A power system resilience assessment method and system considering uncertainty

Abstract

The present invention discloses a method and system for assessing the resilience of a power system that takes uncertainty into account. The method includes: obtaining various types of data on the power system after experiencing an extreme disaster; constructing a component failure rate model dynamically associated with the power system recovery decision based on decision-dependent uncertainty; generating a power outage scenario based on the acquired data and the component failure rate model, taking into account the uncertainty of the state of some components; establishing a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculating the damage confidence of the state-uncertain components, and screening out typical power outage scenarios using system information entropy; constructing a multidimensional power system dynamic resilience assessment index system based on renewable energy output uncertainty; and calculating assessment indicators to conduct resilience assessment. The present invention integrates decision-dependent uncertainty, component state uncertainty, and renewable energy output uncertainty, making the assessment results more closely reflect the complexity of actual post-disaster scenarios.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system resilience assessment, and in particular relates to a power system resilience assessment method and system taking uncertainty into account. Background Art

[0002] The statements herein merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Amidst the global energy transition, the penetration of renewable energy in power systems is gradually increasing, becoming the core of the next-generation energy system. However, with global climate change and the frequent occurrence of extreme weather events, power systems are facing increasing challenges. As critical infrastructure in modern society, the safe and stable operation of power systems is of vital importance to national economic and social development. Large-scale power outages not only disrupt daily life but also severely impact industrial production, transportation, communications systems, and even threaten national security.

[0004] Power system resilience, also known as toughness or elasticity, refers to its ability to withstand, adapt to, and rapidly recover from low-probability, high-loss extreme events. Assessing power system resilience comprehensively reflects the system's overall recovery capabilities, enabling optimization of preventative measures and recovery strategies in the face of extreme natural disasters, thereby minimizing losses.

[0005] Current research on power system resilience assessment falls into two main categories: resilience assessment based on load loss and resilience assessment that considers system performance over time. These approaches primarily reflect the system's resilience or overall performance, but fail to consider the impact of component state uncertainty on power system resilience in post-disaster power outage scenarios. Traditional methods assume that component states are fully known at the time of assessment. However, in real-world power outage scenarios, component states may be uncertain due to monitoring delays or communication interruptions, resulting in deviations between assessment results and actual resilience.

[0006] Furthermore, decision-dependent uncertainty is rarely used in power system resilience assessments, and its mechanisms related to power system resilience after extreme disasters remain largely unstudied. Current system resilience assessment methods primarily focus on fixed indicators and static scenarios, with insufficient modeling of dynamic recovery processes. The dynamic impact of the uncertainty inherent in the widespread integration of renewable energy into the grid on resilience should also be considered. Summary of the Invention

[0007] The purpose of the present invention is to overcome the deficiencies in the above-mentioned prior art and to provide a method and system for assessing the resilience of a power system that takes uncertainty into account. Taking into account the uncertainty of restoration decision dependencies, the uncertainty of component states, and the uncertainty of renewable energy output, a power system resilience assessment index system is constructed to dynamically assess the resilience of the system. This can provide a more comprehensive understanding of the overall resilience of the system, thereby helping the power system to take corresponding measures to improve its resilience and reduce losses caused by extreme disasters.

[0008] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0009] In a first aspect, the technical solution of the present invention provides a method for assessing the resilience of a power system taking into account uncertainty, comprising:

[0010] Obtain various data on the power system after experiencing extreme disasters;

[0011] Build a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty;

[0012] Based on the acquired data and the component failure rate model, a power outage scenario is generated considering the uncertainty of some component states;

[0013] Based on the grey correlation theory, a correlation matrix between state uncertainty and faulty components is established, the damage confidence of state uncertainty components is calculated, and typical power outage scenarios are screened using system information entropy.

[0014] Taking into account the uncertainty of renewable energy output, a multi-dimensional power system dynamic resilience assessment indicator system is constructed;

[0015] Calculate evaluation indicators for resilience assessment.

[0016] In at least one embodiment, the various types of data include historical disaster data, power grid failure data, and renewable energy output time series data.

[0017] In at least one embodiment, generating a power outage scenario is specifically as follows: based on the various types of power system data obtained and the component failure rate model, randomly setting the status of some components to be uncertain, and using the Monte Carlo algorithm to simulate the random generation of the power system fault set under extreme events on the basis of considering the uncertainty of the status of some components, thereby obtaining the power outage scenario of the power system under extreme events.

[0018] In at least one embodiment, the damage confidence of the uncertain state component is calculated specifically as follows: using the grey correlation analysis theory, the grey correlation between the uncertain state component and the faulty component is calculated, and the correlation matrix between the components is further obtained; the correlation matrix is ​​decomposed into eight categories according to the physical distance of the components, the component type and the functional level, and an uncertainty reasoning Petri network is established based on the decomposed correlation matrix, and the damage confidence of each uncertain state component is quickly obtained through matrix calculation.

[0019] In at least one embodiment, the method of using system information entropy to screen out typical power outage scenarios is specifically: using the system information entropy method to quantify the uncertainty of the power system, calculating the corresponding entropy value of each power outage scenario, screening out typical and reasonable power outage scenarios according to the entropy value constraint range, and determining the scale of the fault.

[0020] In at least one embodiment, the power system dynamic resilience assessment index system is constructed from three dimensions: robustness, rapidity, and coupling. Each dimension has different secondary indicators to quantify the power system resilience.

[0021] In at least one embodiment, the secondary indicators of robustness include output uncertainty, average load loss rate, key load recovery rate, and component uncertainty impact factor.

[0022] In at least one embodiment, the secondary indicators of rapidity include dynamic recovery time, load loss speed, and critical load recovery speed.

[0023] In at least one embodiment, the secondary indicator of coupling is an uncertainty-fault coupling coefficient.

[0024] In a second aspect, the technical solution of the present invention further provides a power system resilience assessment system taking uncertainty into account, comprising:

[0025] The data acquisition module is configured to: acquire various data of the power system after experiencing an extreme disaster;

[0026] A model building module is configured to: build a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty;

[0027] The power outage scenario generation module is configured to: generate a power outage scenario based on the acquired data and the component failure rate model, taking into account the uncertainty of some component states;

[0028] The power outage scenario screening module is configured to: establish a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculate the damage confidence of state uncertainty components, and use system information entropy to screen out typical power outage scenarios;

[0029] The indicator system construction module is configured to: combine the uncertainty of renewable energy output to build a multi-dimensional power system dynamic resilience assessment indicator system;

[0030] The resilience assessment module is configured to calculate an assessment indicator to perform resilience assessment.

[0031] The beneficial effects of the technical solution of the present invention are as follows:

[0032] 1) The uncertainty-based power system resilience assessment method proposed in this paper integrates three types of uncertainty factors: decision-dependency uncertainty, component state uncertainty, and renewable energy output uncertainty. This realistic perspective allows the assessment results to move beyond theoretical considerations. Instead, it comprehensively considers the complex conditions and uncertainties in many practical scenarios and incorporates them into the assessment system. This method can truly reflect the system's resilience in complex and changing environments, providing a more practical and guiding basis for power system planning, operation, and maintenance.

[0033] 2) The present invention's uncertainty-based power system resilience assessment method innovatively proposes a "decision-dependent uncertainty" modeling approach, dynamically linking system recovery decisions with component failure rates. This approach can more accurately reflect the power system's true resilience in the face of faults, thereby significantly improving the accuracy and reliability of resilience assessments.

[0034] 3) The uncertainty-based power system resilience assessment method of the present invention takes into account the actual possibility that the status of some components may be uncertain due to information interruption and other reasons after a disaster, and incorporates this status uncertainty into the assessment framework. A method based on grey correlation theory and Petri network is proposed to calculate the damage confidence of components with uncertain status, making the assessment results closer to the actual risks and greatly enhancing the practicality and credibility of the assessment results.

[0035] 4) This paper presents an uncertainty-based power system resilience assessment method. Taking into account the uncertainty of renewable energy output, it constructs a comprehensive dynamic assessment index system based on robustness, rapidity, and coupling. It also proposes an "uncertainty-fault coupling coefficient" metric to quantify the synergistic effect between failures caused by extreme events and the uncertainty of renewable energy output. This constructed index system overcomes the limitations of traditional fixed static indicators and enables a more comprehensive and flexible assessment of power system resilience under different operating conditions, providing a more scientific and accurate basis for decision-making in power system planning and operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0037] Figure 1 1 is an overall schematic diagram of a power system resilience assessment method taking uncertainty into account according to the present invention;

[0038] Figure 2 1 is a flow chart of a method for assessing power system resilience taking uncertainty into account according to the present invention;

[0039] Figure 3 Schematic diagram of decision-making dependency uncertainty in a power system resilience assessment method taking uncertainty into account according to the present invention;

[0040] Figure 4 This is a Petri network diagram for calculating component damage confidence in a power system resilience assessment method taking uncertainty into account according to the present invention. DETAILED DESCRIPTION

[0041] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0042] As introduced in the background technology, the purpose of the present invention is to overcome the shortcomings of the above-mentioned existing technologies and provide a power system resilience assessment method and system that takes into account uncertainty. On the basis of considering the uncertainty of restoration decision dependence, the uncertainty of component status and the uncertainty of renewable energy output, a power system resilience assessment index system is constructed to dynamically evaluate the system's resilience, which can provide a more comprehensive understanding of the overall resilience of the system, and then help the power system take corresponding measures to improve resilience and reduce losses caused by extreme disasters.

[0043] Example 1

[0044] In a typical embodiment of the present invention, Figure 1 As shown, this embodiment discloses a method for assessing power system resilience taking into account uncertainty, including:

[0045] S100. Obtain various data on the power system after experiencing extreme disasters;

[0046] S200. Construct a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty;

[0047] S300. Based on the acquired data and component failure rate model, a power outage scenario is generated taking into account the uncertainty of some component states;

[0048] S400. Establish a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculate the damage confidence of the state uncertainty components, and use system information entropy to screen out typical power outage scenarios;

[0049] S500. Considering the uncertainty of renewable energy output, a multi-dimensional power system dynamic resilience assessment indicator system is constructed.

[0050] S600: Calculate evaluation indicators to perform resilience evaluation.

[0051] Figure 2 The implementation process of this evaluation method is presented. The following describes in detail the above-mentioned power system resilience evaluation method taking uncertainty into account in conjunction with specific embodiments.

[0052] S100. Obtain various data on the power system after experiencing extreme disasters.

[0053] In this embodiment, the various data acquired after the power system experiences an extreme disaster include historical disaster data, grid fault data, and renewable energy output time series data. Historical disaster data includes records of extreme events, such as historical wind speed, intensity, and frequency; historical grid fault data includes fault line location, outage scope, communication interruption frequency, fault diagnosis response time, and the duration of component uncertainty.

[0054] S200. Construct a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty.

[0055] Decision-dependent uncertainty reveals that different restoration decisions of the power system will significantly affect the system resilience assessment results. Therefore, this embodiment uses decision-dependent uncertainty for modeling and establishes a component failure rate model that is dynamically associated with the power system restoration decision.

[0056] Component failure rate modeling and load current based on decision-dependent uncertainty I About, while the load current I It is obtained through power flow calculation, and the power flow calculation result is determined by the system recovery decision. It can be seen that the constructed component failure rate model can link the system recovery decision with the component failure rate, such as Figure 3 Component failure rate The formula is defined as:

[0057] (1);

[0058] in,

[0059] (2);

[0060] (3);

[0061] (4);

[0062] Where, and Respectively represent the rated current value and trip current value, is the initial value of the component failure rate, and They represent the power outage rate and power restoration rate of the component respectively, which can be obtained from historical data.

[0063] S300. Based on the acquired data and the component failure rate model, a power outage scenario is generated taking into account the uncertainty of some component states.

[0064] In this embodiment, the power outage scenario is generated in the following manner: based on the various data of the power system and the component failure rate model obtained, the status of some components is randomly set to "uncertain", and on the basis of considering the uncertainty of the status of some components, the Monte Carlo algorithm is used to simulate the random generation of the fault set of the power system under extreme events, and then the power outage scenario of the power system under extreme events is obtained.

[0065] Specifically, when using the Monte Carlo algorithm to generate power outage scenarios, based on the acquired post-disaster data and model data, a power outage scenario of the power system under extreme events is randomly generated by simulation. A random number between [0, 1] is generated for each component. , and compared with the component failure rate to obtain the status of each component. The specific formula is as follows:

[0066] (5);

[0067] Where, For components i The operating status of For components i probability of failure.

[0068] However, in actual post-disaster scenarios, due to incomplete information caused by communication failures and other reasons, the status of some components cannot be accurately perceived, presenting uncertainty. Therefore, during the simulation process, the status of some components is randomly set to "uncertain".

[0069] S400. Based on the grey correlation theory, a correlation matrix is ​​established between state uncertainty and faulty components, the damage confidence of state uncertainty components is calculated, and typical power outage scenarios are screened out using system information entropy.

[0070] To calculate the damage confidence of the uncertain state component, we first need to obtain the component state correlation. This embodiment uses the grey correlation analysis theory to calculate the grey correlation between the uncertain state component and the known state component. i The attribute sequence is , then the calculation formulas of the correlation coefficient and grey correlation degree between the two elements are as follows, where the correlation degree is the average value of the correlation coefficient.

[0071] (6);

[0072] (7);

[0073] Where, Display components i and components j The correlation coefficient of Display components i and components j Grey relational degree of is the resolution coefficient, usually taken as 0.5.

[0074] After obtaining the grey correlation degree between the state uncertain components and the faulty components, the correlation matrix is ​​constructed R , the formula is as follows:

[0075] (8);

[0076] In the formula, the components i and components j The grey relational degree is abbreviated as .

[0077] The incidence matrix R According to the physical distance of components, component types and functional levels, it can be decomposed into 、 、 、 、 、 、 as well as Eight categories, among which The correlation matrix represents close physical distance, same component type and same functional level; The correlation matrix represents the physical distance, the same component type and the same functional level; The correlation matrix represents close physical distance, different component types and the same functional level; The correlation matrix represents close physical distance, same component type and different functional levels; Correlation matrix representing long physical distances, different component types, and the same functional level; The correlation matrix represents components of the same type and different functional levels at large physical distances; Correlation matrices representing close physical proximity, different component types, and different functional levels; The correlation matrix representing the long physical distance, different component types and different functional levels is then used to establish uncertainty reasoning based on the decomposed correlation matrix. Petri Network, such as Figure 4 As shown, the library P1-P 10 It represents the component state association matrix, and transition T1-T3 is the calculation process of damage confidence. The damage confidence of each component with uncertain state is quickly obtained through matrix calculation.

[0078] The method of system information entropy is used to screen out typical and reasonable power outage scenarios. Its core lies in realizing scenario screening by quantifying the uncertainty of the power system. The method of system information entropy is used to quantify the uncertainty of the power system, calculate the corresponding entropy value of each power outage scenario, and screen out typical and reasonable power outage scenarios according to the entropy value constraint range to determine the scale of the fault. This method screens and optimizes system status scenarios based on the probability distribution of a single event. Entropy, as an important indicator for measuring system uncertainty, has important application value in the dynamic and uncertain system of the power system. The system may fail at any time, and its corresponding entropy value is W It can be described by the following formula:

[0079] (9);

[0080] in, A collection of power system components; T The time from the beginning to the end of the extreme disaster experienced by the system; For components i exist t Failure rate at each moment; Display components i exist t Whether a fault occurs at the moment, if a fault occurs, it is 1, otherwise it is 0; each power outage scenario corresponds to a , which can describe whether a system component fails and when the failure occurs t , and then the entropy value of the system when it is in the power outage scenario can be calculated.

[0081] The entropy value in the power system blackout scenario reflects the uncertainty of whether the system fails. From the actual blackout scenario, the entropy value W The value of cannot be too large or too small, so the entropy value of each reasonable scenario should be within a certain range, that is:

[0082] (10);

[0083] in, and They represent the maximum and minimum values ​​of the power system information entropy respectively. According to the constraints of formula (10), a reasonable set of power system blackout scenarios can be screened out.

[0084] S500. Considering the uncertainty of renewable energy output, a multi-dimensional power system dynamic resilience assessment index system is constructed.

[0085] Taking into account the uncertainty of renewable energy output, the dynamic resilience evaluation index system of the power system is mainly constructed from three dimensions: robustness, rapidity and coupling, and each dimension has different secondary indicators to quantify the resilience of the power system.

[0086] The robustness of a power system can be understood as the ability of the system to maintain its basic functions and performance stability and ensure reliable power supply when subjected to disturbances or facing uncertain factors. Therefore, the secondary indicators of the robustness dimension are output uncertainty, average load loss rate, key load recovery rate, and component uncertainty influencing factors, as follows:

[0087] (1) Output uncertainty

[0088] Output uncertainty The uncertainty range of renewable energy output that the system can withstand is quantified, taking into account the uncertainty of renewable energy output during system recovery and reflecting the regulation capacity of the system. The formula is defined as:

[0089] (11);

[0090] Where, Represents the set of nodes connected to renewable energy sources; Representation node j The standard deviation of renewable energy output; Representation node j The average load power.

[0091] (2) Average load loss rate

[0092] Load loss refers to the phenomenon that after the system suffers an extreme disaster, it is difficult to effectively resist the impact of the disaster, resulting in partial load power outage. To a certain extent, it can quantify the impact of disasters on the power system and also reflect its ability to resist damage under extreme conditions. Specifically, the smaller the value, the stronger the system's ability to maintain normal power supply during a disaster, which also lays the foundation for improving the subsequent system's resilience. The formula is defined as:

[0093] (12);

[0094] Where, n Indicates the number of system nodes; Representation node j The load loss, Representation node j The load amount.

[0095] (3) Critical load recovery rate

[0096] Critical load recovery rate It refers to the ratio of critical loads that have been effectively restored to power during the system emergency recovery phase. Loads that cannot be restored in a short period of time will gradually recover power in the subsequent recovery phase. The critical load recovery rate reflects the stability of the system to a certain extent. The formula is defined as:

[0097] (13);

[0098] Where, Indicates the number of nodes containing critical loads; Representation node j The critical load of the loss, Representation node j The critical load in .

[0099] (4) Component uncertainty impact factor

[0100] Component uncertainty impact factor The role of system resilience assessment is to measure the stability of the system in the face of uncertainty. A high value indicates that the node is subject to greater uncertainty and may face more instability risks during system recovery. The formula is defined as:

[0101] (14);

[0102] Where, Representation node j Uncertain influence factors of components; Representation and Node j A collection of associated components; Display components i Confidence level of damage; Display components i probability of failure; Representation node j The load amount.

[0103] The rapidity of power system recovery directly reflects the strength of the system's resilience. In this embodiment, the secondary indicators of rapidity are dynamic recovery time, load loss speed, and critical load recovery speed.

[0104] (1) Dynamic recovery time

[0105] Dynamic recovery time It represents the cumulative time that the actual output power of renewable energy does not reach the rated capacity, reflecting the uncertainty and dynamic performance of renewable energy during the recovery process, and thus more comprehensively demonstrating the recovery capacity of the entire system. The formula is defined as:

[0106] (15);

[0107] Where, T Indicates the time from the end of the disaster to the system's full restoration of power supply capacity; express t Time Node j The actual output of renewable energy in China; Representation node j Rated capacity of renewable energy sources.

[0108] (2) Load loss speed

[0109] Load loss speed It refers to the average speed at which some loads lose power to the power system from before an extreme disaster to after the disaster. The faster the system loses load, the faster the system can adjust the load after the disaster, creating conditions for restoring power. The formula is defined as:

[0110] (16);

[0111] Where, It indicates the duration of an extreme disaster from the beginning to the end. Representation node j The load loss.

[0112] (3) Critical load recovery speed

[0113] Critical load recovery speed It refers to the critical load quantity that can effectively restore power supply per unit time during the system emergency restoration phase. Restoring critical loads can greatly improve the system restoration efficiency, and its restoration speed intuitively reflects the rapidity of system restoration. The formula is defined as:

[0114] (17);

[0115] Where, It indicates the time from the end of the disaster to the completion of the emergency recovery experience of the system; Indicates the number of nodes containing critical loads; express t Time Node j The critical load to be recovered.

[0116] Secondary index selection of power system coupling uncertainty - fault coupling coefficient This indicator is used to quantify the synergistic effect between the failures caused by extreme events in the power system and the uncertainty of renewable energy output. Its core meaning is to determine whether there is a statistical correlation between the occurrence of failures and the change of uncertainty. When the value is close to 1, it indicates a high positive correlation between the two. In this case, the system has a higher risk after a disaster and a weaker recovery capability. This indicator can reflect the interactive effects of multi-dimensional uncertainty through a single coefficient. The formula is defined as:

[0117] (18);

[0118] (19);

[0119] (20);

[0120] Where, Indicates the load loss rate; Indicates the maximum uncertainty range of renewable energy output; represents covariance; and express and The standard deviation of .

[0121] S600: Calculate evaluation indicators to perform resilience evaluation.

[0122] Once the resilience assessment indicator system is established, the system's resilience assessment can be conducted. First, the data required for each indicator is collected. This can be obtained from historical power system operation records, real-time monitoring data, and other sources. Once this data is acquired, the indicator calculation phase begins. Based on pre-defined calculation methods and formulas, each indicator is precisely calculated, resulting in specific numerical values. This data provides a solid foundation for subsequent analysis and decision-making. Finally, the scores for each evaluation indicator are thoroughly interpreted and analyzed, analyzing the system's resilience level in various aspects. This provides a clear picture of the power system's resilience status, enabling targeted planning and operational optimization of different aspects of the system, and enhancing the scientific and practical nature of the power system's resilience assessment.

[0123] Example 2

[0124] In a typical implementation of the present invention, this embodiment discloses a power system resilience assessment system taking uncertainty into account, including:

[0125] The data acquisition module is configured to: acquire various data of the power system after experiencing an extreme disaster;

[0126] A model building module is configured to: build a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty;

[0127] The power outage scenario generation module is configured to: generate a power outage scenario based on the acquired data and the component failure rate model, taking into account the uncertainty of some component states;

[0128] The power outage scenario screening module is configured to: establish a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculate the damage confidence of state uncertainty components, and use system information entropy to screen out typical power outage scenarios;

[0129] The indicator system construction module is configured to: combine the uncertainty of renewable energy output to build a multi-dimensional power system dynamic resilience assessment indicator system;

[0130] The resilience assessment module is configured to calculate assessment indicators and perform resilience assessment. The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for assessing power system resilience taking into account uncertainty, characterized in that: include: Obtain various data on the power system after experiencing extreme disasters; Build a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty; Based on the acquired data and the component failure rate model, a power outage scenario is generated considering the uncertainty of some component states; Based on the grey correlation theory, a correlation matrix between state uncertainty and faulty components is established, the damage confidence of state uncertainty components is calculated, and typical power outage scenarios are screened using system information entropy. Taking into account the uncertainty of renewable energy output, a multi-dimensional power system dynamic resilience assessment indicator system is constructed; Calculate evaluation indicators for resilience assessment; The damage confidence of the uncertain state component is calculated by using grey correlation analysis theory to calculate the grey correlation between the uncertain state component and the faulty component to obtain a correlation matrix between the components; decomposing the correlation matrix into eight categories based on the physical distance between the components, the component type, and the functional level; and establishing an uncertainty reasoning Petri network based on the decomposed correlation matrix to quickly obtain the damage confidence of each uncertain state component through matrix calculation. The power system dynamic resilience evaluation index system is constructed from three dimensions: robustness, rapidity, and coupling. Each dimension has different secondary indicators to quantify the power system resilience. The secondary indicator of coupling is the uncertainty-fault coupling coefficient, which is: ； ； ； Where, Indicates the load loss rate, n Indicates the number of system nodes; Representation node j The load loss, Representation node j The load capacity; Indicates the maximum uncertainty range of renewable energy output; represents covariance; and express and The standard deviation of .

2. The method for assessing power system resilience taking into account uncertainty according to claim 1, wherein: The various types of data include historical disaster data, power grid failure data, and renewable energy output time series data.

3. The method for assessing power system resilience taking uncertainty into account according to claim 1, wherein: The specific steps of generating a power outage scenario are as follows: based on the various data of the power system and the component failure rate model obtained, the states of some components are randomly set to be uncertain. On the basis of considering the uncertainty of the states of some components, the Monte Carlo algorithm is used to simulate the random generation of the fault set of the power system under extreme events, and then the power outage scenario of the power system under extreme events is obtained.

4. The method for assessing power system resilience taking uncertainty into account according to claim 1, wherein: The method of using system information entropy to screen out typical power outage scenarios specifically includes: using the system information entropy method to quantify the uncertainty of the power system, calculating the corresponding entropy value of each power outage scenario, screening out typical power outage scenarios according to the entropy value constraint range, and determining the fault scale.

5. The method for assessing power system resilience taking uncertainty into account according to claim 1, wherein: The secondary indicators of robustness include output uncertainty, average load loss rate, key load recovery rate and component uncertainty impact factor.

6. The method for assessing power system resilience taking uncertainty into account according to claim 1, wherein: The secondary indicators of rapidity include dynamic recovery time, load loss speed and key load recovery speed.

7. A power system resilience assessment system taking uncertainty into account, characterized in that: include: The data acquisition module is configured to: acquire various data of the power system after experiencing an extreme disaster; A model building module is configured to: build a component failure rate model dynamically associated with power system restoration decisions based on decision-dependent uncertainty; The power outage scenario generation module is configured to: generate a power outage scenario based on the acquired data and the component failure rate model, taking into account the uncertainty of some component states; The power outage scenario screening module is configured to: establish a correlation matrix between state uncertainty and faulty components based on grey correlation theory, calculate the damage confidence of state uncertainty components, and use system information entropy to screen out typical power outage scenarios; The indicator system construction module is configured to: combine the uncertainty of renewable energy output to build a multi-dimensional power system dynamic resilience assessment indicator system; The resilience assessment module is configured to: calculate assessment indicators to perform resilience assessment; The damage confidence of the uncertain state component is calculated by using grey correlation analysis theory to calculate the grey correlation between the uncertain state component and the faulty component to obtain a correlation matrix between the components; decomposing the correlation matrix into eight categories based on the physical distance between the components, the component type, and the functional level; and establishing an uncertainty reasoning Petri network based on the decomposed correlation matrix to quickly obtain the damage confidence of each uncertain state component through matrix calculation. The power system dynamic resilience evaluation index system is constructed from three dimensions: robustness, rapidity, and coupling. Each dimension has different secondary indicators to quantify the power system resilience. The secondary indicator of coupling is the uncertainty-fault coupling coefficient, which is: ； ； ； Where, Indicates the load loss rate, n Indicates the number of system nodes; Representation node j The load loss, Representation node j The load capacity; Indicates the maximum uncertainty range of renewable energy output; represents covariance; and express and The standard deviation of .

Images