Ice disaster distribution network restoration method and device based on catastrophic evolution and multi-source collaboration

Abstract

The application relates to an ice disaster power distribution network recovery method and device based on catastrophe evolution and multi-source cooperation, and particularly relates to the technical field of power systems.The method comprises the following steps: acquiring multi-source power grid data corresponding to a power distribution network in an ice disaster scene, wherein the multi-source power grid data comprises meteorological data, geographical data, traffic road network data, power grid operation data and power grid equipment spatial position data; performing catastrophe evolution simulation based on the multi-source power grid data, predicting the tower failure probability of a power transmission line in the power distribution network and the fragile section in the power distribution network; generating a power grid recovery strategy corresponding to the power distribution network based on the fragile section, the tower failure probability and the recovery constraints of the power distribution network, wherein the power grid recovery strategy comprises a repair task allocation plan, a driving path of a mobile energy storage and a charging and discharging plan of each time period, an operation sequence of a power distribution network switch and a dynamic island division plan, and a time period output plan of a distributed power supply. The application provides a targeted power grid recovery strategy and improves power grid recovery efficiency.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method and device for restoring ice-damaged distribution networks based on disaster evolution and multi-source collaboration. Background Technology

[0002] Ice storms are usually accompanied by severe weather such as heavy snowfall and freezing temperatures, which can easily lead to a chain of faults such as icing of power transmission lines, collapse of towers, and flashover of insulators, causing large-scale and prolonged power outages in the power distribution network, seriously affecting social stability and people's livelihood.

[0003] In related technologies, the emergency repair modes of power transmission lines in distribution networks are mostly based on static fault assumptions and single resource scheduling, which are difficult to cope with the dynamic propagation of faults, traffic disruptions and limited repair resources during ice storms, resulting in poor power grid restoration efficiency in extreme ice storm scenarios. Summary of the Invention

[0004] In view of this, this application provides a method and device for restoring a power distribution network in an ice storm based on disaster evolution and multi-source collaboration. The main purpose is to improve the technical problem that most of the transmission line repair modes of power distribution networks based on related technologies are based on static fault assumptions and single resource scheduling, which are difficult to cope with the dynamic propagation of faults, traffic obstruction and limited repair resources during the ice storm, resulting in poor power grid restoration efficiency in extreme ice storm scenarios.

[0005] Firstly, this application provides a method for restoring ice-damped power distribution networks based on disaster evolution and multi-source collaboration, the method comprising: Acquire multi-source power grid data corresponding to the distribution network in the ice storm scenario. The multi-source power grid data includes meteorological data, geographical data, transportation network data, power grid operation data, and spatial location data of power grid equipment. Disaster evolution simulation is performed based on multi-source power grid data to predict the probability of tower failures in transmission lines and vulnerable sections in the distribution network. Based on the vulnerable sections, tower failure probability, and distribution network recovery constraints, a corresponding power grid recovery strategy is generated for the distribution network. The power grid recovery strategy includes emergency repair task allocation plan, mobile energy storage travel path and charging and discharging plan for each time period, distribution network switch operation sequence and dynamic islanding plan, and distributed power generation time period output plan.

[0006] Secondly, this application provides a power distribution network recovery device based on disaster evolution and multi-source collaboration during ice storms. The device includes: The acquisition module is configured to acquire multi-source power grid data corresponding to the distribution network in the ice storm scenario. The multi-source power grid data includes meteorological data, geographical data, transportation network data, power grid operation data, and spatial location data of power grid equipment. The prediction module is configured to perform disaster evolution simulation based on multi-source power grid data, predicting the probability of tower failures of transmission lines in the distribution network and vulnerable sections in the distribution network. The generation module is configured to generate a power grid recovery strategy for the distribution network based on vulnerable sections, tower failure probabilities, and distribution network recovery constraints. The power grid recovery strategy includes an emergency repair task allocation plan, the travel path of mobile energy storage and the charging and discharging plan for each time period, the operation sequence of distribution network switches and the dynamic islanding plan, and the time period output plan of distributed power sources.

[0007] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of the first aspect.

[0008] Fourthly, this application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the method of the first aspect.

[0009] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method of the first aspect.

[0010] By employing the above technical solutions, this application provides a method and apparatus for ice storm power distribution network restoration based on disaster evolution and multi-source collaboration. Compared with related technologies, this application can acquire multi-source power grid data corresponding to the power distribution network in an ice storm scenario; perform disaster evolution simulation based on the multi-source power grid data to predict the probability of transmission line tower failures and vulnerable sections in the power distribution network; and generate a power grid restoration strategy corresponding to the power distribution network based on vulnerable sections, tower failure probabilities, and power distribution network restoration constraints. In this way, the dynamic disaster evolution characteristics of multi-network coupling such as meteorology, power distribution network, and transportation network under ice storm scenarios are considered, the spatiotemporal propagation path of fault chains is simulated, key vulnerable sections are identified, the prediction accuracy of transmission line tower failure probabilities is improved, and targeted power grid restoration strategies are provided to improve power grid restoration efficiency.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart illustrating the ice disaster power distribution network recovery method based on disaster evolution and multi-source collaboration provided in this application embodiment is shown. Figure 2 A flowchart illustrating an example provided in an embodiment of this application is shown; Figure 3 A flowchart illustrating another example provided in an embodiment of this application is shown; Figure 4 A schematic diagram of the structure of the ice disaster distribution network recovery device based on disaster evolution and multi-source collaboration provided in the embodiments of this application is shown. Detailed Implementation

[0015] To better understand the above-mentioned objectives, features, and advantages of this application, the solution of this application will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0016] Specifically, the power marketing field is characterized by numerous business lines, rapidly changing regulations, significant regional differences, and complex data sources. This results in high costs for manual maintenance of the knowledge base, easy expiration of content, and difficulty in continuous system iteration. General question-and-answer models cannot meet the needs of professional scenarios due to a lack of industry knowledge. As user interaction volume increases, the system also needs to have the ability to automatically correct errors, fill knowledge gaps, and optimize answer logic based on usage.

[0017] To address the shortcomings of existing retrieval systems that rely heavily on manual queries, experience-based responses, or keyword matching, which suffer from weak comprehension, insufficient business coverage, non-standardized responses, and low processing efficiency, making it difficult to accurately capture user intent and meet the needs of business personnel for structured process guidance, document lists, and consistent procedures, this embodiment provides a method for ice-damaged power distribution network recovery based on disaster evolution and multi-source collaboration. Figure 1 As shown, the method includes: Step 101: Obtain multi-source power grid data corresponding to the distribution network in the ice disaster scenario.

[0018] Multi-source power grid data may include meteorological data, geographical data, transportation network data, power grid operation data, and spatial location data of power grid equipment.

[0019] Specifically, multi-source power grid data can be various dimensions of data supporting the resilient recovery optimization of distribution networks during ice storms, reflecting the environmental information and current state of the distribution network. Meteorological data can include weather forecasts for the geographical area where the distribution network is located, such as temperature, precipitation intensity, snowfall, wind speed, wind direction, humidity, and duration of freezing conditions. Geographical data can include topographic spatial data for the geographical area where the distribution network is located, such as altitude, slope, aspect, terrain type, and land cover type (e.g., forest, farmland, bare ground). Transportation network data can include road grade, road segment length, traffic direction, road connectivity, time-varying traffic capacity under ice storm conditions, road congestion coefficient, and available routes and travel times for repair vehicles. Power grid operation data... It can provide real-time operating parameters and equipment parameters for various components in the distribution network, used for power flow calculation, fault diagnosis, and recovery optimization. These parameters may include node voltage amplitude, line current and load rate, distributed power output, energy storage system state of charge and charging / discharging power, switch status, network topology, load power, critical load level, equipment rated parameters and fault status information, etc. Spatial location data of power grid equipment may include the latitude and longitude coordinates, spatial distribution location, relative distance between equipment and topological connection location relationship of towers, lines, switches, distribution transformers, transformer substations, load nodes, and energy storage sites.

[0020] Step 102: Perform disaster evolution simulation based on multi-source power grid data to predict the probability of tower failures of transmission lines in the distribution network and vulnerable sections in the distribution network.

[0021] Vulnerable sections can be critical lines / tower sections that are prone to early failures, large-scale power outages, high repair difficulty, and significant impact on system recovery in ice disaster scenarios. These sections can be designated as the areas with the highest priority for emergency repairs, guiding priority repairs and improving grid recovery efficiency. Tower failure probability can be defined as the possibility of towers failing due to overload, instability, or toppling under combined ice and wind loads.

[0022] In some embodiments, multi-source power grid data can be input into a preset model. Based on multi-source data such as meteorology, geography, and power grid operation, the entire process of ice disaster from occurrence, development to spread can be dynamically simulated. The process of line icing, load growth, fault occurrence and chain propagation can be calculated time by time. The spatiotemporal dynamic damage of the disaster to the distribution network can be simulated, thereby predicting the failure probability of each tower, quantifying the risk of tower instability and failure, and providing a basis for power grid resilient recovery decisions.

[0023] Step 103: Based on the vulnerable sections, tower failure probabilities, and distribution network recovery constraints, generate the corresponding power grid recovery strategy for the distribution network.

[0024] In some embodiments, a preset collaborative optimization model can be constructed based on the recovery constraints of the distribution network. Then, the preset collaborative optimization model is solved according to the vulnerable sections and the failure probability of the towers to generate the power grid recovery strategy corresponding to the distribution network. The power grid recovery strategy includes an emergency repair task allocation plan, the travel path of mobile energy storage and the charging and discharging plan for each time period, the operation sequence of distribution network switches and the dynamic islanding plan, and the time-period output plan of distributed power sources.

[0025] Compared with related technologies, this embodiment can acquire multi-source power grid data corresponding to the distribution network in an ice storm scenario; perform disaster evolution simulation based on multi-source power grid data to predict the probability of transmission line tower failures and vulnerable sections in the distribution network; and generate power grid recovery strategies corresponding to the distribution network based on vulnerable sections, tower failure probabilities, and distribution network recovery constraints. In this way, the dynamic disaster evolution characteristics of multi-network coupling such as meteorology, distribution network, and transportation network in an ice storm scenario are considered, the spatiotemporal propagation path of the fault chain is simulated, key vulnerable sections are identified, the prediction accuracy of transmission line tower failure probabilities is improved, and targeted power grid recovery strategies are provided to improve the efficiency of power grid restoration.

[0026] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the implementation of this embodiment, step 102 may optionally include: dividing the transmission line into line segments; predicting the cumulative icing thickness corresponding to the line segment based on the distance between the line segment and the center of the ice disaster scenario, as well as the radial diffusion degree of the ice disaster impact and meteorological data; determining the vertical ice load and horizontal wind load corresponding to the line segment based on the cumulative icing thickness; predicting the tower failure probability of the transmission line based on the vertical ice load and horizontal wind load; and predicting the vulnerable sections of the distribution network based on the tower failure probability of the transmission line and the global vulnerability index of the line segment.

[0027] To accurately characterize the spatiotemporal dynamics of icing growth on transmission lines in the distribution network affected by ice storms, and to quantify the icing thickness at any given time and line location, a time-varying prediction model for line icing can be constructed, integrating meteorological data, geographical data, and spatial location data of power grid equipment. This model can be used to predict the cumulative icing thickness of transmission lines over time, enabling spatiotemporal dynamic fault evolution and multi-network coupled modeling during ice storms. Specifically, each transmission line can be discretized into several line segments, and for those located at geographical coordinates... line segment (The midpoint of the line segment can be taken as a representative point), its Euclidean distance from the center of the ice storm is... for: ; Since the rate of ice accumulation growth is mainly influenced by local meteorological conditions and is closely related to the distance from the center of the ice storm, a weighting function based on distance attenuation can be used to describe the impact of local meteorological conditions on the rate of ice accumulation growth. It can be represented as: ; In the formula: The spatial attenuation coefficient represents the radial diffusion degree of the impact of ice storms.

[0028] Optionally, the baseline rainfall rate and baseline wind speed can be spatially corrected using a weighting function to obtain the corrected line segment. Local meteorological conditions: ; ; In the formula: Base rainfall rate; Base wind speed; The ambient wind speed.

[0029] Furthermore, based on the revised local meteorological parameters, an improved method is adopted. Model calculation of line segment During the period ice cover growth : ; In the formula: The icing growth efficiency coefficient is related to the conductor material and surface condition. Ice density (kg / m³) 3 Wind speed; The density of water (kg / m³) 3 ); Liquid water content in the air (g / m³) 3 ), ; To calculate the time period length (h), we take 15 minutes.

[0030] For example, when the ice storm starts from the moment it begins... Time, Line Section Cumulative icing thickness It can be represented as: .

[0031] Correspondingly, icing not only increases the vertical load on transmission line conductors, but also, in conjunction with wind loads, seriously threatens the safety of tower structures. The conductors themselves may also break due to excessive icing. To further quantify the combined ice-wind load, the probability of tower failure is assessed based on calculations of icing thickness. This probability can include the probability of dynamic instability of the tower and the probability of conductor breakage.

[0032] For example, the vertical ice load per unit length of conductor caused by icing. It can be represented as: ; In the formula: The acceleration due to gravity (9.8 m / s²) 2 ); The diameter of the conductor (m); For example, if the wind load is proportional to the square of the wind speed and related to the equivalent projected area of ​​the conductor after icing, then the horizontal wind load on the conductor after icing is... It can be represented as: ; In the formula: This is the conductor resistance coefficient; air density (kg / m³) 3 ); The angle between the wind direction and the normal direction of the conductor axis (rad) is given when... This indicates that the wind direction is perpendicular to the guide wire; The total load consisting of the vector sum of ice load and wind load It can be represented as: ; The instability of the tower is uncertain, so a method based on... Representing the tower using a continuous probability model of the function exist Failure probability at any time This represents the ratio of the current total load to the tower's design bearing capacity. The failure probability can be expressed as: ; In the formula: The critical design failure load (N / m) for the tower; This is the proportional threshold, which can be set to 1, indicating that the probability of failure when the load reaches the design value is 0.5. This is the curve steepness coefficient; Correspondingly, transmission lines may consist of multiple towers, and the probability of tower instability leading to outages due to tower instability is... The failure probabilities of each tower along the line can be combined and synthesized based on a series reliability model: ; In the formula: For line segment The set of all towers.

[0033] Furthermore, the probability of conductor breakage faults in the line segment. The same approach can be adopted. Estimation in functional form: ; In the formula: The design ice thickness (mm) for the conductor; This is the curve steepness coefficient; The probability of conductor breakage due to tower instability in line sections. During the period Initial failure probability It can be represented as: .

[0034] Furthermore, during power restoration, the reconnection of repaired lines may cause changes in system power flow, posing a risk of cascading trips due to overload. Therefore, power flow verification can be performed on potential fault scenarios in the distribution network. For example, if the load rate of a certain line segment exceeds the preset overload protection threshold, that line segment can be identified as a faulty line to be repaired, and the repair time can be set to be the same as the initial fault.

[0035] Optionally, by setting a failure probability threshold (assuming...) By determining a fault in time (or by performing a finite number of time-series samplings), a set of ice disaster fault scenarios with spatiotemporal attributes can be generated. Each of the failure scenarios This may include a list of faulty lines in the scenario. And the estimated repair time for each faulty line.

[0036] Further, optionally, it can be based on the global vulnerability index corresponding to the faulty line in the fault scenario. By identifying critical vulnerable sections with the greatest impact on system recovery from fault scenarios, line sections with higher global vulnerability indices are more likely to fail earlier and cause more severe consequences during the disaster evolution process, and can be given higher repair priority in power grid recovery strategies. Global vulnerability index The calculation formula can be expressed as: ; In the formula: Indicates the fault scenario The probability of occurrence; For fault scenarios Central section The fault timing sequence number; the smaller the value, the earlier the fault occurred. For fault scenarios The total number of faulty road sections; Indicates the fault scenario Central section The amount of load loss caused by the fault; This represents the total system load. These are the time-series importance weight and the consequence severity weight, respectively, satisfying... .

[0037] Optionally, based on the road segment distance and the traffic dynamic congestion coefficient corresponding to the cumulative icing thickness, the travel time of the corresponding repair team for the road segment is determined; based on the state of charge of mobile energy storage power sources and fixed energy storage systems in the distribution network, the power supply mode of the distribution network is determined; and based on the power supply mode and travel time, the recovery constraints are determined.

[0038] Correspondingly, the speed of power grid recovery is limited by the dispatch efficiency of emergency repair vehicles and mobile energy storage resources in the context of ice storm traffic conditions.

[0039] To reflect the impact of ice storms on road network capacity, a time-varying road network model can be constructed based on traffic network data and power grid equipment spatial location data. This model reflects the interaction between disaster severity and traffic dispatch rate, achieving time-varying modeling of traffic network capacity. For example, based on traffic network data and power grid equipment spatial location data, the starting point of a line segment in a fault scenario can be determined. To the end point The travel distance is Under normal weather conditions, the average speed of repair vehicles is The basic passage time is In ice storm scenarios, due to icy roads, vehicle travel time increases, which can be defined as... The dynamic traffic congestion coefficient of the road segment at any given time is : ; In the formula: The average icing thickness of this road section area can be obtained by spatial interpolation based on the cumulative icing thickness of each road section. The parameters for model calibration can be obtained through regression of historical traffic data; Therefore, the travel time of the line segment under the ice storm scenario can be obtained. The time-varying input parameter, which can be used as a constraint for dynamic scheduling of emergency repair resources and for energy storage paths and charge / discharge constraints, can be expressed as: .

[0040] Furthermore, to optimize the synergistic performance of mobile energy storage systems (MESS) and stationary energy storage systems (FESS), enabling MESS to prioritize responding to dynamically generated, isolated load demands or emergency repair point power supply needs, FESS can provide regional power balancing and voltage support, and can also serve as an emergency power replenishment point for MESS. Based on grid operation data and grid equipment spatial location data, its spatiotemporal state and operational boundaries can be modeled to construct a spatiotemporal collaborative scheduling model for mobile and stationary energy storage, achieving uncertainty modeling of multi-source collaborative power restoration resources. For example, for the first... Taiwan MESS The state of a time period can be determined by its location, state of charge, and operating mode, introducing a binary variable. To characterize the location of the mobile energy storage power source: when the first Taiwan MESS Time intervals are connected to nodes in the transmission circuit. hour, Otherwise, it is 0. Nodes can be electrical junctions in the distribution network topology, used to connect transmission / distribution lines, towers, switches, transformers, distributed power sources, energy storage devices, or loads. Mobile energy storage power sources need to meet single-point connection constraints: ; In the formula: For the set of all nodes in the distribution network; State of charge of mobile energy storage power source The changes are influenced by its charging and discharging behavior and energy consumption during movement, and can be expressed as: ; Mobile energy storage power supplies also need to meet the following capacity and power constraints to operate: ; In the formula: For the first Taiwan MESS State of charge over a period of time; , The first Taiwan MESS Charging and discharging power (kW) during the time period; , The first The charging and discharging efficiency coefficient of the MESS unit; For the first Rated energy storage capacity (kWh) of one MESS unit; For the first Taiwan MESS during the time slot Internal factors include the energy consumed during movement; , These are the lower and upper limits of its operating state of charge, respectively; , These are the maximum permissible charging and discharging power, respectively.

[0041] For nodes configured in transmission circuits The evolution equation of SOC for FESS can be: ; In the formula: For nodes FESS in State of charge over a period of time; , They are nodes FESS in Charging and discharging power during a given period; , They are nodes The FESS charge and discharge efficiency coefficient; For nodes The rated energy storage capacity of the FESS.

[0042] Optionally, the lower limit coefficient of photovoltaic power output of the photovoltaic unit corresponding to the line segment is determined based on the cumulative icing thickness; the lower limit of photovoltaic power output of the photovoltaic unit is determined based on the lower limit coefficient of photovoltaic power output; and the lower limit of wind power output of the wind turbine unit corresponding to the line segment is determined based on the lower limit coefficient of wind power output of the wind turbine unit.

[0043] Correspondingly, extreme ice storms can significantly reduce the output capacity of photovoltaic and wind power, and the degree of attenuation is highly uncertain. Based on meteorological data, geographical data, grid operation data, and grid equipment spatial location data, output intervals can be predicted to characterize the possible output range, thus realizing interval-based modeling of new energy output. The actual output of new energy under extreme ice storms is highly uncertain. To ensure the robustness and safety of the recovery strategy, during the generation of the grid recovery strategy, the lower limit of the output in each output interval can be determined as its available output, which is used as the input parameter of the preset collaborative optimization model for the generation of the grid recovery strategy, ensuring the feasibility of grid recovery.

[0044] For example, for nodes The corresponding photovoltaic units, their Photovoltaic output range during the time period It can be represented as: ; In the formula: For nodes without disasters Photovoltaic units in Predicted maximum photovoltaic output for the specified time period; , They are respectively The lower limit coefficient and upper limit coefficient of photovoltaic output for different time periods According to the node The coefficient is derived from the cumulative icing thickness of the line segment; the thicker the icing, the lower the coefficient. The lower limit for photovoltaic output can be... .

[0045] For example, for nodes The wind turbine units, The wind power output range during a given time period can be represented as: ; In the formula: Node at rated wind speed Wind turbine units in Predicted maximum wind power output for the specified time period; , They are respectively The lower limit coefficient and upper limit coefficient of wind power output for different time periods. Among them, the lower limit of wind power output can be... .

[0046] Optionally, before step 103, the method of this embodiment may further include: determining recovery constraints, which include dynamic scheduling constraints for emergency repair resources, node power balance constraints, dynamic islanding and network topology constraints, system safe operation constraints, energy storage path and charging / discharging constraints, and distributed power output fluctuation constraints.

[0047] Among them, emergency repair resources can be emergency repair teams that carry out line repair tasks.

[0048] For example, the specific details of each constraint are as follows: (1) Constraints on dynamic scheduling of emergency repair resources: Task allocation constraint: Each faulty line can be repaired by at most one repair team, and each repair team can repair at most one faulty line at any given time, to ensure the reasonable allocation of line repair tasks. This constraint can be expressed as: ; ; In the formula: As a 0-1 variable, when the repair team Assigning and executing emergency line repair tasks The value is 1 if the time condition is met, and 0 otherwise; the emergency repair team is... The set of faulty lines is ; Repair sequence and traffic time constraints: For any repair team, assume its assigned task sequence is as follows Then the following condition is met: ; ; In the formula: For the repair team Start the emergency line repair mission The moment when emergency repairs began; For the emergency repair of the power line The estimated repair time; For the repair team from the location Move to location Passage time; For the repair team Completed the emergency repair task The predicted end time; This indicates the emergency repair team The first in the task sequence One faulty line. This constraint ensures that the movement and operation of the repair team in time and space are continuous and logical. (2) Node power balance constraints: For each node In each time period The injected power must be equal to the sum of the outflow power and the load power (minus the load shedding), and this constraint can be expressed as: ; In the formula: For nodes A collection of branch routes at the beginning; For nodes This is the set of branch routes at the end; branch road During the period The active power at the head end (by point to (positive); branch road During the period The square of the current amplitude; branch road The resistance; For nodes During the period Net output of distributed power sources; For nodes During the period The net discharge power (discharge is positive) of mobile or stationary energy storage; For nodes During the period The active power of the load; 0-1 variables represent nodes During the period Has power been restored? (3) Dynamic island partitioning and network topology constraints: Node affiliation and connectivity constraints: Each powered node must belong to an island (or a main grid power supply area), and nodes within the same island must be connected. Introducing 0-1 variables. Represents a node During the period Is it an isolated island? ( Indicates the area supplied by the main grid. (Indicates the island number), this constraint can be expressed as: ; In the formula: For the set of all possible island identifiers; Radial operation constraint: Each island (including the main grid power supply area) must operate radially. To avoid circulating current, a virtual power flow method is used to construct the constraint, which can be expressed as: ; ; In the formula: branch road During the period Virtual power flow; Represents a node During the period Virtual net injection, satisfying Time is the node If it is the main power supply point within the isolated island, then it is 0; Variables of 0-1 represent branches During the period Whether it is closed (1 for closed, 0 for open); It is a sufficiently large positive number; (4) System safety operation constraints: The system state during the recovery process must meet basic electrical safety requirements, which can be expressed as: ; In the formula: For nodes During the period The square of the voltage amplitude; , Represents a node The lower and upper limits of voltage amplitude; Optionally, step 103 may specifically include: constructing a preset collaborative optimization model based on vulnerable sections, tower failure probabilities, and distribution network recovery constraints; using a multi-objective gray wolf optimization algorithm to solve the constructed preset collaborative optimization model and generate a power grid recovery strategy corresponding to the distribution network.

[0049] To enable efficient and resilient recovery of the power distribution network under ice storms, a pre-defined collaborative optimization model is constructed based on disaster evolution modeling and resource characteristic characterization. This model coordinates emergency repair resource scheduling, mobile energy storage path planning, network topology reconfiguration, and energy output. The multi-objective gray wolf optimization algorithm is then used to solve the constructed model and generate a power grid recovery strategy.

[0050] Optionally, a pre-defined collaborative optimization model can be constructed using the multi-objective gray wolf optimization algorithm to generate a power grid restoration strategy for the distribution network. Specifically, this may include: using the multi-objective gray wolf optimization algorithm based on the minimization of recovery time rule and the maximization of active power rule to construct a pre-defined collaborative optimization model and generate a definite power grid restoration strategy; the minimization of recovery time rule is used to determine the minimum restoration time of the distribution network based on the passage time of the emergency repair team corresponding to the line segment and the repair time of the power outage nodes in the line segment; the maximization of active power rule is used to determine the power supply ratio of the critical loads based on the active power of the critical loads in the line segment.

[0051] Among them, the minimize recovery time rule can be used to minimize the total time from the occurrence of an ice storm to the last recoverable power-loss load node receiving power, thereby shortening the overall power outage time of the system.

[0052] For any node in the system with recoverable load Define its power supply path set For all possible power supply paths from the main network power point or the islanded master power point to this node, based on the current network topology... The determined actual path. Node The necessary condition for restoring continuous power supply is its power supply path. All faulty lines have been successfully repaired. Node The first time continuous power was restored It can be represented as: ; For any one of the line repair tasks The time when its repair is completed The time when emergency repairs begin can be determined by the start of the repair process. and estimated repair time Summation yields the result; if the node If it is already energized at the initial moment (i.e., there are no faulty lines on its power supply path), then it is defined as follows: .

[0053] Therefore, the total recovery time of the distribution network This can be the maximum value of the recovery time among all recoverable load nodes: ; In the formula: This refers to the total restoration time of the power distribution network. This refers to the set of all recoverable load nodes in the system. For nodes The moment of the first restoration of continuous power supply is determined by the emergency repair decision variables. Task allocation variables and network topology decision variables A joint decision.

[0054] Correspondingly, the active power maximization rule can maximize the proportion of actual power supply to critical loads throughout the recovery process: ; In the formula: The weighted average recovery rate of the system's critical loads; The set of recovery periods included in the repair process; A set of important load nodes; 0-1 variables represent nodes During the period Is the device in a power supply state (1: powered, 0: de-powered); Represents a node During the period The active power demand of important loads.

[0055] For example, such as Figure 2 As shown, the specific solution steps for the model are as follows: (1) Input data and algorithm parameter initialization: Input a set of ice storm fault scenarios And select representative failure scenarios from them. Get the list of faulty lines. Estimated repair time for each faulty line and key vulnerable area indicators Enter the corresponding travel time for the line segment. The algorithm includes the following parameters: lower limit of photovoltaic power output, lower limit of wind power output, state of charge of mobile energy storage power sources and stationary energy storage systems, and grid operation data, and sets the algorithm population size. Initial parameters; (2) Initialize the improved gray wolf population: Randomly generate according to the hybrid coding scheme. A gray wolf During the generation process, three pre-selected individuals in the population are... , , The wolf's role is initialized with a bias: Wolf's emergency repair sequence based on Value sorting generation; The distributed power output of the wolves is taken as the lower limit of the interval. All other individuals are generated completely randomly to ensure population diversity. (3) Constraint verification and infeasible solution repair: For each individual in the population The complete recovery scheme is decoded. The scheme is then checked sequentially to ensure it meets all recovery constraints: the allocation of emergency repair tasks and spatiotemporal continuity constraints are checked; power flow calculations are performed based on the current network topology and node injection power to check node power balance constraints and system security constraints. For schemes that do not meet the constraints, repair operators are activated: for example, for schemes that violate the emergency repair timing constraints in the dynamic scheduling constraints of emergency repair resources, the execution order of emergency repair tasks is adjusted; for schemes that violate the SOC constraints in the spatiotemporal coordinated scheduling constraints of mobile and fixed energy storage, the energy storage charging and discharging power is corrected. Schemes that remain infeasible after repair are assigned a very poor objective function value (e.g., ...). (), so that it can be naturally eliminated in the iteration; (4) Individual evaluation and non-dominated ranking: For each feasible individual, based on the decoded recovery plan, calculate two objective function values: total system recovery time. and critical load recovery rate .according to and The value is used to perform a fast non-dominated sort on the current population, dividing individuals into different Pareto front ranks (rank 1 being the optimal front). For individuals within the same front rank, their crowding distance is calculated according to the method in Section S4.1.3. ; (5) Selection of Leader Wolves and Population Renewal: From the first non-dominant frontier of the current population, select the three individuals with the largest crowding distance and use them as the new generation leaders. , , Wolves. Based on the position update formula of the standard gray wolf optimization algorithm, guided by the positions of the three leader wolves, the positions of all other wolves in the population are updated. The position vector of the wolf. For the discrete part The updated continuous values ​​are converted into valid discrete values ​​using a discretization mapping strategy. (6) Updating and archiving the optimal solution: Add the non-dominated solutions (i.e., the first frontier individuals) from the new generation of population to the external archive. Re-sort all solutions in the archive according to their non-dominated nature and remove solutions dominated by other solutions. If the archive size exceeds the preset limit, remove the solution with the smallest crowding distance to maintain distribution. (7) Convergence judgment (whether the algorithm termination condition is met): If the current iteration number reaches the preset maximum iteration number. Or the Pareto frontier in external archives in continuous If no significant changes occur in the next iteration (i.e. no new non-dominated solutions are added), the algorithm termination condition is met, and the process jumps to step 8); otherwise, the process returns to step 3) to continue iterating. (8) Output Pareto optimal recovery solution set: After the algorithm terminates, output all non-dominated solutions saved in the external archive. Each solution corresponds to a complete and feasible power grid recovery strategy, specifically including: task allocation and scheduling schemes for each repair team (such as task allocation status, task execution order, start / end time of each task); travel paths of each mobile energy storage and charging / discharging plans for each time period; operation sequence of distribution network switches and dynamic islanding scheme; output plans of each distributed power source for each time period; (9) Decision-making and scheme execution: Based on the actual situation at the disaster site (such as real-time road conditions and the availability of emergency repair resources) and the priority of the restoration objectives (such as prioritizing power supply to important loads or prioritizing the restoration of the main grid), select a final execution scheme from the Pareto optimal scheme set. Issue the dispatch instructions of the selected scheme to each emergency repair team, mobile energy storage device and distribution automation system for execution.

[0056] Specifically, to achieve efficient solution of the pre-defined collaborative optimization model and address multi-stage, multi-objective, and mixed-integer nonlinear programming problems, an improved pre-defined multi-objective Grey Wolf optimization algorithm can be designed. Based on core methods such as hybrid encoding, adaptive parameter adjustment, and multi-objective collaborative optimization, this algorithm effectively improves search efficiency and decision quality in complex solution spaces. The specific steps may include the following: (1) Hybrid coding scheme and initialization of the leader wolf role: To simultaneously handle discrete decision variables (such as emergency repair team task allocation and branch switch status) and continuous decision variables (such as energy storage charging and discharging power and distributed power output) in a pre-defined collaborative optimization model, a hybrid encoding scheme was designed. Each individual gray wolf in the population... Each position vector represents a complete recovery scheme, which consists of discrete parts. and continuous parts Composed of: ; Where: Discrete part Using integer and binary encoding, specifically including: each emergency repair team Fault repair sequence, status of all branch switches Island ownership identifiers for each node ; continuous part Real-number encoding is used, specifically including: the charging and discharging power of each mobile energy storage power source and stationary energy storage power source in each time period, and the dispatch output of each distributed power source in each time period. wait; To the leader wolf ( , , Role positioning and initialization: In the gray wolf optimization algorithm, , , The three leader wolves represent the individuals with the best, second-best, and third-best objective function values ​​in the current population, respectively. Role assignment is only used to guide the generation of the initial solution; after the algorithm iteration begins, each generation... , , The wolves will reselect from the current population based on non-dominated sorting and crowding distance mechanisms to ensure the objectivity and global convergence of the search process. To accelerate algorithm convergence and fully utilize prior knowledge of the problem, a biased initial solution for the three leader wolves is constructed during the population initialization phase: The wolf's initial solution is generated randomly and is responsible for exploring the globally optimal recovery scheme; In the initial solution for the wolf, its discrete part Tendency to generate indicators based on key vulnerable segments Guided emergency repair sequence, i.e., priority repair Lines with higher values ​​are used to facilitate the rapid restoration of power supply to the main grid; In the initial solution for the wolf, its continuous part We tend to use a conservative lower limit value for the output of each distributed power source to ensure the robustness of the initial recovery scheme; (2) Adaptive convergence factor and dynamic weight strategy: To balance the algorithm's global search and local exploitation capabilities and enhance population diversity, an adaptive convergence factor with nonlinear decreasing behavior is designed. and the dynamic leader wolf weight. The adaptive convergence factor can be expressed as: ; In the formula: This is the initial value (usually set to 2); This represents the current iteration number; This represents the maximum number of iterations. To avoid premature convergence caused by the fixed influence of the leader wolf, dynamic weights based on the Pareto front distribution density are introduced. The distance between its corresponding solution and the crowding degree Proportional ( ). It can be represented as: ; In the formula: Use a very small integer to avoid division by zero; (3) Pareto-dominated iterative leader wolf selection strategy: To address the dual-objective nature of the optimization model, a mechanism integrating non-dominated ranking and crowding distance is employed to handle multi-objective optimization. In each iteration of the algorithm, a new leader needs to be selected from all individuals in the current population based on the following non-dominated ranking and crowding distance mechanism. , , The character consists of three individuals. This selection process is entirely based on the current objective function value of each solution, independent of any bias construction during the initialization phase. In each generation of the population, the values ​​of each individual are calculated. The two objective function values ​​include the total system recovery time. and critical load recovery rate ; First, all individuals are ranked using a fast non-dominated ordering based on Pareto dominance, dividing them into several frontier levels. The first frontier contains all current non-dominated solutions. To differentiate the quality of individuals within the first frontier while preserving distribution, the crowding distance for each individual is calculated. For individuals at the forefront The congestion distance is calculated as follows: ; In the formula: Indicates the first One objective function value; , These are the maximum and minimum values ​​of the target on the first frontier, respectively; , For all first-frontier individuals according to the first After sorting the objective function values, and... Two adjacent individuals.

[0057] The selection of the leader wolf should be based on the crowding distance from the first non-dominant frontier. The three largest individuals were appointed as Wolf, wolves and Wolves, ensuring that the leading wolf is not only the current best, but also well distributed in the target space, and can effectively guide the population to explore the entire Pareto frontier.

[0058] As one possible implementation method, such as Figure 3As shown, firstly, a spatiotemporal dynamic fault evolution and multi-network coupling model for ice disasters is constructed, integrating meteorological, geographical, power grid, and transportation data. A line icing prediction model is used to predict the probability of tower failure under ice-wind loads, and a dynamic instability probability assessment of towers and conductors is performed, enabling fault scenario generation and identification of key vulnerable sections. Next, a fault chain propagation model and a transportation-power grid coupling analysis framework are established, constructing a set of collaborative optimization constraints. Using a multi-objective optimization model, gray wolf collaborative optimization is performed based on hybrid coding initialization, constrain repair and exploration are conducted, resulting in a Pareto optimization solution set. Feedback model optimization is then performed, and finally, the optimal recovery scheme is output. Furthermore, multi-source resource aggregation can be performed to achieve distributed power source modeling and energy storage collaborative scheduling, and islanded dynamic reconfiguration is carried out based on feedback collaborative decision-making.

[0059] In this way, this embodiment provides an optimization method for the resilient recovery of distribution networks during ice storms, considering both the disaster process and multi-source collaboration. First, a spatiotemporal dynamic fault evolution and multi-network coupling model for ice storms is constructed, integrating meteorological and geographical data to achieve time-varying prediction of line icing and assess the probability of tower failure under ice-wind loads. A fault chain propagation model and a transportation-power grid coupling analysis framework are established to accurately identify key vulnerable sections. Second, a dynamic aggregation and response model for multi-source collaborative power restoration resources is established, integrating the power complementarity and collaborative scheduling mechanism of mobile energy storage, fixed energy storage, and distributed power sources, and quantifying the accessibility of repair resources in ice storm traffic environments. Finally, a resilient recovery model is constructed with the objectives of minimizing the total system recovery time and maximizing the recovery rate of critical loads. The collaborative optimization model comprehensively considers constraints such as dynamic scheduling of emergency repair resources, energy storage paths and charging / discharging, distributed power output fluctuations, islanded topology reconfiguration, and safe operation. It constructs complex constraints involving multiple stages, multiple objectives, and mixed-integer nonlinearity to improve the model's solution accuracy. Finally, a collaborative optimization solution strategy based on an improved multi-objective gray wolf optimization algorithm is designed. Through hybrid coding, adaptive parameter adjustment, a co-evolutionary framework, and constraint repair mechanisms, it efficiently solves multi-stage, multi-objective, and mixed-integer nonlinear optimization problems, realizing the optimization of the entire process of distribution network fault evolution, resource coordination, and recovery decision-making under ice disaster conditions. This significantly improves the resilient recovery capability of the power grid in extreme ice disasters and effectively supports rapid, resilient, and collaborative recovery decisions of the distribution network under ice disaster conditions.

[0060] Furthermore, embodiments of this application provide a power distribution network recovery device based on disaster evolution and multi-source collaboration during ice storms, such as... Figure 4 As shown, the device includes: an acquisition module 31, a prediction module 32, and a generation module 33.

[0061] The acquisition module 31 is configured to acquire multi-source power grid data corresponding to the distribution network in the ice disaster scenario. The multi-source power grid data includes meteorological data, geographical data, transportation network data, power grid operation data, and spatial location data of power grid equipment. Prediction module 32 is configured to perform disaster evolution simulation based on multi-source power grid data, and predict the probability of tower failure of transmission lines and vulnerable sections in the distribution network. The generation module 33 is configured to generate a power grid recovery strategy for the distribution network based on the vulnerable section, the probability of tower failure and the recovery constraints of the distribution network. The power grid recovery strategy includes the emergency repair task allocation plan, the travel path of mobile energy storage and the charging and discharging plan for each time period, the operation sequence of distribution network switches and the dynamic islanding plan, and the time period output plan of distributed power sources.

[0062] In some embodiments, the prediction module 32 is specifically configured to divide the transmission line into line segments and predict the cumulative icing thickness corresponding to the line segment based on the distance between the line segment and the center of the ice disaster scenario, as well as the radial diffusion degree of the ice disaster impact and meteorological data. Based on the cumulative icing thickness of the corresponding line segment, determine the vertical ice load and horizontal wind load corresponding to the line segment; Based on vertical ice load and horizontal wind load, predict the probability of tower failure of transmission lines. Based on the failure probability of transmission line towers and the global vulnerability index of line segments, the vulnerable sections of the distribution network are predicted.

[0063] In some embodiments, the generation module 33 is specifically configured to construct a preset collaborative optimization model based on vulnerable sections, tower failure probabilities, and distribution network recovery constraints; A pre-defined collaborative optimization model is constructed using the multi-objective gray wolf optimization algorithm to generate the corresponding power grid recovery strategy for the distribution network.

[0064] In some embodiments, the generation module 33 is further configured to determine recovery constraints, which include dynamic scheduling constraints for emergency repair resources, energy storage path and charging / discharging constraints, distributed power output fluctuation constraints, islanded reconfiguration topology and safe operation constraints.

[0065] In some embodiments, the generation module 33 is specifically configured to use the multi-objective gray wolf optimization algorithm to solve and construct a preset collaborative optimization model based on the minimization recovery time rule and the maximization active power rule, and generate a determined power grid recovery strategy; The minimum recovery time rule is used to determine the minimum recovery time of the distribution network based on the passage time of the emergency repair team corresponding to the line segment and the repair time of the power outage node in the line segment; The active power maximization rule is used to determine the proportion of power supply required for critical loads based on their active power in a line segment.

[0066] In some embodiments, the acquisition module 31 is further configured to determine the travel time of the emergency repair team corresponding to the line segment based on the road segment distance and the traffic dynamic congestion coefficient corresponding to the cumulative icing thickness.

[0067] In some embodiments, the acquisition module 31 is further configured to determine the lower limit coefficient of photovoltaic power output of the photovoltaic unit corresponding to the line segment based on the cumulative icing thickness; determine the lower limit of photovoltaic power output of the photovoltaic unit corresponding to the photovoltaic unit based on the lower limit coefficient of photovoltaic power output; and determine the lower limit of wind power output of the wind turbine unit corresponding to the wind turbine unit based on the lower limit coefficient of wind power output of the wind turbine unit corresponding to the line segment.

[0068] It should be noted that other corresponding descriptions of the functional units involved in the ice disaster distribution network recovery device based on disaster evolution and multi-source collaboration provided in this application embodiment can be found in the following references. Figure 1 The corresponding description in [the document] will not be repeated here.

[0069] Based on the above, Figure 1 As illustrated in the example, correspondingly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described... Figure 1 The example method shown.

[0070] Based on the above, Figure 1 As illustrated, correspondingly, embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the above-described... Figure 1 The example method shown.

[0071] Based on this understanding, the technical solutions of the embodiments of this application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0072] Based on the above, Figure 1 The method shown, and Figure 4 To achieve the above objectives, the present application also provides an electronic device, comprising a storage medium and a processor; the storage medium for storing a computer program; and the processor for executing the computer program to implement the above-described virtual device embodiments. Figure 1 The method shown.

[0073] Optionally, the aforementioned electronic device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, an input unit, etc.

[0074] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0075] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented in hardware. This application acquires multi-source power grid data corresponding to the distribution network in an ice storm scenario; performs disaster evolution simulation based on the multi-source power grid data to predict the probability of transmission line tower failures and vulnerable sections in the distribution network; and generates a power grid recovery strategy corresponding to the distribution network based on vulnerable sections, tower failure probabilities, and distribution network recovery constraints. In this way, the dynamic disaster evolution characteristics of multi-network coupling such as meteorology, distribution network, and transportation network under ice storm scenarios are considered, the spatiotemporal propagation path of fault chains is simulated, key vulnerable sections are identified, the prediction accuracy of transmission line tower failure probabilities is improved, and targeted power grid recovery strategies are provided to improve power grid restoration efficiency. By employing hybrid coding, adaptive parameter adjustment, a co-evolutionary framework, and constraint repair mechanisms, this method efficiently solves multi-stage, multi-objective, mixed-integer nonlinear optimization problems, achieving full-process optimization of distribution network fault evolution, resource coordination, and recovery decision-making under ice disaster conditions. This significantly enhances the resilient recovery capability of the power grid under extreme ice disasters and effectively supports rapid, resilient, and coordinated recovery decision-making for the distribution network under ice disaster conditions.

[0077] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0078] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for restoring a power distribution network affected by ice storms based on disaster evolution and multi-source collaboration, characterized in that, include: Acquire multi-source power grid data corresponding to the distribution network in the ice storm scenario. The multi-source power grid data includes meteorological data, geographical data, transportation network data, power grid operation data, and spatial location data of power grid equipment. Based on the multi-source power grid data, a disaster evolution simulation is performed to predict the probability of tower failures of transmission lines in the distribution network and vulnerable sections in the distribution network. Based on the vulnerable section, the tower failure probability, and the distribution network recovery constraints, a power grid recovery strategy corresponding to the distribution network is generated. The power grid recovery strategy includes an emergency repair task allocation plan, the travel path of mobile energy storage and the charging and discharging plan for each time period, the operation sequence of distribution network switches and the dynamic islanding plan, and the time period output plan of distributed power sources.

2. The method according to claim 1, characterized in that, The disaster evolution simulation based on the multi-source power grid data, predicting the probability of transmission line tower failures and vulnerable sections in the distribution network, includes: The transmission line is divided into line segments. Based on the distance between the line segment and the center of the ice disaster scenario, the radial diffusion degree of the ice disaster impact, and the meteorological data, the cumulative icing thickness corresponding to the line segment is predicted. Based on the cumulative icing thickness of the line segment, determine the vertical ice load and horizontal wind load corresponding to the line segment. Based on the vertical ice load and horizontal wind load, predict the probability of tower failure of the transmission line. Based on the tower failure probability of the transmission line and the global vulnerability index of the line segment, the vulnerable sections of the distribution network are predicted.

3. The method according to claim 2, characterized in that, The generation of a power grid restoration strategy for the power distribution network based on the vulnerable section, the tower failure probability, and the power distribution network restoration constraints includes: Based on the vulnerable sections, the tower failure probability, and the recovery constraints of the distribution network, a pre-defined collaborative optimization model is constructed. The pre-defined collaborative optimization model is solved using the multi-objective gray wolf optimization algorithm to generate the power grid recovery strategy corresponding to the distribution network.

4. The method according to claim 3, characterized in that, Before generating the power grid recovery strategy corresponding to the distribution network based on the vulnerable section, the tower failure probability, and the distribution network recovery constraints, the method further includes: The recovery constraints are determined, including dynamic scheduling constraints for emergency repair resources, node power balance constraints, dynamic islanding and network topology constraints, and system safe operation constraints.

5. The method according to claim 3, characterized in that, The step of using the multi-objective gray wolf optimization algorithm to solve the pre-constructed collaborative optimization model and generating the power grid recovery strategy corresponding to the distribution network includes: The pre-defined collaborative optimization model is solved using the multi-objective gray wolf optimization algorithm based on the rules of minimizing recovery time and maximizing active power, thereby generating and determining the power grid recovery strategy. The minimum recovery time rule is used to determine the minimum recovery time of the distribution network based on the passage time of the emergency repair team corresponding to the line segment and the repair time of the power outage node in the line segment; The rule for maximizing active power is used to determine the proportion of power supply to the critical loads based on their active power in the line segment.

6. The method according to claim 2, characterized in that, The method further includes: Based on the road segment distance and the traffic dynamic congestion coefficient corresponding to the cumulative icing thickness, the travel time of the repair team corresponding to the road segment is determined.

7. The method according to claim 2, characterized in that, The method further includes: Based on the accumulated icing thickness, determine the lower limit coefficient of photovoltaic output of the photovoltaic unit corresponding to the line segment; The lower limit of photovoltaic output corresponding to the photovoltaic unit is determined according to the photovoltaic output lower limit coefficient. Based on the lower limit coefficient of wind power output of the wind turbine corresponding to the line segment, the lower limit of wind power output of the wind turbine is determined.

8. A power distribution network recovery device for ice storms based on disaster evolution and multi-source collaboration, characterized in that, include: The acquisition module is configured to acquire multi-source power grid data corresponding to the distribution network in the ice storm scenario. The multi-source power grid data includes meteorological data, geographical data, transportation network data, power grid operation data, and spatial location data of power grid equipment. The prediction module is configured to perform disaster evolution simulation based on the multi-source power grid data, and predict the probability of tower failure of transmission lines and vulnerable sections in the distribution network. The generation module is configured to generate a power grid recovery strategy corresponding to the power distribution network based on the vulnerable section, the tower failure probability and the recovery constraints of the power distribution network. The power grid recovery strategy includes an emergency repair task allocation plan, the travel path of mobile energy storage and the charging and discharging plan for each time period, the operation sequence of power distribution network switches and the dynamic islanding plan, and the time period output plan of distributed power sources.

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

10. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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