors in power systems that take into account uncertainty coupling and depth. Background Technology

[0002] Multiple power outages / curtailment events have demonstrated that their causes encompass multi-dimensional uncertainties related to information, physical factors, and social factors within and outside the power system. Furthermore, with the rapid increase in the penetration rate of new energy sources in emerging power systems, the risk of system outages continues to rise. Therefore, as a crucial link in identifying the targets and boundaries for power system security and stability defense, rapidly assessing the impact of multi-dimensional uncertainties on security defense and using this assessment to extract key uncertainty factor sets—achieving efficient dimensionality reduction of uncertainties and avoiding combinatorial analysis explosion—can effectively improve the efficiency of online defense in handling multi-dimensional uncertainties and ensure the safe and stable operation of emerging power systems.

[0003] However, on the one hand, internal and external uncertainties such as natural disasters, weather fluctuations, equipment failures, forecasting biases, and emergency resources are characterized by high dimensionality, unstructured nature, and seasonal variations, necessitating the exploration of the coupling pathways by which multidimensional uncertainties affect electrical quantities within the power system. On the other hand, revealing the extent to which the coupling effects of massive uncertainties on security defenses urgently requires the design of adaptive factor selection methods and rapid impact assessment methods. Therefore, developing a rapid quantitative method that considers the impact of uncertainty coupling and depth on online security defenses has become a critical requirement. Summary of the Invention

[0004] Objective: In view of at least one of the above technical problems, this application provides a method and system for quantifying the combined influence of key factors in a power system that takes into account uncertainty coupling and depth.

[0005] The technical solution adopted in this application is:

[0006] Firstly, this application provides a method for quantifying the combined impact of key factors in a power system, taking into account uncertainty coupling and depth, including:

[0007] S1: Obtain multi-dimensional uncertainty factor data related to power system security and stability defense from historical power outage / power restriction events, and establish an uncertainty factor database;

[0008] S2: Adaptively filter potential combinations of uncertainty factors in the target area from the uncertainty factor database based on historical uncertainty factor data and online forecast information of the target area;

[0009] S3: Based on the combination of potential uncertainty factors, the source-load power sequence corresponding to each combination of potential uncertainty factors is generated by using the quantitative mapping relationship between uncertainty factors and source-load power sequence; the quantitative mapping relationship between uncertainty factors and source-load power sequence is established by a model-data hybrid driving method.

[0010] S4: Based on the source-load power sequence, calculate the loss index corresponding to each combination of potential uncertainty factors, and classify the loss index according to the preset loss classification threshold to obtain the loss classification result; where the loss index is selected from the expected power shortage in the system reliability assessment.

[0011] S5: The probability of each combination of potential uncertainty factors is classified according to the preset probability classification threshold to obtain the probability classification result;

[0012] S6: Based on the loss classification results and probability classification results, obtain the risk classification results and ranking of each combination of potential uncertainty factors;

[0013] S7: Based on the risk classification results and ranking, obtain the set of key uncertain factors for different seasons and time periods. The set of key uncertain factors includes the combination and depth of uncertain factors.

[0014] Secondly, this application provides a device for quantifying the combined impact of key factors in a power system that takes into account uncertainty coupling and depth, including a processor and a storage medium;

[0015] The storage medium is used to store instructions;

[0016] The processor is configured to operate according to the instructions to execute the method according to the first aspect.

[0017] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0018] Fourthly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0019] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0020] Beneficial Effects: The method and system for quantifying the impact of key power system factor sets considering uncertainty coupling and depth provided in this application have the following advantages: This application innovatively constructs a full-chain path of "factor library construction - adaptive screening - quantitative mapping - loss assessment", proposes an adaptive uncertainty factor combination screening mechanism combined with online forecast information, and establishes a quantitative mapping relationship from multidimensional uncertainty to source-load sequence based on a model-data hybrid driving method; furthermore, by constructing a loss index, it realizes rapid assessment and risk classification of the impact degree of multidimensional uncertainty sets, thereby accurately extracting key uncertainty factor sets for different security defense control needs, effectively solving the problem of rapid extraction, quantitative assessment and priority ranking of multidimensional uncertainty factors under dynamic changes in defense boundaries, and laying a methodological foundation for improving the decision-making efficiency and adaptability of online security defense of new power systems. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a method for quantifying the impact of a set of key power system factors considering uncertainty coupling and depth, according to an embodiment of this application.

[0022] Figure 2 This is a schematic diagram of the source-charge power sequence results under different cold wave levels generated based on model-data hybrid driving according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram showing the loss ranking results of a set of uncertainty factors based on the reliability index EENS according to one embodiment of this application. Detailed Implementation

[0024] The present application will be further described below with reference to the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and should not be used to limit the scope of protection of the present application.

[0025] In the description of this application, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0026] In the description of this application, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0027] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0028] Example 1: This example provides a method for quantifying the combined impact of key factors in a power system that takes into account uncertainty coupling and depth, such as... Figure 1 As shown, it includes:

[0029] S1: Obtain multi-dimensional uncertainty factor data related to power system security and stability defense from historical power outage / power restriction events, and establish an uncertainty factor database;

[0030] In this step, the dimensions of the uncertainty factors include information, physical, and social dimensions;

[0031] Among them, the uncertainties in the information dimension include new energy forecasting deviations and load forecasting deviations;

[0032] Uncertainties in the physical dimension include weather fluctuations, natural disasters, and equipment failures;

[0033] Uncertainty in the social dimension includes emergency resources.

[0034] S2: Adaptively filter potential combinations of uncertainty factors in the target area from the uncertainty factor database based on historical uncertainty factor data and online forecast information of the target area;

[0035] In some embodiments, step S2 specifically includes:

[0036] Based on historical uncertainty factor data for the target area, the probability of uncertainty factors is obtained through seasonal statistics. This probability is then corrected using online forecasting information (the seasonal statistics characterize the seasonality of uncertainty factors, and the online update frequency can be adjusted according to actual power grid needs, such as different seasons or sudden early warning events). When the probability of uncertainty factors meets a set threshold, a corresponding combination of uncertainty factors is constructed. This allows for the adaptive extraction of different combinations and depths of uncertainty factor combinations from the uncertainty factor database, serving as potential uncertainty factor combinations for the target area.

[0037] ;

[0038] ;

[0039] In the formula, This represents the state where the depth of the i-th uncertainty factor is k (e.g., different warning levels for natural disasters). For state The following are probabilities of uncertainty factors obtained from historical data statistics. For state The frequency of occurrence, where n represents the total number of uncertainties and m represents the number of depth levels for uncertainty factor i. For state The probability estimation information for online prediction information, For state The following is an updated probability based on a combination of historical data statistics and online forecast information. The attenuation coefficient represents the probability correction of uncertainties by combining historical data and online forecast information.

[0040] S3: Based on the combination of potential uncertainty factors, use the pre-constructed quantitative mapping relationship between uncertainty factors and source-load power sequences to generate source-load power sequences corresponding to each combination of potential uncertainty factors;

[0041] The quantitative mapping relationship between uncertainties and source-load power sequences is established using a model-data hybrid approach. Based on this model-data hybrid approach, the mapping relationship is quantified according to different combinations of uncertainties across dimensions, taking into account the depth of uncertainty (e.g., different warning levels for natural disasters). This data-model hybrid approach is then used to quantify the impact of multidimensional uncertainties. Specifically, a meteorological-power mapping model is used to convert meteorological, transitional weather, and social activity data into power sequences for new energy sources and loads. The power sequences are then corrected using a data-driven approach, combining the uncertainties and deviation patterns, thereby achieving source-load power mapping under multidimensional uncertainties.

[0042] For example, the meteorological-power mapping model is referenced in the document "Automation of Power Systems: A New Type of Power System Power Planning to Ensure Supply and Demand Security under Extreme Weather". The data-driven correction method can be found in the technical solution of Chinese patent application publication number CN202311088568.9, entitled "A Photovoltaic Power Generation Prediction Method Based on Mechanism-Data-Driven Hybrid Integration".

[0043] S4: Based on the source-load power sequence, calculate the loss index corresponding to each combination of potential uncertainty factors, and classify the loss index according to the preset loss classification threshold to obtain the loss classification result; wherein the loss index is selected from the expected power shortage EENS in the system reliability assessment.

[0044] In this embodiment, based on the source-load power sequence corresponding to the obtained combination of multidimensional uncertainty factors, the expected energy not supplied (EENS) in the system reliability assessment is selected as the loss index to quantify the impact of multidimensional uncertainty, while avoiding a large amount of simulation calculation.

[0045] In some embodiments, the loss metric corresponding to each combination of potential uncertainty factors is calculated, including:

[0046] The EENS is calculated using the convolution method, and is expressed as:

[0047] ;

[0048] in, The system reliability function is defined as follows: the expected power shortage EENS is selected in the reliability assessment, and d represents the load value. To remove the available capacity of conventional generating units from renewable energy sources, The probability density distribution of available capacity of conventional generating units is calculated using the convolution method, where the available capacity of conventional generating units is modeled using normal and fault state models.

[0049] Therefore, in reliability assessment, it is expected that the EENS index of power shortage can simultaneously reflect changes in power supply and load, thereby reflecting the system's power supply reliability level.

[0050] In this embodiment, the loss index is classified according to a preset loss classification threshold to obtain the loss classification result, which is represented as follows;

[0051] ;

[0052] in, Indicates a combination of uncertain factors Loss metrics (for different combinations and depths) All of these represent preset loss thresholds. This refers to the number of tiers / levels.

[0053] By calculating the loss index value corresponding to each combination of potential uncertainty factors, the factors are ranked according to the loss category.

[0054] S5: The probability of each combination of potential uncertainty factors is classified according to the preset probability classification threshold to obtain the probability classification result;

[0055] In this embodiment, the probability of each combination of potential uncertainty factors is categorized according to a preset probability categorization threshold to obtain the probability categorization result, which is expressed as follows:

[0056] ;

[0057] in, Indicates a combination of uncertain factors The probabilities (of different combinations and depths) should be noted. All of these represent preset probability grading thresholds. This refers to the number of tiers / levels.

[0058] It should be noted that when the correlation between factors in different dimensions is high, it is necessary to establish a joint probability distribution estimate (such as the correlation between natural disasters and the prediction deviation of new energy sources). When the correlation between factors in different dimensions is low, it can be handled by assuming independent distributions (such as the correlation between natural disasters and emergency resources).

[0059] S6: Based on the loss classification results and probability classification results, obtain the risk classification results and ranking of each combination of potential uncertainty factors;

[0060] Multiplying the loss classification result and the probability classification result yields the risk classification result based on the set of key uncertainty factors in risk classification.

[0061] In this embodiment, the risk classification results and ranking of each potential combination of uncertainty factors are obtained based on the loss classification results and probability classification results, including:

[0062] ;

[0063] In the formula, Represents a set of uncertain factors Risk classification results Indicates a combination of uncertain factors The probability, A set of uncertain factors The probability grading results Indicates a combination of uncertain factors The loss index value, Represents a set of uncertain factors The loss classification results.

[0064] S7: Based on the risk classification results and ranking, obtain the set of key uncertain factors for different seasons and time periods. The set of key uncertain factors includes the combination and depth of uncertain factors.

[0065] Furthermore, by extracting a set of high-risk uncertainty factors and incorporating them into the security and stability defense framework, these factors serve as key uncertainty factors for different control needs such as prevention, emergency response, correction, recovery, and emergency response, supporting the identification of defense boundaries and efficient dimensionality reduction of multidimensional uncertainties.

[0066] Verification Example: In this case, MATLAB software was used to develop the power system key factor set influence quantification method considering uncertainty coupling and depth, as proposed in this application. Details will not be elaborated further. The following mainly demonstrates its specific technical effects using case data. The model-data hybrid driving method and reliability assessment method in this approach are implemented using MATLAB programs.

[0067] Operating environment:

[0068] Intel Core Ultra 5 225H CPU 1.70GHz, 32GB RAM, Microsoft Windows 11 x64

[0069] MATLAB R2025a

[0070] Implementation results:

[0071] This application example is based on a case study of a provincial power grid in Northwest China. It takes into account a set of multidimensional uncertainties (different combinations and depths) in the region, including physical factors (natural disasters such as cold waves and sandstorms), information factors (prediction deviations of new energy sources), and social factors (proportion of emergency resources). It conducts rapid quantitative assessment of the degree of impact, obtains risk classification ranking results under different combinations and depths by combining probability classification, and analyzes the set of key uncertainties for online security defense and control needs based on the risk classification ranking, thereby providing a basis for dimensionality reduction of uncertainties in different time periods.

[0072] Figure 1 The flowchart of the method for quantifying the impact of key factors in power systems that take into account the coupling and depth of uncertainty includes steps such as building an uncertainty factor library, adaptive screening of uncertainty factors, source-load power sequence mapping, rapid loss assessment, and extraction of key uncertainty factor sets.

[0073] Figure 2The results of the source-load power sequence under different cold wave levels are generated based on the model-data hybrid driving. The generated source-load power sequence shows that cold wave weather will lead to a decrease in photovoltaic output, and even wind power failure and shutdown under high-level cold waves. Meanwhile, the load increases due to the sudden drop in temperature. Therefore, extreme weather or even natural disasters will have a two-way impact on source and load.

[0074] Figure 3 Based on the loss classification and ranking results of the combination of uncertain factors according to the reliability index EENS, the main combination of uncertain factors in the winter power grid of this region is: high warning of cold waves and sandstorms + high prediction bias + insufficient emergency resources, which poses the highest risk. It can be seen that the proposed method can quickly quantify and assess the impact of multidimensional uncertainties on security defense, avoiding detailed simulation analysis. Furthermore, by combining loss classification and probability classification to achieve risk classification and ranking, and by extracting the set of uncertain factors with higher risk and incorporating them into the security and stability defense framework, the problem of rapid extraction, quantification and priority ranking of multidimensional uncertain factors under dynamic changes in the defense boundary is solved.

[0075] Example 2: Based on Example 1, this example provides a device for quantifying the combined impact of key factors in a power system that takes into account uncertainty coupling and depth, including a processor and a storage medium;

[0076] The storage medium is used to store instructions;

[0077] The processor is configured to operate according to the instructions to execute the method according to Embodiment 1.

[0078] Example 3: Based on Example 1, this example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Example 1.

[0079] Example 4: Based on Example 1, this example provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in Example 1.

[0080] Example 5: Based on Example 1, this example provides a computer program product, including a computer program that, when executed by a processor, implements the method described in Example 1.

[0081] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0085] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for quantifying the combined impact of key factors in a power system, taking into account uncertainty coupling and depth, characterized in that, include: S1: Obtain multi-dimensional uncertainty factor data related to power system security and stability defense from historical power outage / power restriction events, and establish an uncertainty factor database; S2: Adaptively filter potential combinations of uncertainty factors in the target area from the uncertainty factor database based on historical uncertainty factor data and online forecast information of the target area; S3: Based on the combination of potential uncertainty factors, the source-load power sequence corresponding to each combination of potential uncertainty factors is generated by using the quantitative mapping relationship between uncertainty factors and source-load power sequence; the quantitative mapping relationship between uncertainty factors and source-load power sequence is established by a model-data hybrid driving method. S4: Based on the source-load power sequence, calculate the loss index corresponding to each combination of potential uncertainty factors, and classify the loss index according to the preset loss classification threshold to obtain the loss classification result; where the loss index is selected from the expected power shortage in the system reliability assessment. S5: The probability of each combination of potential uncertainty factors is classified according to the preset probability classification threshold to obtain the probability classification result; S6: Based on the loss classification results and probability classification results, obtain the risk classification results and ranking of each combination of potential uncertainty factors; S7: Based on the risk classification results and ranking, obtain the set of key uncertain factors for different seasons and time periods. The set of key uncertain factors includes the combination and depth of uncertain factors.

2. The method according to claim 1, characterized in that, The dimensions of the uncertainties include informational, physical, and social dimensions; Uncertainties in the information dimension include new energy forecasting deviations and load forecasting deviations; Uncertainties in the physical dimension include weather fluctuations, natural disasters, and equipment failures; Uncertainty in the social dimension includes emergency resources.

3. The method according to claim 1, characterized in that, Based on historical uncertainty factor data and online forecast information for the target area, potential combinations of uncertainty factors for the target area are adaptively screened from the uncertainty factor database, including: Based on historical uncertainty factor data for the target area, the probability of uncertainty factors is obtained through seasonal statistics. This probability is then corrected using online forecasting information. When the uncertainty factor probability meets a set threshold, a corresponding combination of uncertainty factors is constructed. This allows for the adaptive extraction of different combinations and depths of uncertainty factor combinations from the uncertainty factor database, serving as potential uncertainty factor combinations for the target area. ； ； In the formula, Let represent the state where the depth of the i-th uncertainty factor is k. For state The following are probabilities of uncertainty factors obtained from historical data statistics. For state The frequency of occurrence, where n represents the total number of uncertainties and m represents the number of depth levels for uncertainty factor i. For state The probability estimation information for online prediction information, For state The following is an updated probability based on a combination of historical data statistics and online forecast information. The attenuation coefficient represents the probability correction of uncertainties by combining historical data and online forecast information.

4. The method according to claim 1, characterized in that, Calculate the loss metrics corresponding to each combination of potential uncertainties, including: The EENS is calculated using the convolution method, and is expressed as: ； in, The system reliability function is defined as follows: the expected power shortage EENS is selected in the reliability assessment, and d represents the load value. To remove the available capacity of conventional generating units from renewable energy sources, The probability density distribution of available capacity of conventional generating units is calculated using the convolution method, where the available capacity of conventional generating units is modeled using normal and fault state models.

5. The method according to claim 1, characterized in that, The loss indicators are classified according to the preset loss classification threshold to obtain the loss classification result, which is represented as follows: ； in, Indicates a combination of uncertain factors The loss index value, All of these represent preset loss thresholds. This refers to the number of tiers / levels.

6. The method according to claim 1, characterized in that, The probability grading results are obtained by grading the probability of each combination of potential uncertainties according to a preset probability grading threshold, and are expressed as follows: ； in, Indicates a combination of uncertain factors The probability of this is important to note. All represent preset probability grading thresholds. This refers to the number of tiers / levels.

7. The method according to claim 1, characterized in that, Based on the loss classification and probability classification results, the risk classification results and rankings for each combination of potential uncertainty factors are obtained, including: ； In the formula, Represents a set of uncertain factors Risk classification results Indicates a combination of uncertain factors The probability, A set of uncertain factors The probability grading results Indicates a combination of uncertain factors The loss index value, Represents a set of uncertain factors The loss classification results.

8. A device for quantifying the combined influence of key factors in a power system, considering uncertainty coupling and depth, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

Images