Active power distribution network operation risk assessment method and system considering element uncertainty

Abstract

The invention discloses an active power distribution network operation risk assessment method and system considering element uncertainty, and the method comprises the steps: inputting basic data, and inputting the basic data into a pre-constructed power distribution network wind-solar output and load demand uncertainty model; constructing a multi-level active power distribution network risk assessment index system; the method comprises the following steps: taking wind power, photovoltaic and load output data as input, generating an operation condition scene by adopting LHS sampling of global sensitivity analysis, carrying out scene reduction by utilizing a K-medoids clustering algorithm to obtain an appropriate scene number and a probability of each scene, calculating a power flow in an s-th operation scene, and obtaining an output node voltage matrix and a line active and reactive power matrix; and after all scenes are traversed, the operation risk is calculated according to a pre-constructed risk index system. The method has the advantages that accurate evaluation and weak link positioning of the operation risk of the power distribution network are achieved, and the problem that a traditional risk evaluation method is insufficient in precision in the complex active power distribution network is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of power systems, and in particular to a method for assessing the operational risks of active distribution networks that takes into account component uncertainties. Background Technology

[0002] With the advancement of the "dual-carbon" strategy, distributed generation (DG) power sources such as wind and solar power, as well as electric vehicle (EV) charging loads, are being connected to the distribution network on a large scale. The strong randomness and intermittency of their output and behavior significantly increase the uncertainty of the distribution network's operating status. Traditional risk assessment methods often employ Monte Carlo simulation or standard Latin hypercube sampling (LHS). While these methods can handle uncertainty, they have significant drawbacks: standard LHS samples all random variables uniformly, failing to distinguish the differences in the impact of different variables on system risk. This results in a large waste of computational resources on variables with minimal impact on the final risk index, while insufficient sampling of key risk-driving variables leads to low risk assessment efficiency, large variance in results, and difficulty in accurately identifying system weaknesses. (Invention Content) The technical problem to be solved by this invention is how to achieve accurate assessment of the risks of active power distribution network operation.

[0003] This invention solves the above-mentioned technical problems through the following technical means: a method for assessing the operational risk of active power distribution networks considering component uncertainties, comprising the following steps: S1: Input basic data, including distribution network data, load data, wind power parameters, photovoltaic cell parameters, and wind-solar load probability model parameters, and input them into the pre-built distribution network wind and solar power output and load demand uncertainty model; S2: Construct a multi-level risk assessment index system for active power distribution networks; S3: Using wind power, photovoltaic, and load output data as input, global sensitivity analysis is used to generate operating condition scenarios through LHS sampling, and K-medoids clustering algorithm is used to reduce the number of scenarios and the probability of each scenario. The power flow under the s-th operating scenario is calculated to obtain the output node voltage matrix and line active and reactive power matrix. S4: After traversing all scenarios, calculate the risk indicators of the power distribution network.

[0004] Furthermore, in step S1: The process of constructing the uncertainty model for wind and solar power output and load demand in the power distribution network is as follows: Construct the probability distribution function of the active power output of distributed photovoltaic power generation; Construct the probability distribution function between the output power of the wind turbine and the wind speed: Construct the probability density function of load fluctuations.

[0005] Specifically, the probability distribution function for the active power output of the constructed distributed photovoltaic system is as follows: (1) in, The active power output of distributed photovoltaic power. This represents the maximum active power output of distributed photovoltaic power grid connection. and All are shape parameters of the Beta distribution, μ is the mean illumination, and σ 2 For variance; The probability distribution function of the output power of the constructed wind turbine and the wind speed is as follows: (4) in, This refers to the active power output of the wind turbine. This refers to the rated active power of the wind turbine generator. To cut into wind speed, Rated wind speed, To cut off the wind speed, k For shape parameters, k 1 and k 2 These are transformation parameters used to convert wind speed distribution into power distribution. c For scale parameters; The constructed probability density function of load fluctuation is: (8) (9) in, The active power of the load. This represents the mathematical expectation of the active power of the load. The standard deviation of the active power of the load. The reactive power of the load. This represents the mathematical expectation of the reactive power of the load. This represents the standard deviation of the reactive power of the load.

[0006] Assuming the wind turbine uses a constant power factor, the relationship between its reactive power output and active power output can be expressed as follows: (7) In the formula, The reactive power output of wind turbine generators. The power factor.

[0007] Further, step S2 includes the following steps: From both spatial and temporal dimensions, considering factors such as node voltage, line power flow, and distribution network load shedding, a multi-dimensional risk indicator system is established for the component layer and the distribution network layer. The component layer risk indicators include node overvoltage operation risk, node undervoltage operation risk, and line overload risk. The distribution network layer risk indicators include distribution network overvoltage operation risk, distribution network undervoltage operation risk, distribution network line overload operation risk, and comprehensive distribution network load shedding operation risk. The formula for calculating the risk of node overvoltage operation is as follows: (10) in, For the overvoltage operation risk of the i-th node, The 0-1 flag represents the overvoltage risk, indicating whether an event of voltage exceeding the upper limit has occurred. Let be the voltage of node i in the s-th scenario. For scenario probabilities, This is a function of the severity of exceeding the voltage limit; The formula for calculating the risk of node undervoltage operation is as follows: (12) (13) In the formula, This represents the probability that the voltage of the i-th node exceeds the lower limit. The risk of low-voltage operation for node i; The 0-1 flag represents the risk of low voltage operation, indicating whether a voltage drop below the lower limit event has occurred. This is a severity function for when the voltage does not reach the lower limit.

[0008] The line overload risk index reflects the risk that the active power carried on the line will exceed the maximum limit under different system operating conditions. The probability and risk of line overload are as follows: (15) (16) In the formula, The risk of active power overload operation of line j; This is a line overload operation risk indicator, indicating whether a line overload event has occurred in the s-th scenario; Let be the active power flowing through line j in scenario s at time t; This is a severity function for line overload.

[0009] The overvoltage operation risk of the distribution network is obtained by weighting the overvoltage operation risk of each node according to its importance, as shown below: (18) In the formula, Risk of overvoltage operation in distribution networks; This represents the total number of nodes in the system. This represents the node importance coefficient. The overvoltage operating risk for the i-th node is as follows: (19) In the formula, express i The active power of the node, for i The active power of the node, Read This represents the number of branches connected to this node.

[0010] The risk of low voltage operation in the distribution network is calculated by weighting the low voltage operation risk of each node according to its importance, as shown below: (20) In the formula, To mitigate the risk of low voltage operation in the distribution network, This refers to the risk of low-voltage operation at node i.

[0011] The risk of overload operation of distribution network lines is the sum of the overload operation risks of each line in the distribution network, as shown below: (twenty one) In the formula, To mitigate the risk of overload operation of distribution network lines; N l This represents the total number of branches within the distribution network. This refers to the risk of active power overload operation of line j.

[0012] The overall risk of load shedding in the distribution network is the maximum value among the load losses caused by overload events, as detailed below: (twenty two) (twenty three) (twenty four) (25) In the formula, , and These are the risks of unloaded operation caused by voltage over-limit and line overload, respectively. To mitigate the risk of overall load shedding in the distribution network, , and To reduce the load ratio, The overvoltage operation risk is indicated by a 0-1 flag. The 0-1 value represents the risk of low voltage operation.

[0013] Furthermore, S3 specifically includes: calculating the sensitivity index of each wind and solar load variable to the risk indicator using the Sobol index method, and then normalizing it to obtain the weight ω. i The load, photovoltaic, and wind power are sampled in intervals n1, n2, and n3 respectively, and combined into n1+n2+n3 initial scenarios. The K-medoids clustering algorithm is used to reduce the initial scenarios from n1+n2+n3 to several typical scenarios and the probability of each scenario. The power flow under the s-th operating scenario is calculated to obtain the output node voltage matrix and the line active and reactive power matrix.

[0014] Furthermore, the Sobol index method is used to calculate the sensitivity index of each wind and solar load variable to risk indicators, which includes the following steps: The Sobol exponent method is used to calculate the input random variable X. i The first-order global sensitivity index S of the risk indicator Y i S i The calculation formula is: (27) In the formula, Represents the relationship between all random variables X. i Calculate the expected value of the risk indicator Y. The variance representing the conditional expectation. This represents the total variance of the output risk indicator Y; The first-order sensitivity index S i Normalization yields the sampling weights ω for each variable. i : (28) Set the total sampling size N total According to the weight ω i For each variable X i It is assigned its own unique number of sampling intervals N i : (29) Here, ceil is the floor function. Thus, variables of different importance are assigned different numbers of sampling intervals, and high-impact variables are given more refined interval divisions. For each random variable X i Within its probability distribution range, based on the number of intervals N assigned to it... i LHS sampling is performed, combining the sampled values ​​of wind power output, photovoltaic power output, and load into a multi-dimensional scenario matrix. Each row represents a sample sequence of one variable, and each column represents a scenario, as shown below: (30) The K-medoids clustering algorithm is used to reduce the number of scenarios and obtain the appropriate number of scenarios and the probability of each scenario. The power flow under the s-th operating scenario is calculated to obtain the output node voltage matrix and the line active and reactive power matrix.

[0015] This invention also provides a system corresponding to the above-mentioned active distribution network operation risk assessment method considering component uncertainties, comprising: The input module is used to input basic data and input it into the pre-built uncertainty model of wind and solar power output and load demand in the distribution network. The uncertainty model of wind and solar power output and load demand in the distribution network includes the probability distribution function of active power output of distributed photovoltaic, the probability distribution function between the output power of wind turbine and wind speed, and the probability density function of load fluctuation. The module is used to construct a multi-level active power distribution network risk assessment indicator system; The analysis module takes wind power, photovoltaic, and load output data as input, uses global sensitivity analysis and LHS sampling to generate operating condition scenarios, and uses K-medoids clustering algorithm to reduce the number of scenarios and the probability of each scenario. It calculates the power flow under the s-th operating scenario and obtains the output node voltage matrix and line active and reactive power matrix. The calculation module is used to calculate the operational risk based on the risk indicator system pre-built by the construction module after traversing all scenarios.

[0016] The advantages of this invention are: 1. This invention proposes a method for assessing the operational risks of active distribution networks that considers component uncertainties. It constructs a multi-level indicator system of "component-distribution network" and combines improved LHS sampling and K-medoids clustering to reduce scenarios, thereby achieving accurate assessment of distribution network operational risks and locating weak links. This effectively solves the problem of insufficient accuracy of traditional risk assessment methods in complex active distribution networks and fills the technological gap in this field.

[0017] 2. This invention identifies the key variables with the greatest impact on risk through global sensitivity analysis (Sobol index), and focuses on sampling them through hierarchical weighted LHS, generating more high-risk scenarios, distinguishing the importance of variables, improving the sampling accuracy of key risk variables, greatly enhancing the ability to capture tail risks, and making the risk assessment results more accurate and reliable.

[0018] 3. The evaluation method of this invention helps to deeply understand and quantify the impact of component uncertainty on the operational risks of distribution networks, provides technical support for the planning, operation and management of active distribution networks, promotes the coordinated development of active distribution networks with distributed power sources and electric vehicles, improves the overall efficiency of the power system, and promotes the effective utilization and sustainable development of smart distribution networks.

[0019] 4. This invention does not specify a weighting method, but focuses on the construction and sampling optimization of risk indicators. Compared with the existing method that uses entropy weighting and analytic hierarchy process to determine the weight of indicators, this invention emphasizes the combination of subjective and objective factors and is more practical for real-world applications. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown, this invention provides a method for assessing the operational risk of an active power distribution network considering component uncertainties, comprising the following steps: S1: Collect basic data such as distribution network data, load data, wind power parameters, photovoltaic cell parameters and wind-solar load probability model parameters, and input them into the pre-built distribution network wind and solar power output and load demand uncertainty model; The process of constructing the uncertainty model for wind and solar power output and load demand in the distribution network in this embodiment is as follows: Construct the probability distribution function of active power output of distributed photovoltaic power generation: (1) (2) (3) in, The active power output of distributed photovoltaic power. This represents the maximum active power output of distributed photovoltaic power grid connection. and All are shape parameters of a Beta distribution. μ σ is the mean illumination. 2 For variance, and The mean μ and variance σ of illumination at different time periods can be used to determine the illumination. 2 Seek; Based on the Weibull distribution describing the randomness of wind speed, a probability distribution function is constructed for the relationship between the output power of a wind turbine and wind speed: (4) (5) (6) in, This refers to the active power output of the wind turbine. This refers to the rated active power of the wind turbine generator. To cut into wind speed, Rated wind speed, To cut off the wind speed, k For shape parameters, k 1 and k 2 These are transformation parameters used to convert wind speed distribution into power distribution. c This is a scale parameter, reflecting the magnitude of the average wind speed. For wind speed standard deviation, This is the average wind speed; Assuming the wind turbine uses a constant power factor, the relationship between its reactive power output and active power output can be expressed as: (7) In the formula, The reactive power output of wind turbine generators. This is the power factor, assumed to be a constant power factor, and is typically adjustable within the range of ±0.95. The randomness of load fluctuations can be approximated by a normal distribution, and the corresponding probability density function is shown below: (8) (9) in, The active power of the load. This represents the mathematical expectation of the active power of the load. The standard deviation of the active power of the load. The reactive power of the load. This represents the mathematical expectation of the reactive power of the load. This represents the standard deviation of the reactive power of the load.

[0023] S2: Construct a risk assessment index system for active power distribution networks. From both spatial and temporal dimensions, considering factors such as node voltage, line power flow, and distribution network load shedding, a multi-dimensional risk indicator system is established for the component layer and the distribution network layer. The component layer risk indicators include node overvoltage operation risk, node undervoltage operation risk, and line overload risk. The distribution network layer risk indicators include distribution network overvoltage operation risk, distribution network undervoltage operation risk, distribution network line overload operation risk, and comprehensive distribution network load shedding operation risk. The maximum permissible risk is scaled down to ensure that each indicator is a standardized result distributed in the interval [0, 1]. The higher the value of the operational risk indicator, the greater the risk. When the operational risk indicator is 1, corresponding control measures need to be taken.

[0024] The formula for calculating the risk of node overvoltage operation is as follows: (10) in, For the overvoltage operation risk of the i-th node, The 0-1 flag represents the overvoltage risk, indicating whether an event of voltage exceeding the upper limit has occurred. Let be the voltage of node i in the s-th scenario. For scenario probabilities, The severity function for voltage exceeding the upper limit is as follows: (11) In the formula, V max For the specified maximum operating voltage limit, V max =1.05 pu The formula for calculating the risk of node undervoltage operation is as follows: (12) (13) In the formula, This represents the probability that the voltage of the i-th node exceeds the lower limit. The risk of low-voltage operation for node i; The 0-1 value represents the low-voltage operation risk, indicating whether a voltage drop below the lower limit event has occurred. For scene probabilities; The severity function for voltage not reaching the lower limit is as follows: (14) In the formula, The specified minimum operating voltage limit, =0.95pu.

[0025] The line overload risk index reflects the risk that the active power carried on the line will exceed the maximum limit under different system operating conditions. The probability and risk of line overload are as follows: (15) (16) In the formula, The risk of active power overload operation of line j; This is a line overload operation risk indicator, indicating whether a line overload event has occurred in the s-th scenario; Let be the active power flowing through line j in scenario s; The severity function for line overload is as follows: (17) In the formula, This represents the maximum active power limit flowing through line j.

[0026] The overvoltage operation risk of the distribution network is obtained by weighting the overvoltage operation risk of each node according to its importance, as shown below: (18) In the formula, Risk of overvoltage operation in distribution networks; This represents the total number of nodes in the system. This represents the node importance coefficient. To assess the overvoltage risk of the i-th node, considering both the node's power characteristics and network characteristics, the following is a summary: (19) In the formula, express i The active power of the node, for i The expected value of the active power of a node. Read This represents the number of branches connected to this node.

[0027] The risk of low voltage operation in the distribution network is calculated by weighting the low voltage operation risk of each node according to its importance, as shown below: (20) In the formula, To mitigate the risk of low voltage operation in the distribution network, This refers to the risk of low-voltage operation at node i.

[0028] The risk of overload operation of distribution network lines is the sum of the overload operation risks of each line in the distribution network, as shown below: (twenty one) In the formula, To mitigate the risk of overload operation of distribution network lines; N l This represents the total number of branches within the distribution network. This refers to the risk of active power overload operation of line j.

[0029] The risk of distribution network loss of load is the maximum value among the load losses caused by overload events, as detailed below: (twenty two) (twenty three) (twenty four) (25) In the formula, , and These are the risks of unloaded operation caused by voltage over-limit and line overload, respectively. To mitigate the risk of overall load shedding in the distribution network, , and To reduce the load ratio, The overvoltage operation risk is indicated by a 0-1 flag. The 0-1 value represents the risk of low voltage operation.

[0030] The comprehensive operational risk of the distribution network is a weighted average of the following: distribution network overvoltage operation risk, distribution network undervoltage operation risk, distribution network line overload operation risk, and distribution network underload operation risk, as detailed below: (26) In the formula, The comprehensive operational risk of the distribution network during the assessment period; w1, w2, w3, and w4 are the weighting coefficients corresponding to the distribution network overvoltage operation risk, distribution network undervoltage operation risk, distribution network line overload operation risk, and distribution network underload operation risk, respectively. w1+w2+w3+w4=1. The coefficients can be selected according to the actual requirements of the operators or determined by the analytic hierarchy process.

[0031] S3: Using wind, solar, and load (wind power, solar power, and load) output data as input, LHS sampling with global sensitivity analysis is used to generate operating condition scenarios, including wind power, solar power, and load. K-medoids clustering algorithm is then used to reduce the number of scenarios, obtaining an appropriate number of scenarios and the probability of each scenario. Power flow is calculated under the s-th operating scenario, yielding the output node voltage matrix and line active and reactive power matrix. The LHS sampling method using global sensitivity analysis actively generates more high-risk scenarios containing extreme values, providing high-quality input for subsequent power flow and risk calculations.

[0032] Specifically, the Sobol index method is used to calculate the sensitivity index of each wind and solar load variable to the risk indicator, and then the weights ω are obtained by normalization. i LHS sampling was performed on load, photovoltaic, and wind power in 60, 30, and 10 intervals respectively, and combined into 100 initial scenarios. The K-medoids clustering algorithm was used to reduce the 100 initial scenarios to 10-20 typical scenarios and the probability of each scenario. The power flow under the s-th operating scenario was calculated to obtain the output node voltage matrix and the line active and reactive power matrix. The process of calculating the sensitivity index of each wind and solar load variable to risk indicators using the Sobol index method includes the following steps: The Sobol exponent method is used to calculate the input random variable X. i The first-order global sensitivity index S of the risk indicator Y i S i The calculation formula is: (27) In the formula, Represents the relationship between all random variables X. i Calculate the expected value of the risk indicator Y. The variance representing the conditional expectation. This represents the total variance of the output risk indicator Y; The first-order sensitivity index S i Normalization yields the sampling weights ω for each variable. i : (28) Set the total sampling size N total According to the weight ω i For each variable X i It is assigned its own unique number of sampling intervals N i : (29) Here, ceil is the floor function, which assigns different numbers of sampling intervals to variables of different importance, with high-impact variables receiving more refined interval divisions.

[0033] For each random variable X i Within its probability distribution range, based on the number of intervals N assigned to it... i LHS sampling is performed, combining the sampled values ​​of wind power output, photovoltaic power output, and load into a multi-dimensional scenario matrix. Each row represents a sample sequence of one variable, and each column represents a scenario, as shown below: (30) Using the K-medoids clustering algorithm for scene reduction, the appropriate number of scenes and the probability of each scene are obtained. . S4: After traversing all scenarios, calculate the operational risk based on the risk indicator system pre-built in step S2.

[0034] Power flow calculations were performed for all operating scenarios to obtain the node voltages under each scenario. Branch active power ; Iterate through all running scenarios. For each node, if >V max Then assume =1, otherwise 0; if < V min Then assume =1, otherwise 0; for each line, if > Then assume =1, otherwise 0; Calculate the risk indicators of components and systems for each scenario using formulas (10)-(26), and calculate the overall operational risk of the distribution network. That is, the ultimate risk indicator.

[0035] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the operational risk of active distribution networks considering component uncertainties, characterized in that: Includes the following steps: S1: Input basic data and input it into the pre-built uncertainty model of power output and load demand of the distribution network. The uncertainty model of power output and load demand of the distribution network includes the probability distribution function of the active power output of distributed photovoltaic, the probability distribution function between the output power of wind turbine and wind speed, and the probability density function of load fluctuation. S2: Construct a multi-level risk assessment index system for active power distribution networks; S3: Using wind power, photovoltaic, and load output data as input, global sensitivity analysis is used to generate operating condition scenarios through LHS sampling, and K-medoids clustering algorithm is used to reduce the number of scenarios and the probability of each scenario. The power flow under the s-th operating scenario is calculated to obtain the output node voltage matrix and line active and reactive power matrix. S4: After traversing all scenarios, calculate the operational risk based on the risk indicator system pre-built in step S2.

2. The active distribution network operation risk assessment method considering component uncertainties as described in claim 1, characterized in that: In step S1, the process of constructing the uncertainty model for wind and solar power output and load demand in the power distribution network is as follows: Construct the probability distribution function of active power output of distributed photovoltaic power generation: (1) in, The active power output of distributed photovoltaic power. This represents the maximum active power output of distributed photovoltaic power grid connection. and All are shape parameters of a Beta distribution; Construct the probability distribution function between the output power of the wind turbine and the wind speed: (4) in, This refers to the active power output of the wind turbine. This refers to the rated active power of the wind turbine generator. To cut into wind speed, Rated wind speed, To cut off the wind speed, k For shape parameters, k 1 and k 2 These are transformation parameters used to convert wind speed distribution into power distribution. c For scale parameters; Construct the probability density function of load fluctuations: (8) (9) in, The active power of the load. This represents the mathematical expectation of the active power of the load. The standard deviation of the active power of the load. The reactive power of the load. This represents the mathematical expectation of the reactive power of the load. This represents the standard deviation of the reactive power of the load.

3. The active distribution network operation risk assessment method considering component uncertainties as described in claim 2, characterized in that: in: (2) (3) μ σ is the mean illumination. 2 For variance; (5) (6) For wind speed standard deviation, This represents the average wind speed.

4. The active distribution network operation risk assessment method considering component uncertainties as described in claim 1, characterized in that: Step S2 involves constructing a multi-level active distribution network risk assessment index system, which includes: considering node voltage, line power flow, and distribution network load shedding from both spatial and temporal dimensions, establishing a multi-dimensional risk index system at the component level and the distribution network level. The component level risk indexes include node overvoltage operation risk, node undervoltage operation risk, and line overload risk. The distribution network level risk indexes include distribution network overvoltage operation risk, distribution network undervoltage operation risk, distribution network line overload operation risk, and comprehensive distribution network load shedding operation risk.

5. The active distribution network operation risk assessment method considering component uncertainties as described in claim 4, characterized in that: The formula for calculating the risk of node overvoltage operation is as follows: (10) in, For the overvoltage operation risk of the i-th node, The 0-1 flag represents the overvoltage risk, indicating whether an event of voltage exceeding the upper limit has occurred. Let be the voltage of node i in the s-th scenario. For scenario probabilities, This is a function of the severity of exceeding the voltage limit; The formula for calculating the risk of node undervoltage operation is as follows: (12) (13) In the formula, This represents the probability that the voltage of the i-th node exceeds the lower limit. The risk of low-voltage operation for node i; The 0-1 flag represents the risk of low voltage operation, indicating whether a voltage drop below the lower limit event has occurred. This is a severity function for the voltage not reaching the lower limit; The line overload risk index reflects the risk that the active power carried on the line will exceed the maximum limit under different system operating conditions. The probability and risk of line overload are as follows: (15) (16) In the formula, The risk of active power overload operation of line j; This is a line overload operation risk indicator, indicating whether a line overload event has occurred in the s-th scenario; Let be the active power flowing through line j in scenario s at time t; This is a severity function for line overload.

6. The active distribution network operation risk assessment method considering component uncertainties as described in claim 5, characterized in that: (11) In the formula, V max The specified maximum operating voltage limit; (14) In the formula, The specified minimum operating voltage limit; (17) In the formula, This represents the maximum active power limit flowing through line j.

7. The active distribution network operation risk assessment method considering component uncertainties as described in claim 4, characterized in that: The overvoltage operation risk of the distribution network is obtained by weighting the overvoltage operation risk of each node according to its importance, as shown below: (18) In the formula, Risk of overvoltage operation in distribution networks; This represents the total number of nodes in the system. This represents the node importance coefficient. The overvoltage operating risk for the i-th node is as follows: (19) In the formula, express i The active power of the node, for i The active power of the node, Li This represents the number of branches connected to this node. The risk of low voltage operation in the distribution network is calculated by weighting the low voltage operation risk of each node according to its importance, as shown below: (20) In the formula, To mitigate the risk of low voltage operation in the distribution network, The risk of low-voltage operation for node i; The risk of overload operation of distribution network lines is the sum of the overload operation risks of each line in the distribution network, as shown below: (21) In the formula, To mitigate the risk of overload operation of distribution network lines; N l This represents the total number of branches within the distribution network. The risk of active power overload operation of line j; The overall risk of load shedding in the distribution network is the maximum value among the load losses caused by overload events, as detailed below: (22) (23) (24) (25) In the formula, , and These are the risks of unloaded operation caused by voltage over-limit and line overload, respectively. To mitigate the risk of overall load shedding in the distribution network, , and To reduce the load ratio, The overvoltage operation risk is indicated by a 0-1 flag. The 0-1 value represents the risk of low voltage operation.

8. The method for assessing the operational risk of active distribution networks considering component uncertainties as described in claim 1, characterized in that: Step S3 specifically includes: calculating the sensitivity index of each wind and solar load variable to the risk indicator using the Sobol index method, and then normalizing it to obtain the weight ω. i LHS sampling is performed on the load, photovoltaic, and wind power in intervals n1, n2, and n3 respectively, and combined into n1+n2+n3 initial scenarios: The Sobol exponent method is used to calculate the input random variable X. i The first-order global sensitivity index S of the risk indicator Y i S i The calculation formula is: (27) In the formula, Represents the relationship between all random variables X. i Calculate the expected value of the risk indicator Y. The variance representing the conditional expectation. This represents the total variance of the output risk indicator Y; The first-order sensitivity index S i Normalization yields the sampling weights ω for each variable. i : (28) Set the total sampling size N total According to the weight ω i For each variable X i It is assigned its own unique number of sampling intervals N i : (29) Here, ceil is the floor function. Thus, variables of different importance are assigned different numbers of sampling intervals, and high-impact variables are given more refined interval divisions. For each random variable X i Within its probability distribution range, based on the number of intervals N assigned to it... i LHS sampling is performed, combining the sampled values ​​of wind power output, photovoltaic power output, and load into a multi-dimensional scenario matrix. Each row represents a sample sequence of one variable, and each column represents a scenario, as shown below: （30)； Using the K-medoids clustering algorithm for scene reduction, the appropriate number of scenes and the probability of each scene are obtained. Calculate the power flow under the s-th operating scenario to obtain the output node voltage matrix and the line active and reactive power matrix.

9. The method for assessing the operational risk of active distribution networks considering component uncertainties as described in claim 1, characterized in that: Step S4 specifically includes: Power flow calculations were performed for all operating scenarios to obtain the node voltages under each scenario. Branch active power ; Iterate through all running scenarios. For each node, if > V max Then assume =1, otherwise 0; if < V min Then assume =1, otherwise 0; for each line, if > Then assume =1, otherwise 0; Based on the risk indicator system pre-built in step S2, calculate the risk indicators of components and systems for each scenario, and calculate the comprehensive operational risk of the distribution network. That is, the ultimate risk indicator.

10. An active power distribution network operation risk assessment system considering component uncertainties, characterized in that: include: The input module is used to input basic data and input it into the pre-built uncertainty model of wind and solar power output and load demand in the distribution network. The uncertainty model of wind and solar power output and load demand in the distribution network includes the probability distribution function of active power output of distributed photovoltaic, the probability distribution function between the output power of wind turbine and wind speed, and the probability density function of load fluctuation. The module is used to construct a multi-level active power distribution network risk assessment indicator system; The analysis module takes wind power, photovoltaic, and load output data as input, uses global sensitivity analysis and LHS sampling to generate operating condition scenarios, and uses K-medoids clustering algorithm to reduce the number of scenarios and the probability of each scenario. It calculates the power flow under the s-th operating scenario and obtains the output node voltage matrix and line active and reactive power matrix. The calculation module is used to calculate the operational risk based on the risk indicator system pre-built by the construction module after traversing all scenarios.

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