User power failure probability calculation method, system and device in extreme weather, medium and product

Abstract

The invention discloses a user power failure probability calculation method, system and device in extreme weather, a medium and a product, and relates to the field of power distribution network risk prevention and control, and the method comprises the steps: determining the wind speed of each position point in a wind field through employing a wind field model and the geographic information data of a power distribution network; calculating an element fault probability according to the wind speed; according to user load prediction data and the component fault probability, generating a network load scene containing a line fault combination and load demand fluctuation in extreme weather; building an emergency scheduling model considering user security load, and solving user load loss in the network load scene under the extreme weather according to the emergency scheduling model; and based on the user load loss, counting the number of scenes in which the user is in a power failure state, and determining the user power failure probability, so that a more accurate user power failure probability calculation result can be provided, and power failure risk differences of different types of users can be reflected.

Description

Technical Field

[0001] This application relates to the field of power distribution network risk prevention and control, and in particular to a method, system, equipment, medium and product for calculating the probability of power outages for users under extreme weather conditions. Background Technology

[0002] With the intensification of global climate change, the frequency and intensity of extreme weather events such as typhoons are increasing year by year, posing a serious threat to the safe and stable operation of power distribution networks. Strong typhoons are prone to triggering a series of faults in power distribution networks, such as line tripping, tower collapse, and equipment damage, resulting in widespread power outages and causing huge socio-economic losses. Therefore, calculating the probability of power outages for users of power distribution networks under typhoon disasters is of great importance.

[0003] Meanwhile, driven by the "dual-carbon" policy and the "county-wide photovoltaic" policy, the penetration rate of distributed photovoltaic (PV) power in distribution networks has continued to increase, becoming an important power source component of the distribution network. The integration of PV modules has changed the traditional power supply structure of distribution networks. However, during typhoons, PV modules and their supports are susceptible to damage from strong winds, resulting in loose connections and other faults, directly affecting the power supply capacity of the distribution network and thus altering the probability of power outages for users. Furthermore, the distribution network contains security loads such as hospitals, data centers, and emergency command centers. These loads have uninterrupted characteristics. When typhoon disasters cause insufficient power supply to the distribution network, load shedding is necessary to ensure the continuous power supply to these security loads. The shedding strategy directly affects the scope and probability distribution of power outages for users. Therefore, how to accurately characterize the impact of extreme weather on the distribution network and the power grid's supply needs, while simultaneously considering both to obtain more accurate power outage probability calculations, is a pressing issue that needs to be addressed.

[0004] Currently, traditional analyses of the impact of extreme weather on power distribution networks primarily focus on power components such as lines and towers, neglecting the impact of typhoon-induced photovoltaic module failures on power supply capacity under conditions of high photovoltaic grid integration. This leads to significant discrepancies between calculated power outage probabilities and actual results. Furthermore, most load shedding models aim to minimize total losses or optimize shedding costs, failing to establish differentiated shedding strategies for the uninterrupted nature of security loads and thus failing to accurately reflect the varying power outage risks among different types of users. Therefore, a more accurate method for calculating power outage probabilities under typhoon conditions is needed, integrating the impact of photovoltaic module failures with the differentiated needs of security loads to provide reliable support for power distribution network risk prevention and control. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, device, medium, and product for calculating the probability of power outages for users under extreme weather conditions, in order to solve the problem that the calculated probability of power outages for users deviates significantly from the actual situation and cannot accurately reflect the differences in power outage risks for different types of users.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for calculating the probability of power outages for users under extreme weather conditions, including: The wind speed at each location in the wind field is determined using a wind field model and geographic information data of the power distribution network; the geographic information data includes node coordinates, node connection relationships, line spans, and strength parameters of towers and lines. The probability of component failure is calculated based on the wind speed. Based on user load forecast data and the component failure probability, a grid load scenario under extreme weather conditions is generated, which includes line failure combinations and load demand fluctuations; the extreme weather conditions include typhoon weather. Construct an emergency dispatch model that considers user security load, and solve the user load loss in the network load scenario under extreme weather conditions based on the emergency dispatch model; Based on the user load loss, the number of scenarios in which users are in a power outage state is counted to determine the user power outage probability.

[0007] Secondly, this application provides a system for calculating the probability of power outages for users under extreme weather conditions, including: The wind speed determination module is used to determine the wind speed at various locations in the wind field using the wind field model and the geographic information data of the power distribution network; the geographic information data includes node coordinates, node connection relationships, line spans, and strength parameters of towers and lines. The failure probability and failure probability determination module is used to calculate the failure probability of the component based on the wind speed. The extreme weather grid load scenario generation module is used to generate extreme weather grid load scenarios, including line fault combinations and load demand fluctuations, based on user load forecast data and the component failure probability; the extreme weather includes typhoon weather. The user load loss determination module is used to construct an emergency dispatch model that considers user security load, and to solve the user load loss in the network load scenario under extreme weather conditions based on the emergency dispatch model. The user power outage probability determination module is used to count the number of scenarios in which a user is in a power outage state based on the user load loss, and to determine the user power outage probability.

[0008] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for calculating the probability of power outage for users under extreme weather conditions.

[0009] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for calculating the probability of power outages for users under extreme weather conditions.

[0010] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for calculating the probability of power outages for users under extreme weather conditions.

[0011] According to the specific embodiments provided in this application, this application has the following technical effects: This application calculates the component failure probability based on the wind speed at various locations in the wind farm, thereby generating a grid load scenario under extreme weather conditions that includes line fault combinations and load demand fluctuations. This can reasonably consider the impact of extreme weather on the flexible resources of the distribution network. Furthermore, it constructs an emergency dispatch model that considers user security loads to solve for user load losses within this grid load scenario, thereby calculating the user outage probability. This application considers user load allocation and supply guarantee needs, thus providing more accurate calculation results for user outage probability and reflecting the differences in outage risk among different types of users. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating a method for calculating the probability of power outages for users under extreme weather conditions, provided as an embodiment of this application; Figure 2 A schematic diagram of the geographical information of the power distribution network and the path of a typhoon provided in one embodiment of this application; Figure 3 This is a schematic diagram of user importance and peak load distribution provided in an embodiment of this application; Figure 4 This is a schematic diagram of the component failure probability distribution provided in an embodiment of this application; Figure 5 This is a schematic diagram of the probability distribution of user power outages provided in an embodiment of this application; Figure 6 This is a schematic diagram illustrating the probability distribution of power outages for users without considering the probability of photovoltaic module failure. Figure 7 This is a schematic diagram showing the distribution of power outage probability for users without considering the probability of photovoltaic module failure and security load constraints. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] like Figure 1 As shown in the figure, this application provides a method for calculating the probability of power outage for users under extreme weather conditions, including: S1: Determine the wind speed at each location in the wind field using the wind field model and geographic information data of the power distribution network; the geographic information data includes node coordinates, node connection relationships, line spans, and strength parameters of towers and lines.

[0017] S2: Calculate the component failure probability based on the wind speed; the component failure probability includes the failure probability of each line and the failure probability of the photovoltaic module.

[0018] S3: Based on user load forecast data and the component failure probability, generate a grid load scenario under extreme weather conditions, including line fault combinations and load demand fluctuations; the extreme weather includes typhoon weather.

[0019] S4: Construct an emergency dispatch model that considers user security load, and solve for user load loss in the network load scenario under extreme weather conditions based on the emergency dispatch model.

[0020] S5: Based on the user load loss, count the number of scenarios where the user is in a power outage state, and determine the user's power outage probability.

[0021] In practical applications, before S1, the following are also included: inputting geographic information data of the power distribution network, photovoltaic module parameters, solar intensity prediction data, user load prediction data, typhoon intensity and trajectory parameters; among which, geographic information data includes node coordinates, node connection relationships, line spans, and strength parameters of towers and lines; photovoltaic module parameters include the height of photovoltaic modules above the horizontal plane, strength parameters of photovoltaic modules, rated power of photovoltaic modules, and number of photovoltaic modules installed.

[0022] In an exemplary embodiment, S2 specifically includes: S21: Based on wind load theory, determine the failure probability of each line under different wind speeds.

[0023] In practical applications, the failure rate of overhead lines is represented by a pole series model.

[0024] S22: Utilize Calculate the failure probability of photovoltaic modules The failure probability includes the probability of the photovoltaic panel fastener coming loose. Failure probability at the connection of the slab support beam Probability of support column failure etc.; among them, , , , These are the strengths of the fasteners, the connection points of the plate support beams, and the support columns. , , The average strength values ​​are for the fasteners, the connection points of the plate support beams, and the support columns. , , The standard deviation of strength for fasteners, plate support beam connections, and support columns. , , The stress on the fixing components, the connection between the panel support beams, and the support columns is defined as follows: The photovoltaic module includes fixing components, the connection between the panel support beams, and the support columns. The formula for calculating the stress on the photovoltaic module is as follows: In the formula, For wind vibration coefficient, This is the coefficient of wind pressure height variation. This is the body size coefficient. air density, Wind speed at the photovoltaic installation site. For the solar panel's angle of attack, The wind speed is at 10m above the ground. The building height for installing photovoltaic modules, The center height of the photovoltaic module, This is the terrain roughness correction factor. The length of the lever arm of the fixed component. The angle between the wind direction and the direction the photovoltaic module faces the sun. For wind speed and panel facing correction factors, This refers to the number of pillars.

[0025] In an exemplary embodiment, S3 specifically includes: S31: Using the sequential Monte Carlo simulation method, the photovoltaic fault quantity time series data and line fault combinations are generated based on the component fault probability sampling.

[0026] S32: Generate a set of user load curves based on user load forecast data and load demand fluctuation range sampling.

[0027] S33: Select a set of photovoltaic fault quantity time series data, line fault combination and user load curve to combine into a grid load scenario under extreme weather.

[0028] In an exemplary embodiment, S4 specifically includes: S41: Construct an emergency dispatch model with the objective function of minimizing the total load loss resulting from the product of user weights and user load reduction; the objective function is a model that considers user security load; the objective function is... ;in, For user collection; For users determined based on user importance u The weights; For users u exist Load loss at any given moment; T This refers to the predicted period of extreme weather impact.

[0029] S42: Construct constraints, solve the objective function, and determine the user load loss in the grid load scenario under extreme weather conditions; the constraints include security load constraints and distribution network operation constraints.

[0030] The security load constraint is used to cut off power beyond the security load when the power supply exceeds the security load, and to completely disconnect the user when the power supply is below the security load. The security load constraint is... ;in, For user u, the original active power load during time period t. For users Security load, For users exist t Load loss during a given period; The distribution network operation constraints are: ;in, For the exchange of branch roads, For This is the set of terminal nodes of the AC branch with the first terminal node. For Let be the set of the starting nodes of the AC branch with the terminal node. Let j be the set of substations located at node j. Let j be the set of users located at node j. and These represent the active power on the line with node j as the starting node and node k as the ending node during time period t, and the active power on the line with node i as the starting node and node j as the ending node, respectively. and These represent the reactive power on lines with node j as the starting node and node k as the ending node during time period t, and on lines with node i as the starting node and node j as the ending node. For users in time period t Original reactive load, Let i be the line reactance with i as the first terminal node and j as the last terminal node. To inject active power into node j during time period t of the distributed photovoltaic system. and These represent the original active and reactive loads of node j during time period t. Let be the voltage magnitude of node j during time period t. and These represent the maximum and minimum active power of the substation at node j, respectively. Let be the active power injected into the substation at node j during time period t. and Let be the upper and lower limits of the voltage at node j, respectively. This refers to the safe current of a line with node i as the first node and node j as the last node. Let t be the line current with node i as the first node and node j as the last node during time period t. Let i be the line resistance with i as the first node and j as the last node. Let be the voltage at node j at time t. In practical applications, the distributed photovoltaic output is determined based on the number of normally operating photovoltaic modules. The specific calculation formula for distributed photovoltaic output is: in, for Total photovoltaic output at time node j; for The output of a single photovoltaic module at time node j; for At time node j, the first The operating status of the photovoltaic modules was obtained through fault probability sampling; The total number of photovoltaic elements at node j; for Number of samples extracted at any given time; Let be the probability of photovoltaic module failure at time t. The meteorological scale is much larger than the photovoltaic scale, and photovoltaic modules at the same node are considered to bear the same load, which is calculated based on the node coordinates.

[0031] In one exemplary embodiment, it further includes: utilizing Linearization of security load; among which, A constant greater than a set threshold. for The 0-1 variable represents the user u's security load exceeding the limit at any given moment.

[0032] In an exemplary embodiment, S5 specifically includes: S51: Utilization Determine the probability of power outage for users; among which, This represents the probability of power outage for user u under extreme weather conditions. This represents the total number of samples taken from load-bearing scenarios under extreme weather conditions. This represents the power outage state of user u in the sampled scenario m. , This represents the active power load of user u in the sampling scenario m. For the load loss of user u in sampling scenario m, when When = 0, the power outage state of user u in sampling scenario m is not a power outage. When =1, the power outage status of user u in the sampling scenario m is power outage.

[0033] Taking a 110kV distribution network 49-node system as an example, the method of this application is used to calculate the probability of power outage for users under typhoon conditions.

[0034] Assume the system has seven 120MVA substations built at nodes 2, 14, 18, 19, 23, 28, and 37, and two 100MW distributed photovoltaic (PV) systems built at nodes 12 and 16. The maximum load demand is 814MW. The geographical information of the distribution network and the typhoon's path are as follows: Figure 2 As shown. The user security load factor is 0.5 for top-tier users, 0.3 for first-tier users, 0.2 for second-tier users, and 0.1 for third-tier users. The user importance and peak load distribution are as follows. Figure 3 As shown. Typhoon path information uses data from Typhoon Yunna, with some data having a time granularity of 3-6 hours, which was extended to 1-hour data using linear interpolation. A total of 10,000 grid load scenarios under typhoon conditions were sampled and generated. Load demand fluctuations are taken as ±10%. Component failure probabilities are as follows. Figure 4 As shown.

[0035] Based on the user power outage probability calculation method proposed in this application, the user power outage probability distribution of the proposed method is as follows: Figure 5 As shown, the distribution of user power outage probability based on the method proposed in this application but without considering the probability of photovoltaic module failure is as follows: Figure 6 As shown, the distribution of user power outage probability in the method proposed in this application, but without considering the photovoltaic module failure probability and security load constraints, is as follows: Figure 7 As shown in Table 1, the calculation results of the three methods are as follows.

[0036] Table 1

[0037] From Table 1 and Figures 5-7 It can be seen that after considering photovoltaic module failures and security load constraints, the overall probability of power outages for users increases by about 2%-4%, and the number of power outages for important users increases. The impact of extreme weather on the power supply resources of the distribution network, the composition of user loads, and power supply protection strategies on the deviation of power outage probability calculation should be considered.

[0038] In summary, the power outage probability calculation for users under typhoon conditions in this application can reasonably take into account the impact of extreme weather on the flexible resources of the distribution network, as well as the load allocation and supply guarantee needs of users, and provide more accurate power outage probability calculation results.

[0039] This application provides a system for calculating the probability of power outages for users under extreme weather conditions, including: The wind speed determination module is used to determine the wind speed at various locations in the wind field using the wind field model and the geographic information data of the power distribution network; the geographic information data includes node coordinates, node connection relationships, line spans, and strength parameters of towers and lines.

[0040] The failure probability and failure probability determination module is used to calculate the failure probability of the component based on the wind speed.

[0041] The extreme weather grid load scenario generation module is used to generate extreme weather grid load scenarios, including line fault combinations and load demand fluctuations, based on user load forecast data and the component failure probability; the extreme weather includes typhoon weather.

[0042] The user load loss determination module is used to construct an emergency dispatch model that considers user security load, and to solve for user load loss in the network load scenario under extreme weather conditions based on the emergency dispatch model.

[0043] The user power outage probability determination module is used to count the number of scenarios in which a user is in a power outage state based on the user load loss, and to determine the user power outage probability.

[0044] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the above-described methods.

[0045] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0046] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0047] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0048] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0049] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0050] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0051] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for calculating the probability of power outages for users under extreme weather conditions, characterized in that, include: Using wind field models and geographic information data from the power distribution network, the wind speed at each location in the wind field is determined; The geographic information data includes node coordinates, node connection relationships, line span, and strength parameters of towers and lines; The probability of component failure is calculated based on the wind speed. Based on user load forecast data and the component failure probability, a grid load scenario under extreme weather conditions is generated, which includes line failure combinations and load demand fluctuations; the extreme weather conditions include typhoon weather. Construct an emergency dispatch model that considers user security load, and solve the user load loss in the network load scenario under extreme weather conditions based on the emergency dispatch model; Based on the user load loss, the number of scenarios in which users are in a power outage state is counted to determine the user power outage probability.

2. The method for calculating the probability of power outage for users under extreme weather conditions according to claim 1, characterized in that, The component failure probability includes the failure probability of each line and the failure probability of the photovoltaic module. The probability of component failure is calculated based on the wind speed, specifically including: Based on wind load theory, the failure probability of each line under different wind speeds is determined. use Calculate the failure probability of photovoltaic modules The failure probability includes the probability of photovoltaic panel fasteners coming loose. Failure probability at the connection of the slab support beam and the probability of support column failure ;in, ; , and These are the strengths of the fasteners, the connection points of the plate support beams, and the support columns, respectively. , and This represents the average strength of the fasteners, the connection between the plate support beam and the support column. , and The standard deviation of strength for fasteners, plate support beam connections, and support columns. , and This refers to the stress on the fasteners, the connection points of the panel support beams, and the support columns; wherein, the photovoltaic module includes fasteners, the connection points of the panel support beams, and the support columns, and the stress calculation formula for the photovoltaic module is as follows: In the formula, For wind vibration coefficient, This is the coefficient of wind pressure height variation. This is the body size coefficient. air density, Wind speed at the photovoltaic installation site. For the solar panel's angle of attack, The wind speed is at 10m above the ground. The building height for installing photovoltaic modules, The center height of the photovoltaic module, This is a terrain roughness correction factor. The length of the lever arm of the fixed component. The angle between the wind direction and the direction the photovoltaic module faces the sun. For wind speed and panel facing correction factors, This refers to the number of pillars.

3. The method for calculating the probability of power outage for users under extreme weather conditions according to claim 1, characterized in that, Based on user load forecast data and the component failure probability, extreme weather-related grid load scenarios are generated, including line fault combinations and load demand fluctuations. Specifically, these scenarios include: Using a sequential Monte Carlo simulation method, time-series data on the number of photovoltaic faults and combinations of line faults are generated based on the component fault probability sampling. A set of user load curves is generated based on user load forecast data and sampling of load demand fluctuation range. A set of time-series data on the number of photovoltaic faults, the combination of line faults, and the user load curve are selected and combined to form a grid load scenario under extreme weather conditions.

4. The method for calculating the probability of power outage for users under extreme weather conditions according to claim 1, characterized in that, Construct an emergency dispatch model that considers user security load, and based on the emergency dispatch model, solve for the user load loss in the network load scenario under extreme weather conditions, specifically including: A model is constructed with the objective function of minimizing the total load loss resulting from the product of user weight and user load reduction; this objective function is an emergency dispatch model that considers user security load; the objective function is... ,in, For user collection, The weight of user u is determined based on user importance. The load loss of user u during time period t. T For the predicted period of extreme weather impact; Construct constraints, solve the objective function, and determine the user load loss in the grid load scenario under extreme weather conditions; the constraints include security load constraints and distribution network operation constraints. The security load constraint is used to cut off power beyond the security load when the power supply exceeds the security load, and to completely disconnect the user when the power supply is below the security load. The security load constraint is... ;in, For user u, the original active power load during time period t. For users Security load, For users exist t Load loss during a given period; The distribution network operation constraints are: ;in, For the exchange of branch lines, For This is the set of terminal nodes of the AC branch with the first terminal node. For Let be the set of the starting nodes of the AC branch with the terminal node. Let j be the set of users located at node j. Let j be the set of substations located at node j. and These represent the active power on the line with node j as the starting node and node k as the ending node during time period t, and the active power on the line with node i as the starting node and node j as the ending node, respectively. and These represent the reactive power on lines with node j as the starting node and node k as the ending node during time period t, and on lines with node i as the starting node and node j as the ending node. For users in time period t Original reactive load, Let i be the line reactance with i as the first terminal node and j as the last terminal node. To inject active power into node j during time period t of the distributed photovoltaic system. and These represent the original active and reactive loads of node j during time period t. Let be the voltage magnitude of node j during time period t. and These are the maximum and minimum active power values ​​of the substation at node j, respectively. Let be the active power injected into the substation at node j during time period t. and Let be the upper and lower limits of the voltage at node j, respectively. Let i be the safe current of the line with i as the first terminal node and j as the last terminal node. Let i be the line current during time period t, with i as the starting node and j as the ending node. Let i be the line resistance with i as the first terminal node and j as the last terminal node. For node j in t The voltage at any given time; where the distributed photovoltaic output is determined based on the number of normally operating photovoltaic modules, and the specific calculation formula for the distributed photovoltaic output is: in, for Total photovoltaic output at time node j; for The output of a single photovoltaic module at time node j; for At time node j, the first The operating status of the photovoltaic modules was obtained through fault probability sampling; The total number of photovoltaic elements at node j; for At time node j, the first The number of photovoltaic modules extracted; Let t be the probability of photovoltaic module failure at time t; when the meteorological scale is much larger than the photovoltaic scale, the photovoltaic modules under the same node are considered to bear the same load, and the load is calculated based on the node coordinates.

5. The method for calculating the probability of power outage for users under extreme weather conditions according to claim 4, characterized in that, It also includes: utilizing Linearization of security load; among which, A constant greater than a set threshold. for The 0-1 variable represents the user u's security load exceeding the limit at any given moment.

6. The method for calculating the probability of power outage for users under extreme weather conditions according to claim 5, characterized in that, Based on the aforementioned user load loss, the number of scenarios in which users are in a power outage state is counted to determine the probability of a user's power outage, specifically including: use Determine the probability of power outage for users; among which, This represents the probability of power outage for user u under extreme weather conditions. This represents the power outage state of user u in the sampled scenario m. This represents the total number of samples taken from load-bearing scenarios under extreme weather conditions. , This represents the active power load of user u in the sampling scenario m. For the load loss of user u in sampling scenario m, when When = 0, the power outage state of user u in sampling scenario m is not a power outage. When =1, the power outage status of user u in the sampling scenario m is power outage.

7. A system for calculating the probability of power outages for users under extreme weather conditions, characterized in that, include: The wind speed determination module is used to determine the wind speed at various locations in the wind field using the wind field model and geographic information data of the power distribution network. The geographic information data includes node coordinates, node connection relationships, line span, and strength parameters of towers and lines; The failure probability and failure probability determination module is used to calculate the failure probability of the component based on the wind speed. The extreme weather grid load scenario generation module is used to generate extreme weather grid load scenarios, including line fault combinations and load demand fluctuations, based on user load forecast data and the component failure probability; the extreme weather includes typhoon weather. The user load loss determination module is used to construct an emergency dispatch model that considers user security load, and to solve the user load loss in the network load scenario under extreme weather conditions based on the emergency dispatch model. The user power outage probability determination module is used to count the number of scenarios in which a user is in a power outage state based on the user load loss, and to determine the user power outage probability.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for calculating the probability of power outage for users under extreme weather conditions as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for calculating the probability of power outage for users under extreme weather conditions as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for calculating the probability of power outage for users under extreme weather conditions as described in any one of claims 1-6.

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